Search Results (163)

This study conducts a comprehensive systematic review of the economic impact of Digital Twin (DT) technology within the Architecture, Engineering, and Construction (AEC) industry, following the PRISMA methodology. While DT adoption has been accelerated by advancements in Building Information Modelling (BIM), the Internet of Things (IoT), and data analytics, significant challenges persist—most notably, high initial investment costs and integration complexities. Synthesising the literature from 2016 onwards, this review identifies sector-specific barriers, regulatory burdens, and a lack of standardisation as key factors constituting DT implementation costs. Despite these hurdles, DTs demonstrate strong potential for enhancing construction productivity, optimising lifecycle asset management, and enabling predictive maintenance, ultimately reducing operational expenditures and improving long-term financial performance. Case studies reveal cost efficiencies achieved through DTs in modular construction, energy optimisation, and infrastructure management. However, limited financial resources and digital skills continue to constrain the uptake across the sector, with various extents of impact. This paper calls for the development of unified standards, innovative public–private funding mechanisms, and strategic collaborations to unlock and utilise DTs’ full economic value. It also recommends that future research explore theoretical frameworks addressing governance, data infrastructure, and digital equity—particularly through conceptualising DT-related data as public assets or collective goods in the context of smart cities and networked infrastructure systems. Full article

Accurate extraction of table borders in scanned road engineering drawings is crucial for the digital transformation of engineering archives, which is an essential step in the development of intelligent infrastructure systems. However, challenges such as degraded borders, image blur, and character adjoining often hinder the precise delineation of table structures, making automated parsing difficult. Existing solutions, including traditional OCR tools and deep learning methods, struggle to consistently delineate table borders in the presence of these visual distortions and fail to perform well without extensive annotated datasets, which limits their effectiveness in real-world applications. We propose TableBorderNet, a semantic segmentation framework designed for precise border extraction under complex visual conditions. The framework captures structural context by guiding convolutional feature extraction along explicit row and column directions, enabling more accurate delineation of table borders. To ensure topological consistency in complex or degraded inputs, a topology-aware loss function is introduced, which explicitly penalizes structural discontinuities during training. Additionally, a generative self-supervised strategy simulates common degradation patterns, allowing the model to achieve strong performance with minimal reliance on manually annotated data. Experiments demonstrate that the method achieves an Intersection-over-Union of 94.2% and a topological error of 1.07%, outperforming existing approaches. These results underscore its practicality and scalability for accelerating the digitization of engineering drawings in support of data-driven road asset management. Full article

The management of municipal solid waste remains a critical environmental and energy challenge across the European Union (EU), where a significant portion of waste still ends up in landfills, generating landfill gas (LFG) rich in methane and harmful impurities. In Latvia, despite national strategies to enhance circularity, untreated LFG is underutilized due to inadequate purification infrastructure, particularly in meeting biomethane standards. This study addressed this gap by proposing and evaluating an innovative, multistep LFG purification system tailored to Latvian conditions, with the aim of enabling the broader use of LFG for energy cogeneration and potentially biomethane injection. The research objective was to design, describe, and preliminarily assess a pilot-scale LFG purification prototype suitable for deployment at Latvia’s largest landfill facility—Landfill A. The methodological approach combined chemical composition analysis of LFG, technical site assessments, and engineering modelling of a five-step purification system, including desulfurization, cooling and moisture removal, siloxane filtration, pumping stabilization, and activated carbon treatment. The system was designed for a nominal gas flow rate of 1500 m³/h and developed with modular scalability in mind. The results showed that raw LFG from Landfill A contains high concentrations of hydrogen sulfide, siloxanes, and volatile organic compounds (VOCs), far exceeding permissible thresholds for biomethane applications. The designed prototype demonstrated the technical feasibility of reducing hydrogen sulfide (H₂S) concentrations to <7 mg/m³ and siloxanes to ≤0.3 mg/m³, thus aligning the purified gas with EU biomethane quality requirements. Infrastructure assessments confirmed that existing electricity, water, and sewage capacities at Landfill A are sufficient to support the system’s operation. The implications of this research suggest that properly engineered LFG purification systems can transform landfills from passive waste sinks into active energy resources, aligning with the EU Green Deal goals and enhancing local energy resilience. It is recommended that further validation be carried out through long-term pilot operation, economic analysis of gas recovery profitability, and adaptation of the system for integration with national gas grids. The prototype provides a transferable model for other Baltic and Eastern European contexts, where LFG remains an underexploited asset for sustainable energy transitions. Full article

