Sustainable Digital Transformation in Geotechnical-Related Engineering Disciplines: An Integrated Framework for Türkiye

Abstract

This study proposes the Sustainability-Aligned Digital Integration Model for Geotechnical-Related Engineering Disciplines in Türkiye (SDIM–Geo–TR) as a roadmap for sustainable digital transformation. Built on a four-stage methodology—global technology mapping, national contextualization, criteria definition, and phased integration—the model synthesizes emerging technologies such as GIS, BIM, UAV, IoT and Digital Twin into a maturity framework. It illustrates how digital adoption in Türkiye has evolved from early GIS use to more integrated multi-technology ecosystems but remains hampered by interoperability gaps, skill shortages and cost constraints. SDIM–Geo–TR organizes this evolution into four maturity stages and assesses progress using sustainability impact, technical feasibility, data compatibility, cost effectiveness and adoption level. The findings highlight that achieving fully integrated digital geotechnical practice requires coordinated policy interventions, standardization efforts and capacity building. By aligning international best practices with Türkiye-specific drivers, the model offers a practical roadmap for guiding sustainable and digitally enabled geotechnical engineering.

1. Introduction

The construction and infrastructure sectors are currently experiencing a paradigm shift driven by the accelerating pace of digital transformation. This shift has not only reshaped conventional design and project management processes but has also created new opportunities for enhancing sustainability, efficiency, and resilience in engineering practice [1]. Within this context, geotechnical engineering occupies a central position, as it inherently deals with uncertainty in subsurface conditions, complex soil–structure interactions, and the long-term performance of critical infrastructure systems. Traditional approaches relying solely on deterministic analyses and fragmented data management frameworks are increasingly insufficient to address contemporary challenges such as urban densification, climate-induced hazards, and resource scarcity [2]. Consequently, the systematic integration of digital technologies into geotechnical engineering emerges as a strategic necessity rather than an optional innovation.

A broad spectrum of digital tools are progressively being deployed in the field. Geographic Information Systems (GIS) provide robust capabilities for spatial data acquisition, management, and visualization, supporting seismic microzonation, landslide susceptibility mapping, and infrastructure planning [3]. Building Information Modeling (BIM) enables three-dimensional representation and multidisciplinary coordination, allowing seamless integration of structural, architectural, and geotechnical data throughout the lifecycle of projects [4]. Similarly, Unmanned Aerial Vehicles (UAVs) and remote sensing techniques have transformed site investigation practices, offering cost-effective and rapid solutions for topographic surveys, volumetric estimations, and change detection analyses [5]. Moreover, the deployment of Internet of Things (IoT) sensor networks facilitates real-time monitoring of ground deformation, pore water pressures, and structural responses, thereby advancing predictive maintenance and early warning systems. The convergence of these technologies sets the foundation for digital twin ecosystems, which virtually replicate geotechnical assets to enable continuous performance evaluation, risk forecasting, and decision support [6]. Existing international digital maturity models—such as those developed for infrastructure and construction management—tend to remain descriptive rather than prescriptive, outlining sequential stages of adoption but offering limited operational guidance for implementation. While these frameworks provide valuable conceptual structures, they rarely include contextual adaptation mechanisms, sustainability criteria, or quantifiable indicators that would enable their transferability to different engineering domains or national contexts. Moreover, most existing approaches assume uniform institutional capacity and economic conditions, which restricts their applicability in emerging-country settings where infrastructural, regulatory, and educational disparities persist [7]. Consequently, there remains a significant gap between theoretical models and their practical translation into sector-specific roadmaps.

The Turkish context presents a compelling case study for addressing these challenges. Türkiye is characterized by rapid urbanization, ambitious infrastructure development programs, and heightened exposure to seismic risks.

Recent earthquakes have underscored the urgent need for resilient infrastructure systems, while national policies increasingly emphasize digitalization and sustainability as dual imperatives for the construction sector [8]. In this context, several institutional and regulatory initiatives have been launched to accelerate digital transformation in the construction sector. The Ministry of Transport and Infrastructure has led Türkiye’s BIM implementation, introducing the “BIM Technical Specifications and Tender Documents” in 2021 (updated 2022), mandating BIM use in transport projects. Likewise, the Istanbul Metropolitan Municipality issued its “Rail Systems BIM Technical Specification” in 2024, making the Kabataş–Mecidiyeköy–Mahmutbey metro line one of the first BIM-based public contracts [9]. These institutional efforts align with the 11th Development Plan (2019–2023), which calls for nationwide BIM adoption, postgraduate education in digital construction, and a coordinated transition roadmap [10]. Although emerging practices illustrate promising steps, these implementations are often fragmented, project-specific, and lack strategic coordination. The absence of systematic roadmaps tailored to Türkiye’s geotechnical sector constitutes a critical barrier to achieving sustainable digital transformation.

To bridge this gap, the present study introduces the Sustainability-Aligned Digital Integration Model for Geotechnical-Related Engineering Disciplines in Türkiye (SDIM–Geo–TR), a context-sensitive framework that consolidates global and national insights into a four-stage maturity pathway. The model captures the sequential progression of digital adoption—from GIS-based spatial data standardization to BIM-enabled coordination, IoT/AI-driven monitoring, and fully integrated digital twin ecosystems—while explicitly reflecting Türkiye’s seismic vulnerability, urbanization pressures, and sustainability imperatives. Unlike conventional maturity models that remain largely descriptive, SDIM–Geo–TR incorporates a five-criteria evaluation scheme—sustainability impact, technical feasibility, data compatibility, cost-effectiveness, and adoption level—transforming it into a prescriptive roadmap that guides stakeholders toward resilient and sustainability-aligned digital transformation. The contributions of this study are threefold:

(i)

To develop the SDIM–Geo–TR model as a phased and context-specific framework structuring the digital transformation of geotechnical engineering in Türkiye;

(ii)

To establish a five-criteria evaluation framework that operationalizes the model and provides measurable justification;

(iii)

To propose a strategic policy roadmap for Türkiye’s geotechnical sector, including recommendations for education, open data governance, software standardization, and platform integration.

2. Methodology: Framework Design and Contextualization

The methodological approach adopted in this study is structured as a sequential yet iterative process designed to construct the SDIM–Geo–TR. Rather than relying on pre-existing digital maturity models, this framework was developed through a hybrid methodology that integrates a systematic synthesis of international literature, Türkiye-specific contextualization based on policy and infrastructure needs, and a set of five evaluation criteria grounded in sustainability principles and expert consensus. The resulting model aligns each stage of digital transformation—ranging from spatial data standardization to lifecycle integration—with actionable strategic recommendations tailored to Türkiye’s geotechnical-related engineering disciplines. The research process consisted of four interlinked stages, each building upon the insights generated in the preceding step, yet allowing iterative refinement based on newly revealed findings. This adaptive design ensured that the final model is not only rooted in state-of-the-art global knowledge but also responsive to Türkiye’s unique infrastructural challenges and policy environment. A graphical overview of the entire methodological flow is presented in Figure 1, which illustrates how these four stages collectively inform the development and operationalization of the SDIM–Geo–TR model, while the study selection process for global and national literature is shown in Figure 2. To guide this methodological design, the research sought to answer the following central question: “How can a structured and context-sensitive model be developed to guide the sustainable digital transformation of geotechnical-related engineering disciplines in Türkiye?”. In line with this guiding question, the key stages are summarized as follows:

Step 1—Global Technology Mapping: An extensive literature review was conducted using Scopus and Web of Science for international sources. The search employed combinations of the following keywords: “digital transformation,” “geotechnical engineering,” “GIS,” “BIM,” “UAV,” “IoT,” and “Digital Twin.” Boolean operators (“AND,” “OR”) were applied, and results were filtered to include only peer-reviewed journal articles published in English. Studies were retained if they explicitly addressed geotechnical engineering or geotechnical-related infrastructure applications. Exclusion criteria involved conference abstracts, review-only papers without implementation cases, and studies lacking relevance to engineering practice. The initial screening identified over 500 international studies, from which 86 met the inclusion criteria. Among these, 39 studies were selected for in-depth synthesis based on content richness, diversity of technological integration, and practical relevance.

Step 2—National Contextualization for Türkiye: Building on the global mapping, the second step involved a targeted national review structured around the same five technology domains. Using TR Dizin (Turkish National Citation Index) 40 peer-reviewed studies were included that explicitly applied these technologies in Türkiye’s geotechnical-related sectors. The outcomes of this review illustrate the sequential filtering from the broader global corpus to Türkiye-specific studies.

