Search Results (675)

The construction sector is undergoing a rapid transition toward more resilient, sustainable, and digitally connected systems, creating increasing demand for materials capable of providing functions beyond conventional structural performance. In this context, smart materials have emerged as promising solutions due to their ability to respond to mechanical, thermal, chemical, or electromagnetic stimuli through adaptive behaviors such as self-healing, structural sensing, energy regulation, vibration control, and reversible deformation. Despite growing scientific interest, available knowledge remains fragmented across specific material families and isolated application domains. Therefore, this study presents a PRISMA-based systematic review of smart materials in construction using peer-reviewed journal literature indexed in Scopus during the 2021–2026 period. The review examines the principal smart material families currently applied in construction, including self-healing concretes, self-sensing cementitious systems, Shape Memory Alloys (SMA), piezoelectric materials, phase change materials, adaptive coatings, conductive nanocomposites, and multifunctional geopolymers. Their engineering functions, structural and architectural applications, reported performance characteristics, sustainability contributions, digital integration potential, and implementation barriers are comparatively discussed and qualitatively synthesized based on the reviewed literature. The findings indicate that smart materials can improve durability, structural health monitoring, seismic resilience, thermal efficiency, lifecycle performance, and carbon reduction when properly integrated into buildings and infrastructure. However, large-scale adoption remains constrained by high initial costs, manufacturing scalability, regulatory uncertainty, long-term durability validation, and limited market confidence. The review further shows that the greatest future potential lies in combining material intelligence with IoT platforms, artificial intelligence, BIM environments, and digital twins. Overall, smart materials are positioned as strategic enablers of next-generation low-carbon, adaptive, and intelligent construction systems. Full article

Fire safety in high-rise buildings represents a critical challenge in the United Arab Emirates (UAE), where intensive urbanization, extreme climatic conditions, and multilayered regulatory frameworks impose unique competency demands on architects and Fire Safety Engineering (FSE) consultants. Despite this, no empirically validated competency framework exists that simultaneously addresses both professional groups and is tailored to the specificities of the UAE context. This study aimed to construct and empirically validate such a framework. A three-phase sequential exploratory mixed-method design was employed. In the first phase, a systematic literature review yielded a preliminary set of 69 competency indicators organized within a Knowledge, Skills and Attitudes (KSA) structure. In the second phase, a three-round Delphi technique with an expert panel of 18 specialists validated the set to 62 final indicators. In the third phase, importance–performance analysis (IPA) was conducted on a sample of 250 professionals actively engaged in fire safety projects across four UAE. IPA identified 16 priority competency gaps, most pronounced in digital transformation (BIM, CFD, AI; gap = 1.23), proactive client advisory competencies (gap = 1.21), and regulatory navigation and Civil Defence coordination (gap = 1.00). A counterintuitive finding emerged whereby architects systematically rated competencies higher than FSE consultants across all dimensions (all p < 0.05). Psychometric validation confirmed excellent instrument reliability (Cronbach’s Alpha > 0.95) and a theoretically consistent three-factor KSA structure explaining 70.06% of variance. The developed framework of 62 empirically validated indicators represents the first competency model of its kind for architects and FSE consultants in the Gulf Cooperation Council (GCC) region. Its findings provide a direct empirical basis for curriculum reform, Continuing Professional Development (CPD) programmes, and professional licencing standards in the UAE and across the GCC region. The study makes three original contributions: the first empirically validated UAE-specific competency framework for these professional groups; a methodological combination of Delphi, IPA, EFA, Mann–Whitney, and Kruskal–Wallis not previously applied in fire safety competency research; and empirical confirmation that 74% of indicators required original development or adaptation, demonstrating the limitations of generic international competency models in the UAE context. Full article

Conventional rock blasting produces large rock masses that do not fully meet engineering construction requirements. Therefore, mechanical crushing technology is necessary to reduce these masses into crushed stone of a specific particle size. Consequently, enhancing the comprehensive utilisation rate of excavated materials and exploring new application avenues has become critical. Initial crushing experiments were conducted on limestone of varying strengths. Based on the measured parameters, simulation experiments were performed to analyse the accuracy of crushing particles of different strengths. Cube specimens confirmed that the created crushing model accurately reflects the actual crushing behaviour of particles with different strengths. A Structure Sensor 3D scanner was used to scan representative shapes of rock particles. Software processing yielded the true three-dimensional apparent morphology of the rock material. Combined with physical crushing tests and simulation experiments, this confirmed that the developed crushing model accurately reflects the actual crushing behaviour of rock particles when their true morphology is considered. The research findings demonstrate that the digital crushing model can accurately depict the crushing process and particle size distribution of rock materials with different lithological characteristics and true morphology. Full article