Machine Learning (ML) is increasingly applied across various domains, addressing tasks such as predictive analytics, anomaly detection, and decision-making. Many of these applications share similar underlying tasks, offering potential for systematic reuse. However, existing reuse in ML is often fragmented, small-scale, and ad hoc, focusing on isolated components such as pretrained models or datasets without a cohesive framework. Product Line Engineering (PLE) is a well-established approach for achieving large-scale systematic reuse in traditional engineering. It enables efficient management of core assets like requirements, models, and code across product families. However, traditional PLE is not designed to accommodate ML-specific assets—such as datasets, feature pipelines, and hyperparameters—and is not aligned with the iterative, data-driven workflows of ML systems. To address this gap, we propose Machine Learning Product Line Engineering (ML PLE), a framework that adapts PLE principles for ML systems. In contrast to conventional ML reuse methods such as transfer learning or fine-tuning, our framework introduces a systematic, variability-aware reuse approach that spans the entire lifecycle of ML development, including datasets, pipelines, models, and configuration assets. The proposed framework introduces the key requirements for ML PLE and the lifecycle process tailored to machine-learning-intensive systems. We illustrate the approach using an industrial case study in the context of space systems, where ML PLE is applied for data analytics of satellite missions. Full article

There has been growing interest among investors to include cryptocurrencies in their portfolios because of their diversification potential. However, the diversification role of cryptocurrencies when added to South African bank equities is yet to be determined. This study rigorously evaluates asset co-movement and diversification benefits of integrating cryptocurrencies into South African bank equity portfolios. Using advanced financial engineering techniques, including multi-asset particle swarm optimizer (MA-PSO), random optimizer, and a static equal-weighted portfolio (EWP) model, this study analyzed the dynamic portfolio performance and diversification of cryptocurrencies in the 2017–2024 period. The portfolio performance of the three methods is also compared with the results from the traditional one-period mean–variance optimization (MVO) method. The findings underscore the superiority of dynamic models over static EWP in assessing the impact of cryptocurrency inclusion in bank equity portfolios. While pre-COVID-19 studies identified cryptocurrencies as effective hedges against market downturns, this protective role appears attenuated in the post-COVID-19 era. The dynamic MA-PSO model emerges as the optimal approach, delivering better-diversified portfolios. Consequently, South African portfolio managers must carefully evaluate investor risk tolerance before incorporating cryptocurrencies, with regulators imposing stringent guidelines to mitigate potential losses. Full article

The convergence of blockchain and metaverse technologies is poised to redefine how Global Value Chains (GVCs) create, capture, and distribute value, yet scholarly insight into their joint impact remains scattered. Addressing this gap, the present study aims to clarify where, how, and under what conditions blockchain-enabled transparency and metaverse-enabled immersion enhance GVC performance. A systematic literature review (SLR), conducted according to PRISMA 2020 guidelines, screened 300 articles from ABI Global, Business Source Premier, and Web of Science records, yielding 65 peer-reviewed articles for in-depth analysis. The corpus was coded thematically and mapped against three theoretical lenses: transaction cost theory, resource-based view, and network/ecosystem perspectives. Key findings reveal the following: 1. digital twins anchored in immersive platforms reduce planning cycles by up to 30% and enable real-time, cross-border supply chain reconfiguration; 2. tokenized assets, micro-transactions, and decentralized finance (DeFi) are spawning new revenue models but simultaneously shift tax triggers and compliance burdens; 3. cross-chain protocols are critical for scalable trust, yet regulatory fragmentation—exemplified by divergent EU, U.S., and APAC rules—creates non-trivial coordination costs; and 4. traditional IB theories require extension to account for digital-capability orchestration, emerging cost centers (licensing, reserve backing, data audits), and metaverse-driven network effects. Based on these insights, this study recommends that managers adopt phased licensing and geo-aware tax engines, embed region-specific compliance flags in smart-contract metadata, and pilot digital-twin initiatives in sandbox-friendly jurisdictions. Policymakers are urged to accelerate work on interoperability and reporting standards to prevent systemic bottlenecks. Finally, researchers should pursue multi-case and longitudinal studies measuring the financial and ESG outcomes of integrated blockchain–metaverse deployments. By synthesizing disparate streams and articulating a forward agenda, this review provides a conceptual bridge for international business scholarship and a practical roadmap for firms navigating the next wave of digital GVC transformation. Full article