Step 3—Evaluation Criteria Definition: Based on a synthesis of international literature on digital transformation in infrastructure sectors, five evaluation criteria were initially identified: sustainability impact, technical feasibility, data compatibility, cost-effectiveness, and adoption level. These preliminary criteria were then evaluated by a panel of ten geotechnical experts from academia, public agencies, and private engineering firms in Türkiye. Each expert reviewed the criteria individually and assessed their applicability, clarity, and contextual relevance to Türkiye’s geotechnical engineering landscape. Feedback was collected through structured interviews and consolidated to confirm the suitability of the criteria. Consensus was established when over 80% of experts confirmed the relevance and sufficiency of the selected criteria, ensuring both theoretical validity and local applicability. This process ensured that the framework reflects both international best practices and national priorities.

Step 4—Integration into the SDIM–Geo–TR Roadmap: The final step involved the formulation of the SDIM–Geo–TR model as a phased integration roadmap, grounded in the findings from the previous three steps. Instead of applying a numerical ranking or optimization approach, the five selected evaluation criteria were used to define four progressive levels of digital maturity—ranging from spatial data standardization to fully integrated digital twin ecosystems. The resulting framework bridges global technological insights with Türkiye’s national geotechnical priorities and provides a conceptual basis for evaluating current gaps and planning future interventions.

3. Global Technology Mapping in Geotechnics

The first stage of the study involved a structured synthesis of international literature to map the digital technology landscape in geotechnical and related engineering fields. Through systematic screening and thematic coding, a representative body of global research was consolidated, capturing the most consistently applied digital tools across geotechnical contexts. This analysis revealed five dominant technological domains—GIS, BIM, UAVs, IoT, and Digital Twins—which together form the foundational pillars of digital transformation in the field. The following Section 3.1, Section 3.2, Section 3.3, Section 3.4 and Section 3.5 and the tables presented in these subsections intended as descriptive baselines that consolidate the most representative global applications within each technological domain. Rather than offering evaluative comparisons, they provide a structured foundation for the analytical synthesis developed in the subsequent national contextualization and evaluation sections.

3.1. Geographic Information Systems (GIS)

The integration of GIS into geotechnical engineering has been increasingly recognized since the mid-1990s. Brabb [11] employed GIS-based mapping in San Mateo County, California, to assess geohazards for land-use planning and risk mitigation, focusing on landslide susceptibility and liquefaction potential at a local scale. This study provided an early example of how GIS could support decision-making processes by enhancing spatial hazard representation. Building on these early efforts, Jibson et al. [12] conducted probabilistic seismic landslide hazard mapping in Los Angeles, California, using ARC/INFO GIS platform to integrate multiple datasets. Their approach enabled regional-scale probabilistic risk assessment and highlighted the capacity of GIS to support complex hazard modeling. In 2001, Sakellariou and Ferentinou [13] developed a custom GIS-based decision support tool in the Vouraikos Valley, Greece, for slope stability analysis and landslide hazard zonation. This work demonstrated the utility of GIS in generating site-specific risk maps and supporting local mitigation planning.

Subsequently, Kunapo et al. [14] advanced the field by developing a web-based, national-scale GIS for geotechnical data management in Singapore. The system integrated a relational database to enable online spatial queries, borelog generation, and geotechnical analyses, representing an important step toward interactive, web-enabled geotechnical information systems. van Westen et al. [15] further extended GIS capabilities for landslide mapping by integrating remote sensing, digital elevation models (DEMs), and global positioning system (GPS) data. Their work emphasized GIS’s role in regional to national-scale landslide inventory creation, hazard assessment, and risk evaluation, illustrating the growing importance of multi-source data integration.

In the 21st century, GIS applications in geotechnical engineering have evolved toward more sophisticated digital integrations, including BIM. Khan et al. [16] combined BIM and GIS to support three-dimensional geotechnical modeling and construction safety zoning in Peshawar, Pakistan. This approach facilitated city- and regional-scale collaboration and data sharing, demonstrating the value of integrated digital platforms in modern geotechnical projects. Similarly, Kadhim et al. [17] integrated GIS, GPS, and remote sensing in Basrah, Iraq, to map geotechnical properties, conduct spatial analyses, and visualize results, illustrating the growing emphasis on spatially explicit geotechnical data management. Leinauer et al. [18] demonstrated the use of GIS-supported real-time monitoring for detecting precursors of an imminent rock slope failure in the Bavarian Alps, while Vidal-Páez et al. [19] used GIS-based modeling to develop a landslide susceptibility map for the central Chilean Andes. Additionally, review studies by Singh [20] and Player [21,22] highlighted GIS’s role in data integration, visualization, hazard identification, and project planning, particularly in site investigation, soil management, hydrological analysis, and transportation geotechnical projects.

The diversity of global GIS applications is presented in

Table 1. GIS-based applications in geotechnical-related engineering disciplines.

Study	Application Area	Technologies Integrated	Key Outputs
Brabb [11]	Hazard mapping for regional planning	GIS	Seismic hazard zoning
Jibson et al. [12]	Seismic landslide hazard assessment	GIS + DEMs + Landslide inventories	Probabilistic risk maps
Sakellariou and Ferentinou [13]	Landslide hazard	GIS	Slope stability & Hazard assessment
Kunapo et al. [14]	Geotechnical online platform	GIS + Relational DB	Borelog generation & Online analysis
Westen et al. [15]	Landslide mapping & Risk assessment	GIS + DEM + LiDAR + Photogrammetry	Landslide susceptibility mapping
Khan et al. [16]	3D modeling & Safety zoning	BIM + GIS	Subsurface modeling & Zoning maps
Kadhim et al. [17]	Digital geotechnical mapping	GIS + GPS + Remote Sensing	Maps of bearing capacity & Shear strength
Leinauer et al. [18]	Rock slope monitoring & Early warning	GIS + Real-time monitoring	Monitoring & Early warning system
Vidal-Páez et al. [19]	Landslide susceptibility mapping	GIS + Fuzzy Logic	Landslide susceptibility mapping
Singh [20]	GIS-based data visualization	GIS	Visualization & Integration
Player [21,22]	Data communication & Visualization	GIS	Data integration & Stakeholder communication

, which categorizes selected case studies by their functional domains, integrated digital technologies, and geotechnical outputs.

3.2. Building Information Modeling (BIM)

BIM has emerged as a transformative technology in geotechnical engineering, particularly within large-scale infrastructure and underground projects. BIM facilitates multidisciplinary coordination by enabling the seamless integration of geotechnical data with structural and architectural models, thereby improving constructability assessments, clash detection, and data-driven decision-making in complex subsurface environments. Its object-oriented nature allows for rich data attribution, geometric precision, and real-time collaboration across engineering domains [23,24].

The application of BIM in geotechnical and underground infrastructure has evolved rapidly over the past decade, reflecting both advances in digital integration and increasing project complexity. The diversity of applications is summarized in

Table 2. BIM-based applications in geotechnical-related engineering disciplines.

Study	Application Area	Technologies Integrated	Key Outputs
Morin et al. [25]	Tunnel construction	BIM	Collaborative geotechnical workflows
Stelzer et al. [26]	Metro & Tunnel construction	BIM + Numerical analysis	Design optimization & Risk minimization
Stascheit et al. [27]	Tunnel construction	BIM +3D modeling + Web-based integration	Data management & Design
Alsahly et al. [28]	Tunnel & Metro infrastructure	BIM-to-FEM	Streamlined geotechnical & Structural analysis
Fabozzi et al. [29]	Tunnel construction	BIM-to-FEM & FEM-to-BIM	Construction scheduling & Time management
Klinc et al. [30]	Tunnel construction	BIM-to-FEM + Parametric modeling	Parametric modeling & FEM analysis
Huang et al. [31]	Metro infrastructure	Multi-level BIM + IFC interoperability	Numerical modeling & Heuristic workflows
Hung et al., [32]	Deep excavation & Metro	3D/4D BIM + Simulation	Clash detection & Project simulation
Erharter et al. [33]	Tunnel construction	BIM ground models + IFC/DAUB alignment	Structured geotechnical data management
Shi et al. [34]	Deep excavation	BIM-to-FEM + Python scripting	Automatic modeling & Data repository

, which categorizes representative case studies by functional scope, integrated digital tools, and key geotechnical outputs. For example, Morin et al. [25] applied BIM principles in the Silvertown Tunnel project in London, UK, emphasizing geotechnical data integration and collaborative workflows for large-scale tunnel construction. Stelzer et al. [26] demonstrated the utility of BIM processes in metro and tunnel construction projects in Stockholm, Sweden, emphasizing early design optimization and risk minimization. Their approach combined commercial three-dimensional BIM products with numerical analysis tools, highlighting the potential for improved workflow efficiency and hazard mitigation.

