ABSTRACT Gas pipeline failures can lead to substantial economic and environmental loss. Thus, comprehensive seismic-resistant designs and risk mitigation strategies for gas pipelines are essential. Typically, in the seismic design of gas pipelines, only construction costs are considered. In this study, a cost-effective life-cycle framework is developed to design seismic-resistant buried gas pipelines. This framework involves modeling the pipeline using finite element methods, conducting incremental dynamic analyses, extracting seismic fragility curves, and calculating expected annual losses. This framework is applied to a designed gas pipeline network as an illustrative case study to show how the framework works. The case study includes pipelines with varying diameters and thicknesses of 20 and 30 inches. The results reveal how design parameters influence both seismic vulnerability and total life-cycle cost, offering valuable insights for optimizing resilience-oriented pipeline design. Increasing seismic intensity significantly raises expected life-cycle losses, underscoring the trade-off between construction cost and seismic performance.

ABSTRACT Tornadoes devastate communities, razing buildings and critical infrastructure and claiming hundreds of lives annually. To reduce these catastrophic impacts and inform prioritized retrofits under resource constraints, an Alternatives for Resilient Communities (ARC) model for tornado hazards is introduced. ARC is a mathematical programming model informing optimal strategies to improve community-level resilience while meeting constraints related to system interdependencies, budget limits, and other considerations. The ARC model evaluates optimal retrofit strategies, including tornado shelters and structural retrofits for buildings, to minimize tornadoes’ social and economic impacts. Under various potential tornado events and budgetary constraints, optimal resilience enhancement strategies are evaluated, facilitating comparison of alternatives and investment prioritization to create tornado-resilient communities. As a case study, the model is run for the Joplin, Missouri community. The analysis demonstrates the tradeoffs of different retrofit strategies and quantifies their costs and benefits under various conditions, empowering decision makers to enhance resilience.

ABSTRACT In line with paragraph 37 of the United Nations Agenda 2030, sport plays a pivotal role in advancing sustainable development. Despite growing attention to environmental concerns in sports infrastructure, integrated and empirically validated models for sustainable stadium design remain limited. This study develops and validates a comprehensive green model for large sports venues in a context marked by rapid growth of sports infrastructure. Employing a mixed-methods approach, 19 expert interviews were analyzed through the Glaserian grounded theory, yielding 465 initial codes, 72 sub-categories, and 10 core concepts. Subsequently, 380 valid survey responses were examined using second-order structural equation modeling (SEM) with AMOS. The validated framework integrates cultural factors, structural sustainability, green management, recycling practices, transportation systems, and national identity. By illustrating the interrelation of these components, the model offers a strategic tool for environmentally responsible stadium design and provides actionable insights for planners, policymakers, and sports facility designers committed to sustainability in large-scale venue development.

ABSTRACT Vehicle traffic is the primary noise source in urban areas, mainly generated by tire-pavement interaction, vehicle propulsion, and air turbulence. Tire-pavement noise stems from tire pattern, sliding of tire, pavement roughness, and air-pumping, influenced by tread impact and other mechanisms. Amplification factors like horn and resonance effects amplify this noise: surface characteristics like porosity, texture, and mechanical impedance play a crucial role. For asphalt pavements, noise reduction can be achieved through different construction technologies such as porous, textured, and poroelastic pavements. For rigid pavements, adjustments to porosity and surface texture exhibit notable noise reduction capabilities. Despite the higher initial implementation costs, case studies highlight long-term societal benefits, contributing to sustainable, quieter environments. This paper provides a narrative account of the tire-pavement noise generation, amplification, and reduction mechanisms, as well as examines the different quiet pavement technologies for asphalt and concrete pavements. Furthermore, the scope for further research is also discussed.

ABSTRACT Construction progress monitoring faces errors due to subjective reporting, lack of integrated visualization, and poor quantification of physical progress. Digitizing as-built construction progress versus the as-planned through immersive visualization can help in real-time progress measurement. Mixed Reality (MR), integrated with Building Information Modeling (BIM) can enable such digitization. Literature shows that workflow for real-time progress measurement in an MR environment, combining visualization of as-built and remaining as planned elements, is currently lacking. This study develops a workflow integrating MR and BIM for construction progress measurement, utilizing actual geometric measurements in an MR environment rather than binary approach of built vs. non-built elements. The Unity3D game engine was used to create an MR-based application, along-with Revit-based BIM for real-time immersive visualization and progress measurement. The application overlays remaining as-planned work over the as-built in real-time, along-with improving user experience to measure progress, and identify errors or clashes. Progress data can be extracted in spreadsheet for comparison, aiding further analysis. The developed workflow’s implementation on a case study building showed accuracy of 98.67%, having maximum error of 4.07%, hence, 99% confidence interval for progress measurement. This workflow can enhance construction management efficiency with improved progress data collection, comparison, and immersive visualization.