Bridges are assets in every society, and their deterioration can have severe economic, social, and environmental consequences. Therefore, implementing effective asset management strategies is crucial to ensure bridge infrastructure’s long-term performance and safety. Roadmaps can serve as valuable tools for bridge asset managers, helping bridge engineers make informed decisions that enhance bridge safety while maintaining controlled life cycle costs. Although some bridge asset management roadmaps exist, such as the one published by the United States Federal Highway Administration (FHWA), there is a lack of structured research roadmaps that are both region-specific and adaptable as guiding frameworks for similar studies. For instance, the FHWA roadmap cannot be universally applied across diverse regional contexts. This study addresses this critical gap by developing a research roadmap tailored to Idaho, USA. The roadmap was developed using a three-phase methodological approach: (1) a comprehensive analysis of past and ongoing Department of Transportation (DOT)-funded research projects over the last five years, (2) a nationwide survey of DOT funding and research practices, and (3) a detailed assessment of Idaho Transportation Department (ITD) deficiently rated bridge inventory, including individual element condition states. In the first phase, three filtering stages were implemented to identify the top 25 state projects. A literature review was conducted for each project to provide ITD’s Technical Advisory Committee (TAC) members with insights into research undertaken by various state DOTs. Moreover, in the second phase, approximately six questionnaires were designed and distributed to other state DOTs. These questionnaires primarily covered topics related to bridge research priorities and funding allocation. In the final phase, a condition state analysis was conducted using data-driven methods. Key findings from this three-phase methodological approach highlight that ultra-high-performance concrete (UHPC), bridge deck preservation, and maintenance strategies are high-priority research areas across many DOTs. Furthermore, according to the DOT responses, funding is most commonly allocated to projects related to superstructure and deck elements. Finally, ITD found that the most deficient elements in Idaho bridges are reinforced concrete abutments, reinforced concrete pile caps and footings, reinforced concrete pier walls, and movable bearing systems. These findings were integrated with insights from ITD’s TAC to generate a prioritized list of 23 high-impact research topics aligned with Idaho’s specific needs and priorities. From this list, the top six topics were selected for further investigation. By adopting this strategic approach, ITD aims to enhance the efficiency and effectiveness of its bridge-related research efforts, ultimately contributing to safer and more resilient transportation infrastructure. This paper could be a helpful resource for other DOTs seeking a systematic approach to addressing their bridge research needs. Full article

Effective asset management in the architecture, engineering, and construction/facilities management (AEC/FM) industry is crucial for improving asset performance and lifespan, as well as reducing downtimes and maintenance costs. Current asset management practices mostly rely on outdated paper-based approaches that are prone to data loss, security attacks, and missing information. Emerging technologies, such as digital twins, are being proposed to solve existing asset management problems in the AEC industry. However, the industry perspective is often missing in the evaluation of such technology-led approaches regarding actual applications and implementation challenges. This study seeks to understand the potential of digital twins in solving current asset management issues and challenges within the United Arab Emirates (UAE) context. To achieve this aim, structured interviews were conducted with 14 industry experts to capture their understanding of current digital technologies and existing issues in asset management. The findings of this study underscore the transformative potential of digital twins as a tool for optimizing asset performance and decision-making throughout the asset lifecycle. Full article