Subsequent studies expanded BIM’s role into more automated and integrated geotechnical applications. Stascheit et al. [27] focused on mechanized shield tunneling, implementing BIM for design, construction, and data management through web-based integration. This work illustrated the potential of BIM not only for visualization but also for centralized information management, supporting multi-disciplinary collaboration. Similarly, Alsahly et al. [28] applied BIM-to-FEM automation in tunnel construction and metro infrastructure projects in Düsseldorf, Germany, leveraging sub-models and data servers to streamline structural analyses and geotechnical design workflows.

Fabozzi et al. [29] further advanced BIM integration by implementing bi-directional BIM-to- Finite Element Method (FEM) and FEM-to-BIM workflows, augmented with four-dimensional (4D) BIM for construction time management. This methodology allowed temporal simulations and scheduling optimization, demonstrating how BIM can enhance both geotechnical design and project delivery. Klinc et al. [30] introduced a semi-automatic parametric BIM-to-FEM method for tunnel construction in Europe, integrating Autodesk Civil three-dimensional (3D)/Revit, Dynamo visual programming, and DIANA Finite Element Analysis. Their approach enabled parametric geotechnical modeling, bridging the gap between design data and structural analysis in a systematic, reproducible manner.

In urban deep excavation contexts, Huang et al. [31] applied multi-level-of-detail BIM with IFC interoperability to metro and underground infrastructure in Melbourne, Australia, facilitating numerical modeling and heuristic workflows for complex urban projects. Hung et al. [32] demonstrated 3D/4D BIM applications in Taipei, Taiwan, integrating construction simulation and clash detection for deep excavation and metro infrastructure projects. Erharter et al. [33] in Austria developed BIM ground models and sub-models tailored for geotechnical data, aligned with the Deutscher Ausschuss für unterirdisches Bauen (DAUB) and Industry Foundation Classes (IFC) standards, illustrating the increasing focus on standardization and structured data management in tunnel construction.

Similarly, Shi et al. [34] applied BIM-to-FEM integration to support automatic numerical modeling and geotechnical analysis, using Python scripting to enable BIM as a centralized data repository for finite element computations.

Collectively, these studies highlight a clear evolution in BIM applications for geotechnical engineering—from early three-dimensional visualization and workflow optimization to sophisticated, automated BIM-to-FEM integration, multi-dimensional simulation, and standardized geotechnical data management. This body of research underscores BIM’s transformative role in enhancing design accuracy, project safety, and inter-disciplinary collaboration across both urban and challenging subsurface environments.

3.3. Unmanned Aerial Vehicles (UAVs)

The rapid advancement of UAV technologies has significantly transformed data acquisition and monitoring practices in geotechnical engineering. UAV platforms, equipped with diverse sensors such as RGB cameras, LiDAR, multispectral, and thermal infrared instruments, offer unprecedented capabilities for high-resolution, cost-effective, and timely collection of geospatial and topographic data [35]. These technologies enable precise characterization of complex terrain, real-time monitoring of slopes, cliffs, and infrastructure, and enhanced risk assessment for natural hazards such as landslides, coastal erosion, and rockfall events. By integrating UAV-derived datasets with computational modeling, photogrammetric workflows, and, in some cases, digital twins, geotechnical practitioners can improve decision-making processes, optimize resource allocation, and implement early-warning systems with higher reliability [36]. The growing body of literature demonstrates both the methodological evolution and the expanding scope of UAV applications across environmental, civil, and infrastructure-focused geotechnical domains.

Building on this foundation, early methodological investigations emphasized the feasibility of low-cost UAV platforms for high-resolution topographic mapping in challenging terrain. For instance, Hackney and Clayton [37] conducted an early methodological review of UAV technologies for generating high-resolution topographic data, with a case study application in pro-glacial terrains in Iceland. The study highlighted the potential of low-cost, rapidly deployable UAV platforms equipped with RGB cameras and Structure-from-Motion (SfM) photogrammetry for geomorphic mapping. Their findings demonstrated that UAVs could provide high spatial and temporal resolution topographic data, offering a flexible and cost-effective alternative to conventional survey methods in challenging environments. Caprioli et al. [38] focused on the use of UAV-based photogrammetry for landslide mapping and monitoring in coastal cliff areas. Their work provided high-resolution fracture characterization and facilitated quantitative risk assessments, thereby confirming the practical applicability of UAV platforms in hazard management and geotechnical monitoring of coastal environment

Subsequent research focused on evaluating UAV performance in terms of measurement accuracy, operational reliability, and sensor integration. Casagli et al. [39] performed a comprehensive technological and methodological review of remote sensing techniques for landslide mapping, monitoring, and risk assessment, emphasizing UAV-enabled photogrammetry. The authors compared multicopter drones using digital photogrammetry (DP) and Ground-Based Interferometric Synthetic Aperture Radar (GB-InSAR), demonstrating millimeter-level accuracy. This study underscored the utility of UAVs in providing rapid, high-accuracy datasets for geohazard assessment and decision-making. Shaw et al. [40] conducted a comparative study of UAV technologies for beach monitoring, evaluating both photogrammetry and LiDAR sensors across DJI platforms. This work highlighted UAV versatility in coastal monitoring and comparative performance evaluation of different sensor technologies. Lin et al. [41] investigated UAV-LiDAR integration for coastal erosion monitoring. The research provided robust evidence for UAV-LiDAR applications in precision geomorphology and environmental risk assessment.

In parallel, UAV applications began intersecting with construction and infrastructure domains, highlighting their operational versatility. Small et al. [42] evaluated drone-based photogrammetry for on-site construction quantity surveys. Utilizing quadcopter UAVs with RGB cameras, the study integrated photogrammetric outputs with BIM, enabling accurate volume and area measurements with quantifiable percent errors. This application showcased UAVs’ potential in the construction sector, enhancing digital workflows and real-time project evaluation. de Sousa Mello et al. [43] assessed the performance of UAV photogrammetry for landfill monitoring. This research underscored the role of UAV-based monitoring in operational efficiency and volumetric analysis within engineering applications. Gaspari et al. [44] explored UAV-based bridge inspection methodologies, combining RGB SfM photogrammetry with LiDAR to generate high-resolution inspection data for infrastructure monitoring. The study emphasized the integration of UAV data into digital twin models to support predictive maintenance and inspection reliability.

Most recently, UAV methodologies have evolved toward multi-sensor, high-precision platforms capable of comprehensive geotechnical monitoring and emergency response applications. Sun et al. [45] provided a methodological review of UAV technologies applied in landslide studies, integrating photogrammetric, multispectral, thermal infrared, LiDAR, and Synthetic Aperture Radar (SAR) sensors. The research demonstrated centimeter-level DEM accuracy, effective crack and landslide detection, and reliable volume estimation, highlighting the progression toward multi-sensor UAV platforms for high-precision geotechnical monitoring, emergency response, and hazard assessment. Despite rapid methodological progress, UAV adoption in practice remains limited. As Noroño [46] highlights, beyond technical challenges such as cost and interoperability, perceptual and organizational factors—particularly institutional culture and economic context—also constrain widespread use.

Collectively, these studies illustrate a clear trajectory in UAV research: from proof-of-concept and single-sensor surveys to integrated multi-sensor platforms that support advanced geotechnical assessment, risk management, and infrastructure monitoring. Representative studies are summarized in

Table 3. UAV-based applications in geotechnical-related engineering disciplines.

Study	Application Area	Technologies Integrated	Key Outputs
Hackney and Clayton [37]	Pro-glacial topography mapping	UAV + RGB + SfM	High-resolution topographic maps
Caprioli et al. [38]	Coastal landslide monitoring	Hexacopter UAV + RGB + SfM + GIS	Landslide hazard mapping
Casagli et al. [39]	Landslide mapping & Risk management	UAV-DP + TLS GB-InSAR + Infrared	Landslide monitoring & Risk assessment
Shaw et al. [40]	Beach monitoring	DJI UAVs + SfM LiDAR	Shoreline & Change assessment
Lin et al. [41]	Coastal erosion monitoring	RGM + LiDAR + DJI M600	Coastal erosion monitoring
Small et al. [42]	Construction site surveying	UAV + RGB + BIM	Construction site volume & Area estimation
Mello et al. [43]	Landfill monitoring	UAV + GCPs	Landfill volume & Surface monitoring
Gaspari et al. [44]	Bridge/infrastructure monitoring	UAV-LiDAR + SfM + TLS	Bridge/infrastructure inspection
Sun et al. [45]	Landslide mapping & Monitoring	UAV + RGB + LiDAR + SAR + GNSS + ML	Landslide hazard mapping & Monitoring

.

