Intelligent Infrastructure and Construction

This study aims to explore the transformative potential of Artificial Intelligence (AI) in enhancing cost planning and control within Qatar’s construction industry. By examining both opportunities and challenges associated with the adoption of AI, it seeks to uncover that AI can lead to significant improvements in accuracy in cost estimates and optimisation of various resources. The nation faces significant cost-overruns influenced by delays, shifting market conditions, and although AI has demonstrated its benefits in cost-control management globally, there is a lack of research on its practical applications in Qatar’s construction industry. Existing practical applications are more likely to experience errors due to them requiring manual labour and limited pattern recognition. Meanwhile, this study attempts to align AI-driven advancements with Qatar’s Vision 2030, which emphasises sustainable development and economic diversification. It adopts an analysis of semi-structured interviews with a group of experienced professionals from leading construction companies in Qatar, giving a comprehensive picture of the current landscape and future prospect for AI in the construction industry. The findings of this study reveal that AI technologies can significantly mitigate common issues in the construction industry, such as cost overruns, project delays, and resource wastage. On the other hand, this study identifies various obstacles that inhibit AI adoption, including high financial costs and insufficient training data. By weaving together theoretical understandings and practical experiences, it highlights the importance of integrating AI technologies within existing workflows while addressing key concerns. Full article

Tunnels serve as a critical hub in urban transportation networks; their monotonous and enclosed environment is prone to inducing speeding behavior, necessitating an efficient vehicle speed monitoring system. Traditional methods suffer from high costs and slow response times, making them inadequate for the complex scenarios encountered in tunnel environments. This study proposes a real-time tunnel vehicle speed monitoring system based on YOLOv8s and DeepSORT. YOLOv8s is used to detect and classify cars, trucks, and buses, while DeepSORT applies Kalman filtering and the Hungarian algorithm to construct motion trajectories. Vehicle speed is estimated through perspective geometric transformation combined with a sliding-window approach, with a speeding threshold of 100 km/h and corresponding visual alerts. Using surveillance video from an expressway tunnel as the dataset, the system achieved detection accuracies of 98% for cars, 96% for trucks, and 91% for buses. Speed detection performance metrics included an average speed deviation (ASD) of 2.54 km/h, a deviation degree of vehicle speed (DDVS) of 3.12, vehicle speed stability (VST) of 1.22, and speed difference ratio (SDR) of 2.9%. Analysis revealed a longitudinal “deceleration–acceleration–deceleration” inverted U-shaped speed profile along the tunnel. Statistical tests confirmed these findings: the Mann–Whitney U test showed highly significant differences in vehicle speeds between cars and trucks across different tunnel sections, and the Kruskal–Wallis test further indicated significant speed variations across the entrance, middle, and exit segments for both vehicle types. Full article

This study presents a nonlinear regression expansion model tailored to the characteristics of fissured highly expansive soils. Through in-depth investigations, fissure ratio (K_r), dry density (ρ_d), initial water content (w₀), and overburden stress (ln(1 + σ)) were identified as critical factors influencing expansion behavior. Experimental results revealed linear relationships between ultimate expansion (δ_ep) and w₀, ρ_d, and ln(1 + σ), and an exponential relationship with K_r. A multivariate nonlinear regression model was developed and validated, demonstrating high predictive accuracy. The model highlights the significant role of fissure infill materials, particularly gray-green clay, on soil expansiveness. It provides a reliable tool for predicting the expansion characteristics of fissured expansive soils under various conditions, offering theoretical and practical support for engineering applications in expansive soil regions. This study uses a single highly expansive clay from the Nanyang section. The soil is a transported Middle Pleistocene alluvial–proluvial clay (al-plQ2) in which fissures are predominantly filled by 2–5 mm gray-green clay. Accordingly, the proposed regression is most applicable to fissure systems that are largely infilled; extrapolation to open or partially infilled fissures should be made with caution. Full article