The gradual deterioration of underground water infrastructure requires constant condition monitoring to prevent catastrophic failures, reduce leaks, and avoid costly unexpected repairs. However, given the large scale and tight budgets of water utilities, it is essential to implement strategies for optimal selection and deployment of monitoring technologies. This article introduces a unified framework and methods for optimally selecting condition monitoring technologies while locating their deployment at the most vulnerable pipe segments. The approach is underpinned by an R-E-R-A-V (Redundant, Established, Reliable, Accurate, and Viable) principle and asset management concepts. The proposed method is supported by a thorough review of assessment and monitoring technologies, as well as common sensor placement approaches. The approach selects optimal technology using a combination of technology readiness levels and SFAHP (Spherical Fuzzy Analytic Hierarchy Process). Optimal placement is achieved with a k-Nearest Neighbors (kNN) model tuned with minimal topological and physical pipeline system features. Feature engineering is performed with OPTICS (Ordering Points to Identify the Clustering Structure) by evaluating the pipe segment vulnerability to failure-prone areas. Both the optimal technology selection and placement methods are integrated through a proposed algorithm. The optimal placement of monitoring technology is demonstrated through a modified benchmark network (Net3). The results reveal an accurate model with robust performance and a harmonic mean of precision and recall of approximately 65%. The model effectively identifies pipe segments requiring monitoring to prevent failures over a period of 11 years. The benefits and areas of future exploratory research are explained to encourage improvements and additional applications. Full article

With the evolution of data collection technologies, sensor-generated data have become the norm. However, decades of manually recorded maintenance data still hold untapped value. Natural Language Processing (NLP) offers new ways to extract insights from these historical records, especially from short, unstructured maintenance texts often accompanying structured database fields. While NLP has shown promise in this area, technical texts pose unique challenges, particularly in preprocessing and manual annotation. This study proposes a novel methodology combining Failure Mode and Effect Analysis (FMEA), a reliability engineering tool, into the NLP pipeline to enhance Named Entity Recognition (NER) in maintenance records. By leveraging the structured and domain-specific knowledge encapsulated in FMEAs, the annotation process becomes more systematic, reducing the need for exhaustive manual effort. A case study using real-world data from a major electrical utility demonstrates the effectiveness of this approach. The custom NER model, trained using FMEA-informed annotations, achieves high precision, recall, and F1 scores, successfully identifying key reliability elements in maintenance text. The integration of FMEA not only improves data quality but also supports more informed asset management decisions. This research introduces a novel cross-disciplinary framework combining reliability engineering and NLP. It highlights how domain expertise can be used to streamline annotation, improve model accuracy, and unlock actionable insights from legacy maintenance data. Full article

Preserving historical railway assets presents a complex systems challenge, in which uncertainties in material performance, structural degradation, and regulatory requirements directly impact long-term reliability and operational continuity. Traditional maintenance practices often limit the use of modern materials, introducing inefficiencies, increased lifecycle costs, and higher failure risk due to material ageing and environmental exposure. This study proposes a reliability-informed preservation framework that supports the integration of contemporary materials into historical railway infrastructure while accounting for legal, material, and procedural uncertainties. The framework is validated through two industrial case studies, each reflecting different regulatory and operational constraints. The first case demonstrates the successful substitution of timber with certified PVC cladding on a non-listed signal box, achieving improved durability, reduced maintenance intervals, and enhanced system reliability. The second case explores an unsuccessful attempt to replace decayed timber gables with aluminium, in which late-stage planning misalignment, underestimated risks, and uncertainty in approval outcomes led to a significant cost increase and reduced reliability regarding delivery. By systematically applying and evaluating the framework under real-world conditions, this research contributes to engineering asset management by introducing a structured method for mitigating regulatory and material uncertainties. Full article