3.4. Internet of Things (IoT)

The proliferation of IoT sensor networks has fundamentally reshaped real-time geotechnical monitoring practices by enabling continuous, distributed, and remotely accessible data acquisition. These systems combine various multi-parameter sensors—including MEMS tilt meters, volumetric water content probes, GNSS units, piezometers, and rainfall gauges—within wireless communication frameworks such as LoRa, GPRS, 4G/5G, and BeiDou-based mesh networks. Power autonomy is ensured through solar modules or long-duration batteries, allowing for uninterrupted operation in remote, hazardous, or underground environments [46,47]. Data are transmitted to cloud-based platforms for real-time visualization, statistical analysis, and predictive analytics, including early-warning and risk management systems. These advances enhance infrastructure resilience, safety, and sustainability while reducing manual intervention and operational costs [48].

Table 4. IoT-based applications in geotechnical-related engineering disciplines.

Study	Application Area	Technologies Integrated	Key Outputs
Segalini et al. [49]	Rockfall & Structural tilt	Mechanical triggers + Web visualization	Rockfall barrier monitoring & Building tilt alerts
Abraham et al. [50]	Landslide	MEMS tilt + VWC sensors + Wireless battLiry modules	Slope stability tracking & Early warnings
Li et al. [51]	Landslide monitoring	GNSS + 5G + WSN Groundwater sensors	Multi-level LEWS & Displacement detection
Gamperl et al. [52]	Informal settlements	MEMS tilt + LoRa + Groundwater sensors	Low-cost open-source IoT & LEWS for urban zones
Sreevidya et al. [53]	ML-based early warnings	Geophysical sensors + ML integration	Slope failure prediction & Accuracy
Oguz et al. [54]	Water-induced landslides	VWC sensors + 4G+ Matric suction + piezometers	Real-time pore pressure data & Alert calibration
Liu et al. [55]	Embankment slopes	Portable sensors + Mobile routers	All warnings before landslide & Rainfall-triggered detection

summarizes the selected case studies. They highlight both the technological maturation and methodological diversification of IoT applications, providing a foundation for the implementation of real-time, high-resolution geotechnical monitoring systems in diverse environmental and infrastructural contexts.

IoT technologies into geotechnical monitoring has advanced considerably over the past few years, offering innovative solutions for early warning systems, infrastructure assessment, and environmental hazard management. Segalini et al. [49] presented an exploration of IoT applications for geotechnical monitoring, focusing on rockfall barriers and building tilt detection. Their approach utilized tilt-based sensors coupled with mechanical triggers and web-based visualization to monitor impact events and structural tilt, demonstrating the potential for remote monitoring of geotechnical hazards even in the absence of fully detailed deployment contexts. Building on these insights, Abraham et al. [50] developed a Micro-Electro-Mechanical Systems (MEMS)-based IoT landslide early warning system in the Chibo region of the Darjeeling Himalayas, India. This system employed MEMS tilt sensors and volumetric water content (VWC) sensors integrated into wireless battery-powered modules to monitor ground deformation and soil moisture on monsoon-affected, unstable slopes. The study highlighted the capability of IoT devices to provide continuous, real-time monitoring of critical geotechnical parameters in challenging mountainous environments. Li et al. [51] extended IoT-based monitoring to a fifth-generation (5G) wireless framework for landslide early warning in Lianhe terraces, Youxi County, Fujian, China. This study incorporated GNSS, rainfall, crack, and groundwater sensors within a mesh or linear wireless sensor network, powered by solar energy and leveraging BeiDou/GPRS communication. The system successfully measured three-dimensional surface displacements, rainfall events, ground cracks, and groundwater levels, emphasizing the potential of high-speed, next-generation IoT networks for real-time hazard prediction in hilly, monsoon-prone landscapes.

Parallel efforts in Colombia by Gamperl et al. [52] demonstrated the applicability of open-source, cost-effective IoT landslide early warning systems in informal settlements in Medellin. By integrating MEMS tilt sensors and subsurface groundwater monitoring via LoRa wireless communication and battery/solar power, the authors were able to monitor ground deformation and groundwater dynamics, highlighting the adaptability of IoT solutions to socially and geographically complex urban settings. Sreevidya et al. [53] have also explored the integration of machine learning with IoT frameworks, which proposed a machine learning-based IoT landslide early warning system capable of generalizable, global application. Utilizing geophysical sensors for soil moisture, shear strength, rainfall, and slope monitoring, this approach illustrated the potential of combining advanced data analytics with IoT sensing to enhance predictive capabilities for geotechnical hazard assessment.

Complementing these field implementations, Oguz et al. [54] focused on IoT-based hydrological monitoring for landslide-prone areas in central Norway. Using volumetric water content sensors, piezometers, and suction monitoring within a 4G-powered network, the study captured critical soil parameters under cold, snow, and rainfall conditions, underscoring the relevance of IoT for hydrological and geotechnical monitoring in diverse climatic zones. Liu et al. [55] developed a solar-powered, low-cost, portable IoT landslide early warning system in Fukuoka, Japan. The system, though lacking detailed parameter specification, illustrated the feasibility of lightweight, mobile, and energy-autonomous IoT devices for monitoring embankment slopes in geologically adaptive contexts. Collectively, these studies illustrate the rapid progression of IoT-enabled geotechnical monitoring—from early sensor deployments in controlled case studies to sophisticated, multi-parameter networks integrated with wireless communications and predictive analytics.

3.5. Digital Twin

At the frontier of digital transformation, digital twin systems represent a disruptive evolution in geotechnical engineering, enabling continuous synchronization between physical infrastructure and virtual models. A digital twin is a dynamic, high-fidelity digital replica of a physical asset—such as a tunnel, dam, slope, or deep excavation—that integrates BIM, IoT sensor data, remote sensing, and AI-powered simulations [56,57]. Unlike static 3D models or one-way digital shadows, advanced digital twins support bi-directional data exchange, enabling real-time performance monitoring, risk prediction, and adaptive control of geotechnical systems [58].

Table 5. Digital twin applications in geotechnical-related engineering disciplines.

Study	Application Area	Technologies Integrated	Key Outputs
Hodgkinson and Elmouttie [59]	Mining slopes & Geohazards	GPR + LIBS + MOOSE + ML	Digital mining & Slope monitoring
Elmo and Stead [60]	Rock slopes & Massifs	IoT + Smart sensors + Numerical simulation	Framework & Slope monitoring
Hu et al. [61]	Underground infrastructure	CAD + IoT + 5G + Image recognition	Review of enablers & System interoperability
Haryono et al. [62]	Deep excavation	Digital Shadow (BIM only)	Static data flow & Digital coordination
Cheng et al. [63]	Tunnel & Slope design	BIM + Handheld LiDAR + UAV + Autodesk tools	Real-time predictive & Sustainable design
Firoozi and Firoozi [64]	Tunnels & Bridges	IoT sensors + ML algorithms	Maintenance & Resilience assessment
Salzgeber et al. [65]	Tunnel construction	Autodesk Services + IoT sensors	Real-time communication & Data integration
Rivadeneira-Moreira [66]	Dams & Tunnels	Bayesian inference + ML + Stochastic models + LiDAR	Predictive monitoring & Real-time risk assessment
Tan et al. [67]	General Geotechnical	3D models + IoT–AI + Lifecycle platforms +	Review of status & Future directions

illustrate a clear trajectory in geotechnical digital twin research.

The evolution of digital twin applications in geotechnical engineering has been marked by a progressive integration of real-time data acquisition, predictive analytics, and advanced computational platforms. Hodgkinson and Elmouttie [59] provided an early review of digital twin implementation in mining, emphasizing geological modeling and slope stability applications. The study highlighted the use of multiphysics simulation environments (MOOSE), Ground Penetrating Radar (GPR), Laser-Induced Breakdown Spectroscopy (LIBS), and machine learning algorithms such as self-organizing maps, demonstrating the potential of bi-directional, real-time interaction between physical and digital models in the mining context. Similarly, Elmo and Stead [60] critically evaluated digital twins in rock engineering, highlighting conceptual models and the use of IoT-enabled smart sensors and numerical simulations for slope stability. These foundational studies underscore the initial emphasis on static or semi-dynamic representations, establishing the technological groundwork for subsequent real-time and predictive systems.

Building upon these conceptual foundations, researchers increasingly explored the integration of IoT, CAD platforms, and big data analytics to enhance real-time monitoring and predictive capabilities. Hu et al. [61] synthesized enabling technologies for digital twins in tunnel and underground engineering, demonstrating the role of CAD tools (UG, AutoCAD, SolidWorks, Creo), IoT sensing, image recognition, reverse engineering, and large-scale data processing frameworks in supporting bi-directional data interactions. This progression marked a shift toward more sophisticated, data-driven digital twin architectures capable of linking field observations with computational models for enhanced geotechnical assessment. Concurrently, Haryono et al. [62] provided an overview of digital twin adoption in deep excavation and challenging soil conditions, characterizing digital shadows with one-way data synchronization and BIM integration, underscoring the early-stage adoption and potential for future bi-directional systems.