The larger research problem is to get combustion engines more effective and flexible and reduce or even eliminate greenhouse gas emissions. Here we concentrate more on a smaller-scale and focused research problem about the significance of air feeding in engine operation. Therefore, the need for modeling a charge air system is obvious. The interaction and co-operation between the charge air systems and combustion engines is a central issue in this article. A literature review was carried out on related topics, and it reveals a research gap in this area. A simulation model of a charge air system based on first principles is developed. It is based on physical and systemic modeling, and it is constructed including control loops reducing and controlling the pressures in the charge air chain. The simulation models of this auxiliary system and engine are successfully combined, and functioning together is demonstrated. The composed models represent real research laboratory equipment in the University of Vaasa Energy Laboratory under construction. The research laboratory equipment and the whole research environment are described. Simulation scenarios are presented both with the charge air system alone and with the combined model, including also the engine part. The significance of the developed models is discussed, and the path towards a digital twin experiment environment is outlined. As a conclusion, we can claim that the combined simulation model is successfully constructed and shown to operate in a stable and physically plausible manner. The digital twin concept can be tested completely only when the research laboratory is constructed and ready and the test runs begin to produce measurement data for the digital part. Then also the simulation models can be tuned to a better accuracy level, and the operation as a digital twin will be verified. Full article

Prefabricated timber construction relies on coordinated digital processes that integrate architectural design, structural engineering, and manufacturing requirements. However, current industry practices are highly fragmented, with models often reconstructed across different software platforms, and collaboration is mainly focused on exchanging files and document-based approvals. These issues lead to geometric misalignments, delayed coordination of manufacturing limitations, and inefficient design-to-manufacturing workflows. This study introduces a parametric geometry-based integration framework aimed at enhancing digital continuity throughout the design, engineering, and manufacturing stages of prefabricated timber buildings. The framework offers a rule-based parametric system where geometry is governed by specific relationships and constraints, enabling the development of discipline-specific models from a unified computational source. A model was created using Rhinoceros and Grasshopper to generate a parametric timber module and to test cross-platform compatibility with structural analysis software (Dlubal) and manufacturing detailing software (Cadwork). The results show that parameter-driven geometry can be integrated across platforms, supporting reduced primary geometry re-authoring and improved cross-platform coordination within the proof-of-concept workflow. The framework extends technical validation by shifting parametric modelling from merely a generative design tool to a comprehensive infrastructure that supports industrialised timber workflows. The proposed approach provides a practical solution to enhance design-to-manufacturing integration in prefabricated construction, while maintaining modelling flexibility specific to each discipline. Full article

Precise dimensional conformity of precast concrete members is critical for structural performance and on-site assembly accuracy, yet conventional manual inspection remains labor-intensive and unable to scale with modern production-line throughput. Existing scan-vs-BIM approaches address geometric verification in principle but are constrained by manual registration dependencies, the absence of machine-readable IFC-linked tolerance criteria, and limited validation under real factory yard conditions. This study presents a production-scale automated shape quality control (SQC) framework that closes all three gaps simultaneously. A purpose-designed two-point target device enables fully automated, repeatable registration seed-point extraction. A formal IFC property-set-linked rule engine architecture—comprising entity extraction, deviation computation, rule interpretation, and pass/fail decision stages—replaces ad hoc script-based tolerance checking with an interoperable, auditable compliance pipeline. Factory-scale validation on precast arch segments (n = 10) and wall panels (n = 12) achieved registration RMSE of 1.25–1.95 mm, pass rates exceeding 91%, and a 37.1% reduction in inspection time versus manual methods (95% CI: 34.5–39.6%; p < 0.001; Cohen’s d = 3.89). Repeatability testing yielded ICC = 0.971 and Bland–Altman limits of agreement of [−0.45, +1.07] mm. The framework represents a substantive step toward fully digital, production-integrated quality management for industrialized precast construction. Full article