This advanced exploration of integrating cross-modal Artificial-Intelligence-Generated Content (AIGC) within the Unity3D game engine seeks to elevate the diversity and coherence of image generation in game art creation. The theoretical framework proposed dives into the seamless incorporation of generated visuals within Unity3D, introducing a novel Generative Adversarial Network (GAN) structure. In this architecture, both the Generator and Discriminator embrace a Transformer model, adeptly managing sequential data and long-range dependencies. Furthermore, the introduction of a cross-modal attention module enables the dynamic calculation of attention weights between text descriptors and generated imagery, allowing for real-time modulation of modal inputs, ultimately refining the quality and variety of generated visuals. The experimental results show outstanding performance on technical benchmarks, with an inception score reaching 8.95 and a Frechet Inception Distance plummeting to 20.1, signifying exceptional diversity and image quality. Surveys reveal that users rated the model’s output highly, citing both its adherence to text prompts and its strong visual allure. Moreover, the model demonstrates impressive stylistic variety, producing imagery with intricate and varied aesthetics. Though training demands are extended, the payoff in quality and diversity holds substantial practical value. This method exhibits substantial transformative potential in Unity3D development, simultaneously improving development efficiency and optimizing the visual fidelity of game assets. Full article

An efficient and robust solution for 3D indoor reconstruction is crucial for various managerial operations in the Architecture, Engineering, and Construction (AEC) sector, such as indoor asset tracking and facility management. Conventional approaches, primarily relying on SLAM and deep learning techniques, face certain limitations. With the recent emergence of radiance field (RF)-inspired methods, such as Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS), it is worthwhile to evaluate their capability and applicability for reconstructing built environments in the AEC domain. This paper aims to compare different RF-inspired methods with conventional SLAM-based methods and to assess their potential use for asset management and related downstream tasks in indoor environments. Experiments were conducted in university and laboratory settings, focusing on 3D indoor reconstruction and semantic asset segmentation. The results indicate that 3DGS and Nerfacto generally outperform other NeRF-based methods. In addition, this study provides guidance on selecting appropriate reconstruction approaches for specific use cases. Full article

Historic Building Information Modelling (HBIM) is the application of BIM, a digital information management and 3D modelling technique, to cultural heritage (CH) assets. It will assist with the ongoing sustainable management of CH in line with the United Nations’ Sustainable Development Goals (i.e., SDG 11 and SDG 13) by providing an enduring record of asset information and enabling the energy-efficient use and adaption of assets. However, the application of HBIM is currently limited by a lack of defined end-user requirements and standard methodology in its application. This paper is the second piece in a series of works where the authors adopted a systems thinking approach, utilising both the Soft Systems Methodology (SSM) and hard systems engineering (SE), for the development of HBIM. This paper presents the results of an extensive survey undertaken with the UK Heritage Community. It validates forty-one previously proposed information requirements, identifies a further twenty new information requirements for HBIM, and utilises the SE process to define thirty-three system requirements for HBIM according to the end user. Full article

Predictive maintenance of built assets often relies on scheduled routine practices that are disconnected from real-time stress assessment, degradation and defects. However, while Digital Twin (DT) technology within building and urban studies is maturing rapidly, its use in predictive maintenance is limited. Traditional preventive and reactive maintenance strategies that are more prevalent in facility management are not intuitive, not resource efficient, cannot prevent failure and either underserve the asset or are surplus to requirements. City Information Modeling (CIM) refers to a federation of BIM models in accordance with real-world geospatial references, and it can be deployed as an Urban Digital Twin (UDT) at city level, like BIM’s deployment at building level. This study presents a systematic review of 105 Scopus-indexed papers to establish current trends, gaps and opportunities for a cognitive predictive maintenance framework in the architecture, engineering, construction and operations (AECO) industry. A UDT framework consisting of the CIM of a section of the University of Florida campus is proposed to bridge the knowledge gap highlighted in the systematic review. The framework illustrates the potential for CNN-IoT integration to improve predictive maintenance through advance notifications. It also eliminates the use of centralized information archiving. Full article