ABSTRACT Environmental protection has become increasingly critical due to challenges such as global warming, air pollution, and threats to human health and ecosystems. This study assesses environmental impacts of a hollow steel roof structure using a cradle-to-grave life cycle assessment. Data were collected through interviews, a site visit to a steel structure with waffle slabs in Isfahan, and the Ecoinvent database within SimaPro software. Analysis using the ReCiPe 2016 Endpoint (H) method showed that steel beams and columns had the highest impact score of 36.36, followed by aluminum with 5.25 and concrete with 4.76, affecting human health, ecosystem quality, and resource depletion. In contrast, recycling polyethylene, glass, and steel resulted in lower impact scores of 1.68, 1.3, and 0.9. Monte Carlo analysis revealed uncertainties related to water quality, human health, and ecosystem integrity. The results emphasize recycling, circular economy strategies, and life cycle assessment in reducing construction environmental impacts.

ABSTRACT This paper explores how linear infrastructure 4.0 projects impact sustainable development in developing countries across economic, social, environmental, and governance dimensions. Linear infrastructure 4.0 represents the advanced evolution of linear networks through the integration of digital technologies like the IoT, Big Data, AI, sensors, and BIM. From planning to operation, this methodology optimizes all project phases into efficiency, safety, environmental, economic, and social sustainability. Despite progress, most existing studies focus on developed nations, leaving a gap regarding their applicability in developing contexts. To address this, a systematic literature review and expert surveys (48 professionals) using the Relative Importance Index (RII) were conducted. Key strategies include reducing traffic congestion, lowering emissions, and improving quality of life, categorized into seven components such as resources and waste, investment and governance and energy efficiency. Focused on experts from South America, the study provides a practical framework to strengthen sustainability in infrastructure projects and support the Sustainable Development Goals (SDGs).

ABSTRACT Natural and human-made hazards deteriorate critical infrastructures and cause failures. To reduce losses, operators perform preventive maintenance. However, such hazards are often recurrent and interact dynamically with each other, complicating the preventive maintenance policy optimization. In this study, we propose a framework to model system availability and resilience with one or more performance measures under dependent recurrent hazards, and to derive optimal preventive maintenance for varying risk preferences. Specifically, we characterize hazards whose occurrence frequencies and severities are interdependent, model system availability and resilience under these interactions, and formulate two optimization problems that yield policies for risk-neutral and risk-averse decision makers. Efficient algorithms are developed to evaluate availability and sample representative hazard scenarios while searching for optimal policies. A case study on the underground pipeline system in Pennsylvania and New York is provided to illustrate the application of the proposed resilience assessment and maintenance optimization methods.

ABSTRACT This study aimed to propose a model to predict the frequency of fire incidents and classify their severity in urban residential buildings, with a focus on fire prevention. Data on 92,000 fire incidents in London from 1981 to 2020 were collected. Data mining was then performed using the NumPy and Pandas libraries in Python to extract statistical information. The data were converted into numeric form using natural language processing (NLP) before modeling. In the modeling phase, a time series algorithm was utilized to predict fire frequency in London up to 2040, demonstrating that the fire frequency in 2040 would be 50% lower than in 2020. In the second phase, various machine learning models, including DT, RF, GB, SVM, and LR, were implemented for building fire severity classification. Among these models, the neural network demonstrated the highest performance, achieving an accuracy rate of 82%, outperforming the other approaches.

ABSTRACT Spatio-temporal data provides significant benefits for urban infrastructure by enabling dynamic insights and predictive decision-making across complex environments. Integrating spatial and temporal dimensions allows asset managers to anticipate issues, optimize performance, improving adaptability and promoting resilience. However, systematic methods and practical applications in asset management remain underexplored which necessitating deeper investigation. Therefore, this study conducts a Systematic Literature Review (SLR) following the PRISMA protocol to examine current applications of spatio-temporal data in infrastructure asset management. The review identified four major themes and eighteen sub-themes on spatio-temporal data for asset management covering (1) application areas in asset management, (2) stages of asset lifecycle benefiting from spatio-temporal data, (3) data sources, software, and tools, and (4) analytical approaches in enhancing asset management. Key challenges related to spatio-temporal data are also discussed, and future directions highlight unified frameworks and advanced tools such as 3D visualization and digital twins.