Understanding the spatial distribution of water infrastructure heritage is crucial for its conservation in large regions. This study analyses 147 water infrastructure heritage sites in the Shandong section of the Yellow River Basin using an integrated nearest neighbour distance index (NNDI)-kernel density estimation (KDE)-GeoDetector framework. Spatial techniques, including NNDI and KDE, reveal that sites display a statistically significant clustered pattern described as 'large aggregation, small dispersion', forming a spatial layout of 'three cores and one line'. GeoDetector analysis quantitatively shows that anthropogenic engineering factors, especially water infrastructure systems (q = 0.92), are the primary determinants of distribution, while natural geographical elements serve as secondary constraints. This integrated framework outperforms traditional single-method analyses, offering a robust tool for conservation planning, sustainable tourism, and heritage management in complex river basins. The methodology also provides a transferable model for analysing water infrastructure heritage in other major river systems globally.
This study focuses on the optimisation of the urban waterlogging disaster warning system. In view of the limitations of traditional warning systems in complex urban environments, deep reinforcement learning () technology is introduced. The proposed DRL-EWS model integrates a spatiotemporal graph convolutional network for spatial feature extraction, a gated recurrent unit for temporal sequence modelling, and a policy gradient-based decision module for adaptive warning generation. By innovatively combining these components, the risk of urban waterlogging is accurately predicted. The results show that the waterlogging disaster warning system (DRL-EWS) based on is superior to traditional and other machine learning warning models in terms of warning accuracy, false alarm rate, and false alarm rate, and has good adaptability to different terrains, pipe network density, rainfall patterns, and other conditions. This study not only enriches the waterlogging warning technology system in theory, but its practical results are expected to enhance the urban waterlogging defence capabilities and provide a strong guarantee for urban safety. At the same time, it also points out the direction for further improvement in subsequent research.
This study assesses the asset management (AM) maturity, implementation challenges, and capacity-building needs of local governments in New Brunswick, Canada, following updated provincial AM reporting requirements introduced alongside the 2023 Local Governance Reform. Using a mixed-methods approach, data were collected through a province-wide survey of municipalities (53% response rate) and eight follow-up interviews with municipal staff. The findings indicate that while AM is widely endorsed in principle, most municipalities, particularly smaller and rural governments, remain at early stages of AM maturity and report limited organisational capacity to meet evolving provincial expectations. Key barriers include weak integration between AM plans and capital budgeting, low AM awareness among elected officials, fragmented guidance, and constraints in financial and human resources. Municipal respondents identified a strong need for practical and scalable support mechanisms, including standardised core asset management plan components, targeted funding for external expertise, regional shared-service models, and short-format, risk- and climate-informed training. Based on these findings, the paper advances evidence-based recommendations aimed at strengthening provincial AM support structures and improving the conditions for effective AM implementation across municipalities. The results underscore the importance of aligning diagnostic assessments of AM capacity with proportionate, context-sensitive policy interventions to advance sustainable municipal infrastructure management.
This paper presents a reliability-based case study for wastewater lift station asset management using two modelling approaches applied within a unified analytical framework: the Weibull-based Integrated Asset Management System and a discrete Markovian deterioration model. The analysis applies to 134 lift stations in the City of Sugar Land, Texas, covering pumps, controls, SCADA, and related components. The Weibull model estimates risk from age-based probabilities of failure, while the Markov model represents transitions among Good, Fair, Poor, and Failed condition states derived from age-to-state discretisation using expected useful life fractions. Both models were calibrated using the same dataset and evaluated over a ten-year period under three funding scenarios: do-nothing, US$2.5 million per year, and US$5 million per year. The Markov model produced smoother deterioration trends, lower long-term system risk, and higher efficiency stability than the Weibull process. Sensitivity testing confirmed model robustness across repair times and deterioration rates. The results show how comparing age-based and condition-state modelling approaches within a single framework can inform risk forecasting and investment planning decisions for municipal utilities with limited condition monitoring data.
Groundwater is an important resource for domestic consumption, industrial and agricultural use. Overexploitation of groundwater, unpredictable rainfall and severe climate change have imposed a pressure on global groundwater resources. As demand for potable water is increasing, there is a need for evaluating and mapping groundwater potential. In Shimla district, people are dependent on the groundwater for household/agriculture purpose. Geospatial-based studies have gained importance in mapping of groundwater potential zones. This study has been undertaken to create the groundwater potential zone map of Shimla district. The analytical hierarchy process (AHP) was employed to delineate groundwater potential zones. Seven thematic layers were analysed using ArcGIS. Pairwise comparison matrix is formed for these layers and are analysed using weighted overlay analysis tool in ArcGIS. The results are presented as a groundwater potential map with five classes: very low, low, moderate, high and very high. Validation was performed using water yield data through the area under curve method. The AHP is widely recognised as an effective method for groundwater potential mapping and monitoring. The results show that 39% of the district area falls under high groundwater potential, while 47% has moderate potential. These findings can support effective groundwater planning and policy formulation.