The transition from enabling technologies to full-scale digital twin applications is exemplified by Cheng et al. [63], who integrated BIM with digital twins for sustainable geotechnical design in tunnel and slope stability projects. Their approach leveraged UAV photogrammetry, handheld LiDAR, generative design, and machine learning within Autodesk Civil 3D, Revit, and Dynamo environments, creating a fully functional digital twin with real-time, predictive analytics. Firoozi and Firoozi [64] further advanced this paradigm by combining IoT sensors with AI and machine learning to develop smart geotechnical systems for bridge and tunnel infrastructure resilience. These studies illustrate the convergence of sensor networks, computational intelligence, and digital modeling in enabling proactive infrastructure management.

Concurrently, the integration of digital twins with risk management and probabilistic modeling has gained attention. Salzgeber et al. [65] reviewed tunnel construction projects, emphasizing real-time data integration, cloud-based platforms, and IoT-enabled monitoring for operational efficiency and hazard mitigation. Rivadeneira-Moreira [66] synthesized probabilistic digital twin frameworks for dams, slopes, and tunnels, incorporating Bayesian inference, stochastic modeling, piezometers, inclinometers, LiDAR, and advanced machine learning algorithms. Complementing this perspective, Tan et al. [67] reviewed the status and future directions of digital twin applications in China’s geotechnical engineering, highlighting the use of 3D modeling, IoT, artificial intelligence, and unified lifecycle management platforms. While this study primarily described digital models as static representations, it underscored the emerging trend of combining IoT and AI with digital modeling to move toward more interactive and predictive systems. It is also essential that digital twin models in geotechnical engineering capture not only topological configurations but also lithological variability—continuously updating subsurface parameters such as stratigraphy, stiffness, and strength through sensor feedback and numerical calibration—to ensure realistic representation of ground behavior over time.

Collectively, the integration of GIS, BIM, UAVs, and IoT forms the technological backbone of sustainable digital transformation in geotechnical engineering [68]. When used in combination, these tools enable real-time data acquisition, spatial analysis, and decision support for ground improvement and infrastructure safety management. For example, UAVs can provide continuous surface mapping, while IoT-based sensors monitor subsurface responses during ground treatment; both datasets can be automatically linked through GIS-BIM interoperability to update digital twin environments in real time. This integrated ecosystem not only enhances monitoring efficiency and predictive capability but also supports the sustainable management of construction materials and energy use across the project lifecycle.

4. National Contextualization for Türkiye

This section builds directly upon the global technology mapping presented in Section 3, translating the identified international trends into the national context of Türkiye. While the international literature highlighted five core technological domains—GIS, BIM, UAVs, IoT, and Digital Twins—the extent and manner of their adoption in Türkiye remain fragmented and uneven. To capture this national picture, a targeted review of 40 peer-reviewed studies from TR Dizin was undertaken, covering applications. This evidence base, when combined with contextual drivers such as seismic risk, rapid urbanization, mega-project delivery, and national sustainability mandates, provides the foundation for assessing Türkiye’s current trajectory. Section 4 therefore examines (i) the key contextual drivers shaping digital adoption, (ii) the evolution and current state of technology uptake, (iii) barriers and opportunities for implementation, and (iv) the rationale for a structured, sustainability-aligned transformation framework tailored to Türkiye’s geotechnical sector.

4.1. Contextual Drivers

Türkiye’s geotechnical sector operates under a set of interlocking pressures that make sustainable digital transformation a necessity rather than a choice. First, high seismicity and cascading geo-environmental risks place a premium on early warning, performance monitoring, and rapid post-event assessment. Recurrent earthquakes, rainfall-triggered landslides, and hydro-geotechnical failures demand data-rich, time-sensitive workflows that conventional, document-centric practices struggle to provide [69,70]. Second, rapid urbanization and mega-infrastructure delivery (metros, tunnels, bridges, corridors, coastal works) compress planning and construction schedules while increasing underground complexity and stakeholder interfaces. This raises the value of integrated models (for clash detection, constructability, and logistics) and continuous field sensing to control ground risk in dense urban fabrics [71,72,73]. A third driver is the sustainability policy agenda, which is progressively aligning infrastructure with resource efficiency, resilience, and lifecycle performance objectives. Meeting these goals requires decision support that links design options to cost, energy, carbon, and risk outcomes—capabilities that hinge on structured data, interoperable platforms, and feedback from monitoring to models [74,75,76,77]. Fourth, fragmented data ecosystems—legacy borehole archives, project-bound reports, proprietary formats, and siloed agency systems—constrain evidence-based decisions. Interoperability gaps (across GIS, BIM, monitoring databases, and analysis tools) impede reuse of ground data, hinder uncertainty communication, and limit automation (e.g., BIM-to-FEM pipelines, sensor-to-twin updates) [78,79,80,81]. Fifth, capacity and market structure shape adoption pathways. Heterogeneous digital literacy across public clients, contractors, and consultants, uneven access to specialized software/hardware, and procurement rules that emphasize lowest upfront cost over lifecycle value slow diffusion. These constraints are amplified by standards and governance gaps—limited common data environments, variable use of open schemas (e.g., IFC and geospatial standards), and inconsistent requirements for model deliverables and monitoring integration [82,83,84]. Finally, education and professional development needs persist: curricula and CPD pathways are catching up to multidisciplinary skills (geo-data science, information management, model-based coordination, IoT analytics) required to operationalize digital twins in practice [85,86]. Figure 3 summarizes the six contextual drivers that collectively shape the imperative for sustainable digital transformation in Türkiye’s geotechnical engineering.

Taken together, these drivers create both urgency (risk, schedule, compliance) and opportunity (productivity, safety, sustainability) for digitalization. They also explain why adoption in Türkiye has progressed asymmetrically—from GIS-centric islands to project-specific BIM uses and pilots in IoT/AI—while falling short of system-level integration. This context motivates a roadmap that (i) standardizes spatial and geotechnical data; (ii) connects design-construction-monitoring through interoperable platforms; and (iii) institutionalizes lifecycle feedback via digital twins. In Section 4.2, Section 4.3 and Section 4.4, the current trajectory is mapped and cross-cutting patterns are synthesized, before these contextual imperatives are translated into the rationale and structure of the SDIM–Geo–TR framework.

4.2. Evolution and Current Adoption of Digital Technologies in Türkiye

4.2.1. Adoption Trajectories

The trajectory of digital technology adoption in Türkiye’s geotechnical and relevant sectors has evolved through distinct phases, beginning with GIS-centered applications in the early 2000s. Kumsar et al. [87] pioneered structured urban geotechnical databases by combining GIS and DBMS, while Kahriman and Bozdoğan [88] extended digital practices to mining through Surfer-based computer modeling. This initial phase established GIS as a backbone for spatial data management. Garagon and Toz [89] further advanced the field by applying service-oriented GIS to support geoscientific data sharing, and Demirci and Karakuyu [90] integrated GIS with remote sensing for disaster monitoring and planning. Poyraz and Kalafat [91] introduced GIS into seismology for rapid data integration, whereas Kurnaz and Ramazanoğlu [92] applied GIS to settlement suitability analysis, emphasizing risk evaluation in urban planning. Collectively, these studies positioned GIS as the foundational enabler of Türkiye’s digital transition.

By the mid-2010s, research expanded beyond data archiving into decision-support and microzoning applications. Akyol et al. [93] integrated GIS with AHP to strengthen urban resilience through improved settlement suitability and geotechnical microzoning. Yalçın [94] extended open-source GIS applications to industrial inventory and risk assessment. During this stage, digitalization began shifting from static mapping toward decision-oriented frameworks. Tün et al. [95] introduced crowdsourced GIS–mobile app platforms for disaster management, and Bol et al. [96] added GIS–MapInfo for geotechnical assessment, linking efficiency and decision support. In the same year, Keleş and Keleş [97] mapped the growing role of BIM, GIS, UAVs, and IoT across Türkiye’s construction industry, signaling the sector’s gradual catch-up with global trends. Parallel to these efforts, new approaches appeared: Erturan et al. [98] explored digital twin concepts in construction auditing, highlighting real-time data integration for process efficiency.