Digital twin technology is becoming a core enabler for the intelligent transformation of civil engineering. This review adopts an integrated mixed-method design that combines a reproducible bibliometric protocol with structured content analysis to connect macro-level knowledge evolution with domain-specific engineering implementation. Based on the Web of Science Core Collection, the study analyzes publication trends, collaboration patterns, highly cited studies, keyword co-occurrence, network centrality, and citation bursts, and then reviews application status and technical pathways across five thematic areas: intelligent construction, bridge engineering, tunnel engineering, smart water conservancy, and other infrastructure. Key findings include: rapid growth in publication volume after 2021, three dominant keyword clusters (model/system construction, structural health monitoring and sensing, and AI-enabled optimization/decision-making), and an evolution of research frontiers from concept introduction to engineering scenario deepening and further to three-dimensional reconstruction, knowledge fusion, and intelligent decision-making. The content analysis shows differentiated technical pathways across sub-domains and identifies data heterogeneity/interoperability as the most urgent bottleneck because it constrains model updating, cross-platform integration, and engineering-scale deployment. Future directions should focus on data standardization, hybrid modeling, platform interoperability, artificial intelligence empowerment, and full-lifecycle cross-system coordination. This review provides a quantitatively supported panoramic reference for digital twin research in civil engineering. Full article

Driven by the digital transformation of territorial spatial governance, traditional urban planning is irreversibly shifting towards a data-driven empirical paradigm. However, constrained by mimetic isomorphism and path dependence, many geography-based regional universities remain trapped in an educational dilemma: they overemphasize morphological representation while marginalizing quantitative decision-making, fostering a structural mismatch between graduate competencies and industry demands. To explore a systematic pathway out of this dilemma, this study chronicles a three-year pedagogical intervention utilizing a mixed-methods design with a historical control cohort (N = 275) within the urban planning program of Gansu Agricultural University—a regional institution situated in a less-developed frontier where territorial renewal demands macro-spatial synthesis over aesthetic forms. The intervention strategically redefined the graduate competency profile as “spatial data analysts”, constructing a pedagogical model comprising foundational algorithmic training, cross-disciplinary faculty collaboration, and real-world Project-Based Learning (PBL), coupled with a restructured, evidence-based evaluation system. Longitudinal tracking and quantitative analyses indicate a structural alignment with elevated educational efficacy. At the macro level of employment trajectories, the proportion of graduates securing knowledge-intensive data positions experienced a structural shift, rising from a baseline of 14.5% to 42.5%, reflecting an enhanced capacity to capitalize on expanding societal demands. At the meso level of practical competence, the award rate in high-level professional competitions increased by 35.4%. At the micro cognitive level, the new evaluation mechanism is associated with a successful redirection of students’ cognitive resources toward algorithmic logic and policy translation (p < 0.001) while highly significantly enhancing their self-efficacy in tackling complex, wicked engineering problems (p < 0.001). Rather than isolating pure causal mechanics, this study interprets these systemic gains as a contextual realignment of academic supply. It provides a context-sensitive, reproducible methodological reference for cultivating professional distinctiveness and reshaping the spatial planning education system in the digital era. Full article

The Architecture, Engineering, and Construction (AEC) industry has significant potential to improve productivity, quality, and sustainability of its projects through emerging digital technologies. Advances in technology and the complexity of what new graduates need to learn have resulted in persistent training gaps and have highlighted new needs to be addressed in education. One of the new needs is the level of learners’ awareness of new technologies and their adoption practices. This research examines how current education and training practices in the selected sample of the Australian AEC sector support or hinder the development of digital capabilities. The set of technologies considered in this study focuses on Artificial Intelligence (AI), Building Information Modelling (BIM), Digital Twins (DTs), Virtual and Augmented Reality (VR/AR), and the Internet of Things (IoT). A mixed-method design integrates a structured survey of industry professionals and students, along with semi-structured interviews of industry and academic stakeholders, to evaluate exposure, self-rated capability, training participation, organisational support, and perceptions of graduate preparedness. Findings show comparatively higher maturity in BIM, but limited capability in other technologies, inconsistent formal training, and barriers linked to time, cost, organisational priorities, and rapid technological change. Qualitative findings and interpretation of preparedness-related survey responses indicate that stakeholders place greater value on transferable, interdisciplinary digital competencies than on narrow tool-specific proficiency. The research delivers statistically robust findings and actionable recommendations that address the identified barriers and promote the development of a skilled workforce in the AEC industry. Full article