Given the construction industry’s significant contribution of approximately 39% of global CO₂ emissions, implementing effective carbon reduction strategies is becoming increasingly critical. In this context, Internet of Things (IoT) technologies present promising solutions for monitoring and reducing emissions. However, there is a lack of comprehensive understanding regarding specific IoT applications, implementation barriers, and opportunities for carbon reduction in construction practices. This study investigates the role of IoT in reducing carbon emissions in the construction industry. Following PRISMA guidelines, this study analyzed bibliometric data from Scopus and Web of Science databases using VOSviewer for science mapping visualization. Content analysis was conducted on 17 carefully selected articles to identify key research topics and applications. The analysis identified four mainstream application areas: (1) IoT-based smart monitoring systems for carbon emissions, (2) energy efficiency and management applications, (3) sustainable construction implementation frameworks, and (4) smart cities and other built environment applications. Key findings highlight growing research interest in IoT applications for sustainable construction, with China, the United States, and the United Kingdom leading collaborative efforts. Despite demonstrated carbon reduction potential, significant implementation barriers exist, including technical limitations, organizational resistance, skill gaps, and economic constraints. Key opportunities include Artificial Intelligence (AI) integration, Building information modeling (BIM)-IoT synergies, energy prosumer models, and standardization frameworks. This study provides the first focused review of IoT applications specifically targeting carbon reduction in construction, highlighting a critical technology-practice gap where organizational factors frequently outweigh technological barriers. A proposed socio-technical integration framework in this study bridges technical and organizational elements to overcome adoption barriers. Full article

Digitalization has become a driving force for significant advancements in property/facility management (PFM). It is necessary to identify the research gaps and future research directions, which could enable the effective development of digital technologies (DTs) in the context of PFM. This paper aims to review how DTs emerge to drive digitalization in PFM and identify gaps that need to be addressed in future research. The findings reveal that research on integrating BIM, IoT, AR, AI, and big data in sustainable transformations, real-time data, and energy optimization is limited, with challenges in data security, privacy, and system interoperability. Future research should focus on BIM for sustainability, real-time data, and AR applications, alongside IoT and blockchain integration for security. Investigating VR in maintenance, AI for energy optimization, improved prediction accuracy, and enhanced NLP for chatbots are also critical areas for exploration. This state-of-the-art review summarized the gaps from the existing literature of property management digitalization and provides an update on research gaps and directions for the digitalization in PFM. Full article

Understanding the multi-crack coupling fracture behavior in brittle materials is particularly critical for aging dam infrastructure, where 78% of structural failures originate from crack network coalescence. In this study, we introduce the concepts of crack distance ratio (DR) and size ratio (SR) to describe the relationship between crack position and length and employ the discrete element method (DEM) for extensive numerical simulations. Specifically, a crack density function is introduced to assess microscale damage evolution, and the study systematically examines the macroscopic mechanical properties, failure modes, and microscale damage evolution of rock-like materials under varying DR and SR conditions. The results show that increasing the crack distance ratio and crack angle can inhibit the crack formation at the same tip of the prefabricated crack. The increase in the size ratio will promote the formation of prefabricated cracks on the same side. The increase in the distance ratio and size ratio significantly accelerate the rapid increase in crack density in the second stage. The crack angle provides the opposite effect. In the middle stage of loading, the growth rate of crack density decreases with the increase in crack angle. Overall, the size ratio has a greater influence on the evolution of microscopic damage. This research provides new insights into understanding and predicting the behavior of materials under complex stress conditions, thus contributing to the optimization of structural design and the improvement of engineering safety. Full article

The construction industry is moving towards the era of industry 4.0; 5.0 with Building Information Modelling (BIM) as the tool gaining significant traction owing to its inherent advantages such as enhancing construction design, process and data management. However, the integration of BIM presents risks that are often overlooked in project implementation. This study aims to develop a novel amalgamated dimensional factor (Techno-organizational Aspect) that is set out to identify and align appropriate management strategies to these risks. Firstly, it encompasses an in-depth analysis of BIM and risk management, through an integrative review approach. The study utilizes an exploratory-based review centered around journal articles and conference papers sourced from Scopus and Google Scholar. Then processed using NVivo 12 Pro software to categorise risks through thematic analysis, resulting in a comprehensive Risk Breakdown Structure (RBS). Then qualitative content analysis was employed to identify and develop management strategies. Further data collection via online survey was crucial for closing the research gap identified. The analysis by mixed method research enabled to determine the risk severity via the quantitative approach using SPSS (version 29), while the qualitative approach linked management strategies to the risk factors. The findings accentuate the crucial linkages of key strategies such as version control system that controls BIM data repository transactions to mitigate challenges controlling transactions in multi-model collaborative environment. The study extends into underexplored amalgamated domains (techno-organisational spectrum). Therefore, a significant contribution to bridging the existing research gap in understanding the intricate relationship between BIM implementation risks and effective management strategies. Full article