The 2019–2021 period marked diversification into UAV, IoT, and BIM-oriented studies. Ceylan [99] conceptualized Digital Twins in the Turkish construction sector, closely relating them to BIM. Kun and Güler [100] demonstrated UAV–photogrammetry applications in marble mining, emphasizing efficiency and waste reduction. Around the same time, Küçük et al. [101] proposed IoT–fuzzy logic systems for disaster response, combining sensor data and clustering algorithms to optimize rescue operations. Erdik et al. [102] systematically analyzed BIM adoption in Türkiye, revealing barriers such as low expertise and lack of awareness but also identifying visualization as a strong driver. Complementary insights were provided by Memiş and Babaoğlu [103], who examined GIS, UAV, IoT, and VR/AR tools for disaster and emergency management, stressing technology-driven process improvements. Methodological advances continued with Demirbilek and Demirbilek [104] using Python–CPLEX for hydrogeological route optimization, Alver et al. [105] applying Deepsoil and SAP2000 for earthquake-resilient geotechnical design, and Bozkurt and Erenoğlu [106] introducing IoT–cloud early warning systems for railways. Şahin [107] achieved high accuracy in UAV-based volume analyses, while Uygunoğlu et al. [108] embedded IoT sensors for real-time structural monitoring. Collectively, these works reflect the transition from GIS dominance toward multi-technology ecosystems integrating BIM, UAV, and IoT.

From 2022 to 2023, adoption intensified through multi-technology integration and sustainability applications. Acar and Kaya [109] applied GIS–GPS for soil property evaluation, linking digital tools to disaster preparedness. Delibalta [110] highlighted IT–OT convergence in smart mining, and Torlak et al. [111] applied AR–GPR for infrastructure visualization. Coşandal and Partigöç [112] combined IoT, GIS, AI, and RS for urban risk management, while Ciritcioğlu et al. [113] integrated UAV and GIS for precise road excavation–fill calculations. Aladağ [114] evaluated sector-wide digital transformation, identifying BIM as widely used but AR and blockchain adoption as limited. In 2023, railway modernization was advanced by Gökçe et al. [115] through IoT–AI–RFID integration; Dereli and Çay [116] linked GIS–meteorological modeling to green infrastructure; Akdeniz and Ofluoğlu [117] demonstrated BIM–digital twin synergies in metro stations; Akbay et al. [118] showcased BIM–VR/AR–laser scanning on construction sites; and Eryaman and Akün [119] merged BIM, AR, AI, UAVs, and VR into safety inspections. This body of work reflects a clear convergence of technologies for sustainability, efficiency, and safety in Türkiye’s infrastructure projects.

The 2024–2025 period signals a transition into advanced digital twin ecosystems and cross-sectoral adoption. Baran et al. [120] assessed digital transformation across nine municipalities, highlighting fragmented but promising smart city initiatives. Çınar and Aslan [121] integrated BIM, SketchUp, and PLAXIS for geotechnical modeling, improving risk visualization and data transfer. UAV–GIS synergies continued in mining with Önal et al. [122] and Yiğit and Kaya [123], who emphasized monitoring, safety, and accuracy. Bozkurt et al. [124] evaluated BIM–VR integration in building production, noting collaboration gains but cost barriers. Bedur and Erbaş [125] pushed BIM–IoT–AI–VR/AR integration into lifecycle-oriented digital twin systems. Finally, Ekinci [126] provided a comprehensive review of digital twins in architecture and construction, projecting future advances such as city-scale integration and blockchain linkages. Collectively, these studies mark Türkiye’s gradual transition from GIS-based tools to fully integrated, lifecycle-focused digital ecosystems, with digital twins representing the frontier of sustainable geotechnical transformation. The evolution of digital technology utilization in Türkiye’s geotechnical and related sectors, from early GIS applications to advanced digital twin ecosystems, is systematically summarized in

Table 6. Chronological overview of digital technology adoption and integration in Türkiye’s geotechnical-related engineering disciplines.

Study	Application Area	Technologies Integrated	Key Outputs
Kumsar et al. [87]	Urban geotechnical systems	GIS + DBMS	Structured urban geotechnical database
Kahriman and Bozdoğan [88]	Mining	GIS	Mine planning & Data management
Garagon and Toz [89]	Geosciences	GIS	Service-oriented GIS & Data sharing
Demirci and Karakuyu [90]	Disaster management	GIS + RS	Disaster monitoring & Planning
Poyraz and Kalafat [91]	Seismology	GIS	Integrated seismic data & Rapid response
Kurnaz and Ramazanoğlu [92]	Settlement suitability	GIS	Risk evaluation & Suitability mapping
Akyol et al. [93]	Geotechnical	GIS + AHP	Decision accuracy
Yalçın [94]	Industrial inventory	GIS (open source)	Planning & Risk assessment
Tün et al. [95]	Disaster management	GIS + Mobile apps	Rapid response & Data integration
Bol et al. [96]	Geotechnical assessment	GIS + MapInfo	Decision Support & Efficiency
Keleş and Keleş [97]	Construction industry	BIM + GIS + UAV + IoT	Visualization & Design & Management
Erturan and Engin [98]	Construction engineering	Digital twin	Efficiency & Process digitalization
Ceylan [99]	Construction sector	Digital Twin	Guidance system
Kun and Güler [100]	Mining	UAV + Photogrammetry	Efficiency & Waste reduction
Küçük et al. [101]	Disaster management	IoT + Fuzzy logic	Building damage & Efficiency
Erdik et al. [102]	Construction	BIM	Barriers & Drivers
Memiş and Babaoğlu [103]	Disaster & Management	GIS + UAVs + IoT sensors + VR/AR	Process improvement
Demirbilek and Demirbilek [104]	Hydrogeology	Python + CPLEX	Route optimization & Cost reduction
Alver et al. [105]	Geotechnical Design	Deepsoil + SAP2000	Earthquake Resilience & Accuracy
Bozkurt and Erenoğlu [106]	Railways	IoT + Cloud	Early warning & Monitoring
Şahin [107]	Construction & Volume analysis	UAV + GPS	High-accuracy
Uygunoğlu et al. [108]	Structural monitoring	IoT	Real-time monitoring & Extended service life
Acar and Özdemir [109]	Soil evaluation	GIS + GPS	Urban planning& Disaster preparedness
Delibalta [110]	Mining	IT–OT + Smart mining	Resource efficiency& Sustainability
Torlak et al. [111]	Infrastructure visualization	AR + GPR	Planning & Accessibility
Coşandal and Partigöç [112]	Urban risk management	IoT + GIS + AI + RS	Urban Resilience & Risk reduction
Ciritcioğlu et al. [113]	Road design	UAV + GIS + DEM/DTM	Accurate calculations & Planning efficiency
Aladağ [114]	Construction industry	BIM	Quality management& Communication
Gökçe et al. [115]	Railways	IoT + AI + RFID + Sensors	Efficiency & Safety & Sustainability
Dereli and Çay [116]	Infrastructure	GIS + Meteorological SW	Risk reduction & Green infrastructure
Akdeniz and Ofluoğlu [117]	Metro stations	Digital Twin	Energy Efficiency & Sustainability
Akbay et al. [118]	Construction sites	BIM + VR/AR + Laser scanning	Collaboration& Efficiency
Eryaman and Akün [119]	Construction safety	BIM + AR + AI + UAV + VR	Safety & Real-time monitoring
Baran et al. [120]	Governance	BIM + GIS	Adoption
Çınar and Aslan [121]	Geotechnical modeling	BIM + SketchUp + PLAXIS	Integrated modeling & Risk reduction
Önal et al. [122]	Mining	UAV + GIS + Netcad	Monitoring & Safety & Efficiency
Yiğit and Kaya [123]	Mining	UAV + Photogrammetry	Accuracy & Cost savings
Bozkurt et al. [124]	Building production	BIM + VR	Improved collaboration & Cost barrier
Bedur and Erbaş [125]	Construction	Digital Twin	Lifecycle management & Quality
Ekinci [126]	Architecture & Construction	Digital Twin + BIM + IoT + AI/ML	Comprehensive overview & Future trends

.

4.2.2. Cross-Cutting Patterns and Synthesis

The reviewed body of 40 studies reveals distinct clustering in terms of adopted technologies, application domains, and sustainability dimensions. In terms of digital technologies, GIS remains the dominant platform, often combined with other tools such as AHP, MapInfo, or GPS. BIM and its extensions (digital twins, PLAXIS) are the second most common, mainly for modeling, collaboration, and lifecycle management. While IoT-enabled sensing and monitoring systems are frequently combined with AI or cloud computing, UAV-based photogrammetry finds particular application in mining, construction, and road projects. Other immersive and visualization technologies such as VR/AR, IT–OT convergence, Surfer, Python–CPLEX, meteorological modeling, highlighting the diversity of computational approaches supporting digital transformation. From a sustainability perspective, efficiency gains emerge as the most frequently reported benefit, followed closely by improvements in safety, risk reduction, and resilience. Enhancements in decision support and accessibility are also highlighted, while contributions to green infrastructure are less commonly discussed. These observations indicate that digital adoption in Türkiye has progressed from operational optimization toward a broader sustainability agenda, where efficiency, safety, and resilience dominate the focus of technology implementation. The distribution of digital technologies and sustainability components across the reviewed studies shown in Figure 4.