In the digital economy era, computing power, as a novel factor of production, serves as a vital engine for driving high-quality economic development. Building upon China’s traditional 42-sector input–output table, this paper incorporates computing power networks as a new sector to construct a 43-sector dynamic input–output (IO) model. Based on this framework, a Dynamic Stochastic General Equilibrium (DSGE) analysis framework is constructed to systematically reveal the dynamic transmission mechanism of computing power within industrial linkages and capital accumulation. From an energy perspective, energy consumption is implicitly captured through carbon emissions and energy structure, which together reflect the scale, efficiency, and composition of energy use in computing power networks. The findings show that the optimal computing power allocation follows a temporal evolution pattern from the service sector to the manufacturing sector, with ICT manufacturing’s computing power quota reaching 31% by 2030. An investment inflection point occurs in 2026, aligning with the digital infrastructure cycle of China’s 14th Five-Year Plan. The “Eastern Data, Western Computing” strategy reduces unit carbon emissions from computing power by 41%. Policy simulations demonstrate that R&D tax credits generate a 2.9-fold multiplier effect through industrial linkages, boosting GDP by 2.3%. The integrated IO-DSGE framework developed in this study provides a quantitative tool for the full-cycle management of “construction–application–regulation” in computing power networks. It holds significant theoretical value and practical implications for enhancing resource allocation efficiency and promoting green, climate-friendly development. Full article

Engineering consultants and design firms are central to the success of construction projects. However, the systematic evaluation of their performance in the Saudi Arabian context remains methodologically fragmented and empirically underdeveloped. Existing prequalification frameworks rely predominantly on administrative criteria and single-method ranking approaches that cannot adequately differentiate between high- and low-performing firms. To address this gap, the study proceeds in two distinct parts. Part I—Literature Review: A PRISMA-compliant systematic literature review across five major academic databases was conducted to map the existing evidence base, identify three substantive gaps in the Saudi and GCC engineering firm evaluation literature, and derive a consensus-based set of 29 performance criteria grouped into seven dimensions. This review constitutes an independent contribution: it establishes the gap that motivates the empirical work and provides the criterion framework on which that work is built. Part II—Practical Application: A structured questionnaire was administered to 288 construction professionals in Saudi Arabia (Cronbach’s α = 0.936), and the collected data were analyzed through a hybrid RII–Shannon Entropy Weighting (EWM)–TOPSIS pipeline that produced a Composite Priority Index (CPI) for each criterion, enabling a stable and discriminating ranking that integrates subjective expert consensus with objective distributional information. The main finding revealed that five criteria attained Very High Priority status (CPI > 0.70): Supervisory Experience (CPI = 0.740), Engineers’ Capability Index (CPI = 0.717), License Class (CPI = 0.709), Client Satisfaction Index (CPI = 0.708), and Average Delay Time (CPI = 0.705). These top-ranked criteria collectively center on technical leadership, regulatory standing, client-reported outcomes, and schedule reliability, indicating that procurement decisions should prioritize demonstrable competence over structural size or geographic footprint. The consistently lower importance of physical branch networks and headquarters location further suggests that remote management capabilities and digital coordination tools are reshaping performance expectations under Saudi Vision 2030. The Quality Indicators dimension achieved the highest mean CPI across all seven dimensions. The findings provide actionable evidence for procurement authorities, regulatory bodies, and engineering firms seeking to strengthen performance-evaluation practices in the Saudi construction sector. Full article

Building Information Modeling (BIM) implementation is increasingly adopted in the architecture, engineering, and construction (AEC) industries. However, its integration into the academic curricula in developing countries remains limited. Therefore, this study aims to investigate the barriers to integrating BIM into the curricula of civil engineering in Jordanian higher education institutions (HEIs). A quantitative approach was used, including Exploratory Factor Analysis (EFA) and Partial Least Squares Structural Equation Modeling (PLS-SEM). The data was collected from 102 respondents, including industry professionals and academics. Six key barrier constructs were identified: support, standards, delivery, resources, knowledge, and infrastructure and security. Altogether, they explain 66.896% of the BIM integration barriers. The results of the structural model indicate that institutional and governmental support is the most critical barrier (β = 0.486), followed by the lack of standards (β = 0.206) and curriculum-delivery constraints (β = 0.166). Other barriers, including infrastructure and security-related factors, knowledge gaps, and resource limitations, were found to have statistically significant effects on BIM integration. The findings revealed that the barriers to integrating BIM into civil engineering curricula in Jordanian HEIs are institutional and systemic rather than purely technical or resource-based. This study contributes to the BIM education literature by developing one of the first empirically validated PLS-SEM models to investigate barriers to integrating BIM curriculum in Jordan and in developing countries. This research is distinct from previous descriptive studies by prioritizing the institutional, technical, and curricular barriers to the integration of BIM into civil engineering education. Practically, the research provides a specific roadmap for Jordan to integrate BIM into curricula through improving the collaboration between HEIs and the Jordan Engineering Association, strengthening the accreditation standards, enhancing the support of the government for digital construction education, and endorsing the partnerships between HEIs and the industry to align the graduates with the needs for digital transformation of the construction sector in Jordan. Full article