Accurate estimation of an asset's lifetime is essential for making informed decisions about maintenance, renewal, or disposal. This study presents the findings of a survey to understand how professionals evaluate the useful technical and economic life of services when planning projects and renewing assets. In addition to the survey, semi-structured interviews were conducted to gain deeper qualitative insights into experts' reasoning, challenges, and decision-making processes. The survey targeted individuals from diverse professional backgrounds and experience levels to capture a wide range of perspectives on the prioritisation of asset lifetime considerations, particularly useful technical and economic (UEL) life. Insights from the interviews complemented the survey results by revealing contextual factors that influence how asset lifetime is assessed in practice by experts. Results indicate that, while technical factors are predominantly considered, economic factors, especially the UEL, are often overlooked. The paper offers important insights into current practices and challenges faced by industry professionals in managing assets, emphasising the need to consider both technical and economic aspects when making maintenance and renovation decisions.
The existing collaborative design platform fails to maintain the coordination efficiency among different design disciplines, and there are problems such as data integration conflicts and insufficient coordination and communication. In response to this, a collaborative design platform combining building information modelling with geographic information system (GIS) was studied and constructed. In the construction process of the collaborative design platform, a multi-head self-attention mechanism is adopted for multi-parameter collaborative processing. The experimental results show that the hybrid data integration method achieves integration delay control within 8 min without manual intervention, while the integration delay control of the three multi-source heterogeneous time series, iterative fuzzy clustering, and line-based integration methods exceeds 12, 20, and 25 min, respectively. When assessing the risk factors of decision making, the risk probability rating is 0.3, and the consequence rating after the risk occurs is 0.2. In the construction schedule scheduling experiment, when the construction unit was (2,3), the actual completion time was 30 days, which was less than the recommended time. The collaborative design platform proposed in the research can improve the construction efficiency. This research is conducive to enhancing the efficiency of future collaborative management modules and promoting real-time management and design.
This study examines how artificial intelligence (AI)-enabled cost management systems influence cost efficiency and sustainability performance in large-scale construction and property development projects across the Gulf Co-operation Council (GCC). It also explores how national regulatory environments moderate these relationships, providing managerial insights for sustainable decision making and digital governance. A longitudinal panel dataset of engineering and infrastructure projects from the United Arab Emirates, Saudi Arabia, and Qatar covering 2015–2024 is analysed using fixed-effects, difference-in-differences, and generalised method of moments estimations. Projects integrating AI-based cost management systems achieve significantly higher cost efficiency, environmental sustainability, and overall triple-bottom-line performance than those relying on traditional approaches. Stronger regulatory and governance frameworks amplify these benefits, demonstrating that institutional quality enhances the sustainability impact of digital transformation. AI-driven cost systems provide developers, financiers, and policymakers with tools to improve transparency, mitigate budget risks, and align project management with environmental, social, and governance goals. The study contributes to sustainable management and information systems literature by empirically linking AI-enabled cost accounting with measurable financial and environmental outcomes in GCC infrastructure projects.
Infrastructure asset life cycle resilience (LR) is increasingly critical in emerging economies, where weak financial management can undermine long-term asset performance. Although computerised accounting information systems (CAIS) are widely adopted, evidence on how they support LR in infrastructure is limited. This study examines whether CAIS improves financial insights (FI), whether FI strengthens LR, and whether organisational culture (OC) conditions the CAIS-LR relationship. A survey of 310 infrastructure professionals in Iraq was analysed using partial least squares structural equation modelling. Results show that CAIS positively influences FI, and FI positively influences LR. The direct path from CAIS to LR is not significant, indicating that FI is the primary mechanism through which CAIS contributes to LR; mediation testing confirms a partial indirect effect via FI. The CAIS-LR link is also moderated by OC, with a stronger OC weakening the incremental effect of CAIS on LR. The study clarifies how CAIS translates into LR-oriented outcomes through improved FI and highlights the importance of OC when leveraging CAIS for infrastructure asset management.
In response to slow information updates, low data consistency, and long system response times in the operation and maintenance of super large bridges, this study proposes a CATIA-based bridge modelling and management system. The system adopts a bottom-up design framework and integrates parametric family files, automated assembly, and CATIA-Excel-Python linkage to support rapid model creation and real-time defect updates. Performance tests show that the system achieves superior efficiency compared with existing platforms, with a modelling speed of 1.5 models per hour, an information update delay of 5 min, data consistency of 98.7%, and a system response time of 0.5 s. A six-year engineering application on a major bridge in Shanxi Province further verifies the system's advantages: operation and maintenance costs rose moderately from 1.5 to 2.3 million yuan, representing nearly 40% savings compared with conventional systems whose costs increased from 2.4 to 4.1 million yuan. In addition, the proposed system achieved higher scores in safety, cost-effectiveness, and robustness (85, 78, and 82 out of 100, respectively). Overall, the CATIA-based system effectively enhances data processing efficiency, reduces long-term maintenance costs, improves structural safety, and offers a practical and intelligent solution for full-lifecycle management of super large bridges.