Moreover, building on the distribution patterns, a temporal analysis reveals how the focus and objectives of digital technology adoption in Türkiye’s geotechnical-related sectors have shifted over distinct periods. In the early years (2004–2012), emphasis was placed primarily on efficiency outcomes, reflecting a focus on cost and resource optimization during the first wave of GIS-centered applications. Between 2013 and 2018, safety, risk reduction, and decision-support functions gained prominence, aligning with the rise of hazard mapping, microzoning, and the integration of multicriteria decision-making frameworks. The most recent period (2019–2025) shows a marked diversification, with sustainability-oriented objectives such as green infrastructure and resilience emerging alongside continued attention to efficiency and safety. This trajectory demonstrates how Türkiye’s digital geotechnics has progressed from operational gains toward a broader sustainability agenda, integrating resilience and environmental considerations into digital transformation pathways. These findings confirm that digital transformation in Türkiye’s geotechnical and construction sectors has evolved from GIS-dominated systems toward multi-technology ecosystems, with sustainability dimensions—especially efficiency, safety, and resilience—serving as the primary drivers of adoption.

4.3. Implementation Framework: Barriers and Opportunities

The synthesis of the reviewed studies highlights both the promise and the challenges of implementing digital technologies in geotechnical and related domains. As summarized in

Table 7. Implementation framework of digital technologies in Türkiye’s geotechnical-related engineering disciplines.

Technology	Current Usage	Barriers	Opportunities
GIS	Widely used in geotechnical disaster management	Data integration & Standardization	Decision support & Rapid response
BIM	Growing in construction	High cost & Skill gaps & Data integration	Lifecycle management & Sustainability & Stakeholder engagement
UAV	Increasing use in construction	Initial investment & Training needs	High accuracy & Improved safety & Cost savings
IoT	Emerging in safety, risk management, and optimization	Data security & System integration & Expertise gaps	Predictive maintenance & Efficiency & Real-time monitoring
Digital Twin	Applied in safety and visualization	Hardware/software costs & Training requirements	Enhanced learning & Improved safety & Immersive visualization

, GIS remains the most established platform, supporting spatial data management and rapid disaster response, yet persistent issues of integration and standardization constrain its full potential. BIM and digital twin applications are gaining traction in construction, offering transformative opportunities for lifecycle management and sustainability, but they face considerable barriers in terms of cost, expertise, and data interoperability. UAV-based photogrammetry demonstrates clear advantages in safety, accuracy, and cost reduction, although high initial investment and training requirements continue to limit wider adoption. IoT-enabled sensing, AI, and data mining approaches, while emerging as powerful tools for predictive maintenance and efficiency gains, raise concerns over data security, system integration, and specialized expertise. Digital Twin technologies, used in safety, education, and visualization, encounter cost and training barriers but present unique opportunities for immersive learning and real-time safety enhancement.

4.4. Rationale for the SDIM–Geo–TR Framework

The synthesis of 40 reviewed studies (Section 4.2) shows that Türkiye’s digital adoption remains fragmented across technologies and domains: GIS dominates hazard mapping, BIM supports construction and facility management, UAVs enhance monitoring accuracy, and IoT/AI provide real-time sensing. While these efforts respond to pressing challenges—seismic risk, urbanization, and sustainability—they often remain project-specific and lack system-level coordination. This fragmentation underscores the need for a unifying framework that can integrate diverse technologies into a coherent national trajectory.

International maturity models in BIM, Construction 4.0, and digital twin readiness offer structured pathways for staged adoption. However, they do not address geotechnical complexities such as subsurface uncertainty, seismic risk, and fragmented data ecosystems. Türkiye’s contextual drivers—including high seismicity, rapid mega-project delivery, sustainability mandates, and institutional capacity gaps—demand a tailored approach. SDIM–Geo–TR adapts international lessons but grounds them in Türkiye’s geotechnical realities, ensuring both global alignment and local relevance. Moreover, findings demonstrate that efficiency (30.0%), safety and resilience (25.0%), and decision support (17.5%) dominate current digital outcomes, whereas green infrastructure and long-term sustainability remain underrepresented. Without a guiding framework, operational benefits risk overshadowing environmental and lifecycle imperatives. SDIM–Geo–TR explicitly incorporates five evaluative criteria—sustainability impact, technical feasibility, data compatibility, cost-effectiveness, and adoption level—ensuring that digital transformation is measured not only by efficiency but also by resilience, environmental gains, and institutional learning.

Barriers identified across the reviewed studies include lack of interoperability (e.g., GIS–BIM data transfer), high costs (e.g., digital twins), and limited digital literacy among stakeholders. The SDIM–Geo–TR framework provides a structured response by offering:

• Standardization of spatial and geotechnical data exchange (addressing interoperability gaps);
• Bridging of design, construction, and monitoring platforms (enabling BIM–GIS–IoT integration);
• Institutionalization of lifecycle feedback through digital twins (transforming pilots into systemic practice).

Through these mechanisms, the framework not only consolidates technological adoption but also guides procurement policies, educational curricula, and governance reforms.

Taken together, these rationales establish the necessity of SDIM–Geo–TR as a sustainability-aligned digital maturity framework tailored to Türkiye’s geotechnical context. It provides a roadmap that addresses fragmented adoption, aligns with international best practice, embeds sustainability, and facilitates institutionalization. Section 5 introduces the SDIM–Geo–TR Model in detail, outlining both literature-based development pathways and the evaluative criteria that structure its design.

5. SDIM–GEO–TR Model

5.1. Four Stages of the SDIM–Geo–TR Framework

The reviewed studies collectively propose a phased roadmap for integrating digital technologies into geotechnical-related practice in Türkiye. A first step involves consolidating foundational platforms such as GIS and BIM, which provide essential capabilities for data management and spatial analysis. The next stage builds on these foundations by incorporating more advanced technologies, including UAVs, IoT, and digital twin, to enhance monitoring accuracy, automation, and predictive analytics. Ultimately, the trajectory culminates in comprehensive digital twin systems with real-time monitoring capabilities, enabling proactive risk management and lifecycle sustainability. Interoperability and data standardization are consistently highlighted as prerequisites for achieving this progressive integration.

To enable this transition, the studies emphasize the need for deliberate adoption strategies. These include targeted professional training and continuous capacity development, curriculum updates to embed digital competencies in engineering and planning education, and fostering interdisciplinary collaboration across geotechnical, construction, and information sciences. Investment in digital infrastructure and supportive institutional frameworks is also regarded as essential for overcoming structural barriers.

Sustainability objectives remain central to these pathways. The evidence shows that digital technologies contribute to resource optimization, environmental impact reduction, and long-term monitoring. Applications such as smart mining, circular economy models, and digital twins illustrate how operational efficiency can align with environmental stewardship. More broadly, integration of digital systems is reported to advance efficiency (cost, time, resource), safety and resilience, data accessibility and integration, planning and decision support, as well as compliance and collaborative capacity.

While the evidence base remains heterogeneous, there is a consistent emphasis on the transformative potential of GIS, BIM, digital twins, UAVs, and related technologies. Barriers—including high cost, skill gaps, and data integration challenges—are acknowledged, yet the collective findings stress that successful adoption depends on interdisciplinary collaboration, continuous professional training, and alignment with sustainability goals. Synthesizing these pathways highlights the need for a structured maturity model. The SDIM–Geo–TR framework responds to this gap by organizing Türkiye’s digital transformation into four cumulative phases given in Figure 5:

Phase 1: Foundational Digitalization

This stage focuses on establishing digital baselines through GIS repositories, metadata structures, and hazard-mapping layers. It corresponds to the creation of structured geospatial databases as seen in [127,128] and aligns with ISO 19650 principles [129] on information management. In Türkiye, fragmented GIS initiatives have provided valuable hazard maps and settlement suitability assessments, but without standardized schemas they remain insufficient for integration with advanced modeling environments.

Phase 2: Model-Based Coordination

Building on Stage 1, geotechnical data is integrated into three-dimensional BIM environments, enabling lifecycle traceability of ground–structure interactions. This reflects the “model-based systems engineering” paradigm and BIM maturity theory [130]. Case studies such as Wu et al. [131] demonstrate the feasibility of embedding geotechnical elements (fore piles, soil layers, support systems) into BIM–IFC structures, enhancing constructability assessment and interdisciplinary coordination. The current reliance on static PDF reports in many mega-projects, however, highlights the gap that this stage seeks to close.