Soil erosion is a global ecological and environmental issue that severely degrades terrestrial ecosystems. A range of soil and water conservation measures, notably the construction of check dams in gullies, have been widely implemented to mitigate soil erosion and sustain agricultural productivity. In this study, Ningxia province in China was selected as the study area. High-resolution Google Earth imagery and digital elevation model (DEM) data were integrated with three representative deep learning semantic segmentation models—FCN, U-Net, and DeepLab v3+—to achieve automatic extraction and spatial distribution analysis of engineered check dams. Model performance was quantified using overall accuracy (OA), F1-score, and mean intersection over union (mIoU), among other metrics. The results demonstrated that U-Net outperformed FCN and DeepLab v3+ across all evaluation metrics. On the test dataset, U-Net’s F1-score exceeded those of FCN and DeepLab v3+ by 3.89% and 7.08%, while mIoU increased by 2.17% and 6.57%, demonstrating superior boundary delineation. Based on the precise area extraction by U-Net, a piecewise empirical equation was subsequently developed to relate predicted silted land area to actual sediment volume, achieving R² values of 0.92 for small dams and 0.96 for large dams. Spatial distribution analysis revealed that check dams are predominantly concentrated in the southern mountainous and hilly-gully regions, moderately distributed in the central areas, and relatively sparse in the northern plains. Overall, this study demonstrates the feasibility and effectiveness of deep learning-based semantic segmentation for automated check dam mapping and sediment volume estimation. Full article

Lost circulation remains a major challenge during well construction, often leading to non-productive time, increased material consumption, and additional treatment costs. In field practice, the selection of lost circulation materials (LCMs) is still largely based on empirical rules or laboratory testing; however, these approaches are not always suitable for rapid decision-making under variable downhole conditions. This study presents a physics-guided surrogate modeling framework for predicting fracture sealing performance and supporting injection strategy selection. The approach combines laboratory observations with coupled computational fluid dynamics and discrete element method (CFD-DEM) simulations to represent both measured behavior and a broader range of mechanically consistent sealing scenarios. The final dataset included 300 cases, comprising 45 physical experiments and 255 CFD-DEM-generated synthetic cases. A hybrid machine learning architecture based on Temporal Convolutional Networks and Artificial Neural Networks was developed to predict sealing pressure under different material and fluid conditions. The model achieved an R² of 0.89 and a mean absolute percentage error of 6.4%, while showing 94% agreement with laboratory-based recommendations for injection strategy. The proposed framework can therefore serve as a rapid engineering support tool for preliminary formulation screening and a more computationally efficient digital workflow for fracture sealing design in drilling operations. Full article

Construction projects are characterized by fragmented project organizations, complex stakeholder coordination, and inefficient information communication, which often hinder collaborative project management and digital coordination efficiency in the architecture, engineering, and construction industry. Internet-based multi-stakeholder collaborative management technology (IMCMT) has emerged as an important digital solution for improving information sharing, real-time communication, and collaborative project management among project participants. However, its implementation is not optimistic because building professionals are reluctant to implement IMCMT. This study aims to identify the determinants affecting IMCMT implementation and investigate the practical roadways to promote building professionals’ IMCMT implementation. The data were collected from 323 valid building professionals in China through the questionnaire survey. The findings reveal that perceived usefulness and perceived ease of use influence building professionals’ IMCMT implementation through both direct and indirect mechanisms. The relation between building professionals’ behavioral intention and actual usage behavior is expanded by adding moderators of normative pressure and innovative culture. Moreover, practical roadways are explored by identifying the mediating role of technical values and behavioral intention and by adding the moderating role of organizational institutions. This study identifies practical roadmaps to promote building professionals’ IMCMT implementation by identifying its determinants and testing their relations. It also enriches the emerging construction information technology acceptance literature by identifying its boundary conditions and practical mediators in the architecture, engineering, and construction industry. Full article