This study aims to identify bicycle-sharing systems as a means of active transportation in Colombia. A scoping review was conducted following the Joanna Briggs Institute guidelines and PRISMA-ScR reporting standards. A systematic search in PubMed, Scopus, Web of Science, Bireme, EBSCO, and Transport Research International Documentation was completed until 10 December 2024. Record selection in Rayyan followed a two-stage blinded review by two independent reviewers. Data extraction followed the same approach, and results were synthesised descriptively. Of 1492 records identified, 20 met the inclusion criteria: articles (n = 5), theses (n = 3), reports (n = 2), websites (n = 8), a user manual, and press release. Evidence indicates environmental and health benefits associated with use. Local evaluations estimated carbon dioxide reductions of thousands of tonnes and increases in user physical activity (mean 152 min/week). However, persistent challenges included marked gender disparities in use (male-dominant proportions up to 79%-82%), theft and security incidents, road safety concerns, and a lack of longitudinal, rigorous impact evaluations limiting assessment of medium- and long-term effects. Bicycle-sharing systems promote sustainable and healthy mobility in Colombia. However, road safety and gender equity challenges persist, highlighting the need for infrastructure improvements, impact assessments, and awareness campaigns.
This study investigates both the symmetric and asymmetric relationships between infrastructure development and economic growth in India, using annual data spanning from 1991 to 2022. To measure infrastructure, an index of infrastructure development is created through the principal component method. To assess the potential linear and non-linear impact of infrastructure development on economic growth, both linear and non-linear autoregressive distributed lag (ARDL) models are employed. The ARDL results indicate that infrastructure development promotes economic growth both in the short and long run. The non-linear ARDL model confirms the existence of asymmetric effects, showing that positive shocks to infrastructure promote economic growth, whereas negative shocks impede growth. The negative shock has a stronger adverse impact on growth compared with the positive impact generated by the expansion in infrastructure. The Wald test further supported the presence of an asymmetric relationship between infrastructure development and economic growth in the long run. These findings highlight that infrastructure development is a key policy instrument for promoting sustained economic growth in India and also emphasise the urgent need for maintaining existing infrastructure. The study suggests that India should follow an infrastructure development-led growth strategy to achieve high and sustained economic growth.
Planning is challenging. It requires imagining necessary steps, exploring solutions, and evaluating costs. Traditional planning software systems in the AEC (architectural, engineering and construction) industry are often complex, time-consuming and unusable directly by experienced project leaders. This paper contributes a novel framework for easily accessible preliminary project planning with virtual reality (VR) and game engines. It includes: (1) new integration and workflow models, (2) specifications for game engine, physics engine and multi-player functionalities, and (3) newly defined levels of detail that align simulation capabilities with planner needs. To examine the framework's potential efficacy, application domains are identified and corresponding use cases were developed and analysed. It was found that in those use cases, the VR controls were easy-to-learn, and the simulation was powerful for visualisation and useful to detect clashes and line-of-sight issues. However, vertigo affected some users. The game engine facilitated efficient creation of simulations with adequate physics, collision detection and multiplayer capabilities. Thus, the framework is demonstrated to facilitate potentially faster exploration and iteration through plan options, intuitive manipulation of virtual environments, enhanced collaboration and immersive communication with clients.
The discussions on digital twins (DT) have greatly increased, and this technology is believed to solve many of the engineering and construction (E&C) industry's problems. However, there is a significant gap between this perceived potential and the maturity level of practical applications. The proliferation of context-specific frameworks for each DT project hinders the widespread adoption of DTs across the industry. This paper aims to shift the focus from individual DT frameworks to viewing DT as a tailored pack of existing technologies, chosen according to the purpose of the digitalisation initiative. A purpose-driven roadmap for DT adoption in the E&C industry is introduced to bridge the gap between perceived potential and practical applications. The methodology of this study comprises a literature review, DT investigation, and a case study analysis. The stages of the proposed roadmap stages include assessment, purpose definition, technology selection, implementation, and optimisation. The roadmap is validated, and its application is demonstrated through a case study of a reinforced concrete chimney in Sweden, where two longitudinal cracks had been previously identified. The conclusion highlights findings and future directions, emphasising the roadmap's role in fostering a flexible and impactful adoption of DTs in the E&C industry.