Phase 3: Data-Rich Monitoring and Analytics

At this level, IoT-enabled systems, UAV photogrammetry, and AI-based analytics create digital observatories that provide real-time feedback across infrastructure lifecycles [132]. Internationally, resilience engineering and predictive maintenance paradigms guide this stage, while in Türkiye pilot efforts—such as sensorized metro stations or seismic retrofits—illustrate early but fragmented adoption.

Phase 4: Digital Twin Ecosystems

The final stage represents cyber-physical integration, where BIM, IoT, UAV, and LiDAR-derived data streams converge into dynamic, self-updating platforms. These ecosystems recalibrate numerical models in real time, supporting proactive risk management and lifecycle sustainability [133]. In Türkiye, initiatives like the Istanbul Seismic Risk Mitigation and Emergency Preparedness Project (ISMEP) show conceptual interest, yet high costs, governance gaps, and skill shortages remain significant constraints [134].

The stages provide a theoretically grounded and contextually relevant roadmap for Türkiye. They demonstrate an incremental transition from isolated GIS databases to full-fledged digital twin ecosystems, ensuring that digitalization aligns with sustainability objectives, lifecycle efficiency, and resilience-driven infrastructure planning. While the four stages define the structural progression of the SDIM–Geo–TR framework, their operationalization requires measurable and context-specific evaluative dimensions. To this end, five core criteria were identified through a synthesis of international digital maturity models and validated by expert consultations in the Turkish geotechnical context. These criteria are introduced in Section 5.2.

5.2. Evaluation Criteria

A robust evaluation of Türkiye’s geotechnical digital transformation requires criteria that are both theoretically grounded and contextually validated. To establish such a foundation, two complementary methodological tracks were employed. First, a synthesis of established international digital maturity frameworks was undertaken, including BIM capability stages and capability maturity matrices [135], infrastructure transformation roadmap [136], and sustainability-oriented guidelines from the United Nations Sustainable Development Goals (SDGs) [137] and the Organization for Economic Cooperation and Development (OECD) [138]. Second, this conceptual base was tested through semi-structured interviews with seven experts representing academia, public institutions, and private sector geotechnical consultancies. This dual approach ensured that the evaluative criteria capture both the universality of digital maturity theory and the particular institutional, regulatory, and technical realities of Türkiye. Five core criteria emerged from this process:

• Sustainability Impact: Captures environmental and resilience outcomes such as reduced carbon emissions, lifecycle energy savings, and enhanced disaster preparedness. In Türkiye, examples include UAV-enabled slope monitoring in Rize and life-cycle assessments in dam projects, both of which demonstrate the integration of digital tools with sustainability objectives.
• Technical Feasibility: Reflects the maturity and applicability of technologies under real geotechnical field conditions. It ranges from constraint to enabler, depending on the availability of reliable hardware, software, and local expertise. Practical instances include MEMS-based monitoring systems deployed in Istanbul metro excavations.
• Data Compatibility: Concerns interoperability across geotechnical, structural, and spatial datasets. This includes the capacity of BIM, GIS, and monitoring databases to exchange data in standardized formats such as IFC and CityGML. The ongoing challenges of fragmented datasets archives highlight the need for this criterion in Türkiye.
• Cost-Effectiveness: Evaluates financial performance across the lifecycle of digital systems, including both initial investment and long-term operational savings. UAV-based monitoring in projects illustrates how relatively high upfront costs can be offset by significant efficiency and safety gains.
• Adoption Level: Encompasses ecosystem-wide uptake, digital literacy, and institutional embedding of new technologies. National emerging university–industry curricula collaborations indicate how adoption levels are gradually shifting from isolated pilots to wider diffusion.

Collectively, these criteria provide an operational lens for assessing Türkiye’s position along the SDIM–Geo–TR maturity pathway. Each criterion is not static but evolves across the four transformation stages defined, shifting roles from constraint to enabler and ultimately to core benefit. Their phase-dependent behavior is further elaborated in Section 5.3 through a phase–criteria interaction matrix.

5.3. Phase–Criteria Interaction

The evaluative criteria outlined in Section 5.2 are not static; their significance and functional roles evolve as Türkiye’s geotechnical sector progresses across the four stages of the SDIM–Geo–TR framework. To capture this dynamic behavior, the criteria were operationalized in two complementary ways. Figure 6 illustrates how the roles of each criterion shift across the transformation pathway—from initial constraints in the early phases to enablers and eventually core benefits in mature digital ecosystems.

Figure 6 illustrates the evolution of the five evaluation criteria—Sustainability Impact, Technical Feasibility, Data Compatibility, Cost-Effectiveness, and Adoption Level—across the four phases of the SDIM–Geo–TR framework. In Phase 1, Foundational Digitalization, most criteria remain at the constraint level. During Phase 2, Model-Based Coordination, the criteria begin to transition toward enabler roles. In Phase 3, Data-Rich Monitoring, Sustainability Impact, Technical Feasibility, and Cost-Effectiveness emerge as clear benefits, whereas Adoption Level and Data Compatibility continue to play transitional roles. By Phase 4, Digital Twin Ecosystems, Sustainability Impact and Technical Feasibility attain strategic benefit status, and all criteria are fully aligned with lifecycle implementation.

This visualization allows reviewers to compare strengths and weaknesses of each phase at a glance, showing how the transformation matures from constraint-dominated beginnings to a fully enabling ecosystem. Moreover, technical feasibility and cost-effectiveness initially emerge as constraints due to high investment costs, fragmented expertise, and weak interoperability. In the early stages, most criteria appear as constraints, reflecting barriers such as high costs, limited expertise, and fragmented standards. Progress into coordination and monitoring phases gradually transforms these into enablers and benefits, with sustainability impact and adoption level becoming particularly critical in later stages. Ultimately, in Phase 4, digital twin ecosystems establish sustainability, cost-effectiveness, and adoption as core benefits and scaling drivers, underlining the maturity of Türkiye’s geotechnical digital transformation.

6. Conclusions

This study systematically reviewed empirical and conceptual works on the adoption of digital technologies in Türkiye’s geotechnical and construction sectors. The analysis demonstrated that early adoption was GIS-centered, gradually evolving into multi-technology ecosystems involving BIM, UAVs, IoT, and, most recently, digital twins. Cross-cutting synthesis showed that efficiency, safety/resilience, and decision support remain the dominant sustainability drivers, while barriers are rooted in data fragmentation, skill gaps, and high implementation costs.

Building on this evidence, the paper introduced the SDIM–Geo–TR framework, a four-stage maturity roadmap supported by five evaluative criteria (sustainability impact, technical feasibility, data compatibility, cost-effectiveness, and adoption level). Together, these elements provide a structured approach to assessing and guiding Türkiye’s digital transformation in geotechnics. The SDIM–Geo–TR model contributes strategically to Türkiye’s digitalization journey by linking academic insights with practical pathways. At the academic level, it addresses a literature gap by offering the first contextualized digital maturity framework tailored to geotechnical engineering in Türkiye. At the policy and practice level, it translates fragmented case evidence into a roadmap that policymakers, agencies, and industry actors can operationalize. The integration of international theories with Türkiye-specific constraints ensures both global relevance and local applicability.

The framework highlights how digitalization in geotechnics can advance sustainability goals, from lifecycle efficiency to resilience and environmental stewardship. For policymakers, the model provides guidance for setting national standards, reforming procurement practices, and supporting capacity-building programs. For practitioners, it offers a structured lens to prioritize investments in GIS, BIM, IoT, and digital twins according to sectoral readiness. For academia, it provides a scalable framework for comparative studies across regions and technologies. Although empirically calibrated for Türkiye, the SDIM–Geo–TR framework is conceptually transferable to other national contexts; by adjusting data inputs and evaluative parameters, it can guide sustainable digital transformation in diverse geotechnical environments worldwide. This generalizability, however, does not eliminate practical limitations, as its implementation may still be constrained by financial barriers, inconsistent data standards, and varying organizational readiness—making operational success dependent on institutional adaptation, continued investment in digital literacy, and harmonized interoperability frameworks.

Future research should focus on the integration of secure data-sharing architectures, AI-enabled monitoring frameworks, and scalable digital twin implementations across regional infrastructure networks. Equally important will be the development of governance mechanisms that ensure interoperability and the fostering of interdisciplinary collaboration, thereby enabling digitalization to deliver demonstrable advancements in sustainability, resilience, and societal value.