Fissured expansive soils exhibit pronounced moisture-induced swelling, posing significant risks to the stability of geotechnical structures such as dam foundations and core zones. To improve predictive capacity in such environments, this study developed a back-propagation (BP) neural network model to estimate the swelling behavior of fissured expansive soils. The model incorporated four key geotechnical parameters—fissure ratio, dry density, initial moisture content, and overburden pressure—and was implemented in MATLAB using a three-layer feedforward architecture with four inputs, five hidden neurons, and a single output neuron to predict the swelling ratio (increase in specimen height due to water-induced expansion). The model was trained on 81 laboratory-tested samples, with all variables normalized to the range [−1, 1] to ensure numerical stability. Two training algorithms were evaluated: gradient descent with momentum (traingdm) and the Fletcher–Reeves conjugate gradient method (traincgf). The optimal network configuration achieved a mean squared error (MSE) below 0.01, indicating strong predictive accuracy for expansive soil swelling behavior. Comparative results showed that the conjugate gradient algorithm converged nearly 30 times faster than the gradient descent method, while maintaining similar prediction accuracy. Validation on an independent dataset confirmed high agreement with measured swelling ratios. The proposed BP model demonstrates robust generalization and computational efficiency, offering a practical decision-support tool for expansive soil deformation control in dam engineering. Its rapid and accurate predictions make it valuable for Smart City applications such as embankment stabilization, intelligent dam core design, and real-time geotechnical risk assessment. Full article

The nexus between sustainability and safety in construction is crucial for improving a resilient and responsible built environment. By adhering to sustainable principles, construction practices can not only mitigate the environmental impact but also prioritize the health and safety of workers and communities. To prevent accidents and enhance safety in construction, unmanned aerial vehicles (UAVs) are utilized for aerial inspection, site monitoring, surveying, emergency response, and training purposes. However, no systematic review has yet identified UAV deployment’s adoption, challenges, and prospects. UAVs have emerged as promising technologies for improving safety through applications such as aerial inspections, site monitoring, surveying, emergency response, and training. However, a comprehensive review of UAV adoption, challenges, and prospects in the construction industry is still lacking. To address this gap, this study conducts a systematic literature review and bibliometric analysis to examine the current state of UAV implementation in construction safety management. The analysis reveals the interconnectedness between construction, engineering disciplines, and safety management, providing a holistic overview of influential contributors and prevalent themes. Content analysis further uncovers significant barriers hindering widespread UAV implementation, emphasizing technical, regulatory, and safety concerns. This study highlights strategies for overcoming these challenges and optimizing UAV deployment to enhance safety in construction, aligning with broader principles of social sustainability. Full article

Thermal stress control is crucial for massive concrete structures during construction. The cooling strategies directly determine the safety of structures, material quality, construction efficiency, and project cost. However, precise spatiotemporal thermal stress regulation and management are difficult to achieve due to the lack of balanced discriminant criteria and multi-objective optimization methods for the selection of traditional strategies. Therefore, an intelligent optimization method for thermal stress management strategy in massive concrete structures, considering the balance of safety, quality, efficiency, and cost (SEQC-TSOM), is proposed. Initially, a Thermal Stress Simulation Mechanism Model (TSSM) is constructed to accurately evaluate the structural state throughout the entire process. Subsequently, a mechanism data-driven surrogate model (MD-SM) is constructed to quickly evaluate the structural response under different cooling strategies. Furthermore, a multi-objective intelligent optimization model and a multi-criteria decision-making model are proposed to filter the intelligent optimal strategy from the Pareto solution set. Finally, a case study based on the Baihetan arch dam project is conducted, and the results show that the safety, quality, efficiency, and cost (SEQC)-balanced strategy increases safety by 42%, improves cooling efficiency by 36%, and reduces cooling costs by 20.6% compared with traditional strategies. Full article