Road construction projects often face performance challenges due to their complexity and the lack of standardised evaluation frameworks. Existing models, such as the Project Quarterback Rating, are limited in scope and fail to capture key performance dimensions. This study introduces a Modified Project Quarterback Rating (MPQR) model tailored for road construction projects to provide a more comprehensive and stakeholder-inclusive assessment. A mixed-method approach was used, combining survey data from industry professionals with insights from case studies. The MPQR model integrates ten performance areas – including cost, schedule, quality, safety, and stakeholder satisfaction – along with specific metrics to generate an overall project performance score. The results show that the MPQR model provides a holistic and practical framework for evaluating road project performance. It addresses the limitations of existing models, supports informed decision making, and enables benchmarking across projects. This research contributes a novel and comprehensive performance evaluation framework that overcomes the limitations of existing models by integrating multiple stakeholder perspectives and performance metrics. The MPQR model enhances decision making, supports benchmarking across projects, and provides a standardised method for assessing performance in the road construction sector.
Corruption remains a persistent challenge in the construction industry, driven by complex socio-economic and institutional dynamics. This study investigates corruption by combining anthropological and motivational perspectives. A survey identified ten variables contributing to corruption, using SPSS-based analyses, including mean scores, factor analysis, and structural equation modelling. The research involved 30 pilot practitioners and an e-survey with 200 participants. Show a positive correlation between socio-economic factors and industry progress, but greed and favouritism do not significantly correlate with growth. Dysfunctional systems and societal disparities notably hinder development. Addressing these issues through targeted interventions could help accelerate progress. Furthermore, leveraging the socio-economic landscape and managing favouritism may promote development, offering a roadmap for industry stakeholders. Focus on immediate measures such as social welfare programmes and minimum wage regulations, as well as long-term solutions that involve structural reform, transparency, equitable wealth distribution, and law enforcement. This study is novel in highlighting anti-corruption strategies for the construction industry in developing countries, particularly Ghana, where prior research has been limited. Its single-case study design limits wider applicability.
In India, low-volume rural roads make up ≈80% of the road network but often suffer from neglect due to limited funding and inadequate planning. This study aims to address the critical need to prioritise the maintenance of these roads through a comprehensive approach. This study used the quadruple bottom line framework, which incorporates social, environmental, economic, and technological factors, thus expanding the conventional triple bottom line approach. A detailed literature review identifies seven sustainable pavement maintenance factors. An expert survey using the analytic hierarchy process was conducted to assign weights to five potential maintenance alternatives. Based on this analysis, this study developed the Sustainable Pavement Maintenance Treatment Assessment Index (SPMT-AI), a tool designed to guide the selection of sustainable maintenance treatments for low-volume roads. SPMT-AI was applied to data from eight roads in Hanmakonda District, incorporating distress survey data and International Roughness Index values. The tool provides tailored maintenance recommendations aimed at enhancing the sustainability and effectiveness of road upkeeps. These findings underscore the importance of a multidimensional approach for road maintenance. This study offers a practical framework for prioritising and implementing sustainable treatments, significantly contributing the need for innovative strategies for the maintenance of low-volume roads.
Urban renewal plays an important role in building a liveable, green, resilient, smart, and cultural city. Urban security problems are inevitable in urban renewal. How to prevent and control urban safety hazards from the source is a topic worth exploring. City information modelling (CIM) platform is an operational platform for new urban infrastructure construction, which provides important support for urban physical examination and urban security through real-time dynamic monitoring of safety hazards in urban renewal process by Internet of Things (IoT) sensor devices. IoT technology has characteristics like sensing, interconnection, and intelligence. Based on the city safety problems, this paper proposes the method of urban renewal safety hazards identification model and analyses the urban renewal types and the objects of safety hazards, builds a framework from IoT sensing, monitoring, and warning to hazards management (URSHF-CIM). And then based on IoT, this paper builds the urban renewal safety hazards recognition framework which contributes to research on city security and the IoT application in the future.
Construction projects often suffer cost/time overruns and reduced quality due to design-construction mismatches and lack of constructability. This dynamic process involves evolving variables requiring quantified, non-binary assessment to optimise buildability. This study identifies key obstacles to constructability implementation and models their interdependencies and impacts on project time, cost, and quality using fuzzy cognitive mapping (FCM). Based on literature, expert input, and an Iranian megaproject case study, a three-layer FCM with 28 obstacles, 10 consequences, and 3 failure modes was developed. Scenario analysis revealed critical barriers: viewing constructability as a cost rather than investment, contractors’ unwillingness to engage in design, inflexible contracts, and limited contractor knowledge. Results enable targeted planning, early integration, and cultural shifts to reduce duplications, enhance effectiveness, and support constructability from initial design phases.
