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The use of plastic in concrete through various substitution approaches have been targeted in last few decades with the main aim of plummeting the environmental loads of construction industry. Although the major mechanical and durability properties of plastic aggregate concrete have been studied in depth, petite data is available for its creep characteristics. Therefore, the current study was designed to access the creep and creep recovery of plastic processed aggregates concretes. Five concrete mixes consisting of plastic processed aggregate at replacement levels of 0%, 25%, 50%, 75% and 100% were developed at w/c of 0.5. The tests were conducted on all the specimens to determine the mechanical properties and evaluate the creep and creep recovery of the concrete for a period of approx. three years. The augmentation of plastic processed aggregate substitution resulted in the amplification of the instantaneous creep strain, ultimate shrinkage strain and ultimate creep strain of the plastic processed aggregate concrete. An increase of 100, 119 and 69% was noted for instantaneous creep strain, ultimate shrinkage strain and ultimate creep strain at total substitution. The creep recovery increased by 30.1, and 96.8% for a plastic processed aggregate replacement of 25% and 100% respectively. Overall it was noticed that the increase in the plastic processed aggregates have resulted in the decrease of compressive strength and increase of creep strains and creep recovery as compared to the reference mix. The results from this experimental study advocate the use of 100% plastic processed aggregates specifically for non-structural application requiring flexible solution.

This study assesses the impact of traffic-related air pollution (TRAP) on indoor air quality (IAQ) within a Riyadh school. The main objectives are to pinpoint factors influencing IAQ and to evaluate the effectiveness of existing ventilation and filtration systems. Air quality Egg sensors monitored nitrogen dioxide (NO 2 ), carbon monoxide (CO), and particulate matter (PM1.0, PM2.5, PM10) levels both indoors and outdoors over 30 days. Findings revealed average indoor PM2.5 and PM10 concentrations of 14.09 µg/m 3 and 18.15 µg/m 3 , respectively, while outdoor concentrations averaged 20.63 µg/m 3 and 27.88 µg/m 3 . A strong positive correlation was observed between indoor and outdoor PM levels, with Spearman coefficients ranging from 0.81 to 0.94. Principal Component Analysis (PCA) indicated that particulate matter is the primary contributor to IAQ issues, followed by humidity and gases like NO 2 and CO. Notably, indoor NO 2 averaged 43.36 ppb, surpassing the outdoor average of 38.66 ppb, suggesting indoor sources and ventilation shortcomings. These results highlight the need for strategic school siting, enhanced HVAC systems, and routine IAQ monitoring. Recommendations emphasize locating schools away from major traffic areas and adopting advanced air purification technologies. Expanding research across schools could further clarify IAQ impacts, supporting healthier environments for children.

Emergency evacuations in built environments pose significant challenges for individuals with disabilities, yet traditional simulation models often fail to account for heterogeneous mobility needs. While considerable advances have been made in pedestrian dynamic modeling, a critical gap persists in the realistic incorporation of disability-specific movement limitations and environmental barriers. This paper presents an inclusive evacuation simulation framework based on an extended social force model, explicitly integrating wheelchair users and visually impaired individuals. The model modifies agent parameters such as desired speed, relaxation time, body size, and barrier navigation capability to reflect empirical observations. Key enhancements include a probabilistic falling mechanism under high crowd pressure and dynamic interaction with environmental obstacles. A single-room evacuation scenario involving 50 agents, including 20% disabled individuals, was simulated using this framework. Results demonstrated that the presence of disabled individuals increased total evacuation time by approximately 50% compared to an all-able-bodied crowd, led to a 40% reduction in peak evacuation throughput after crowd falls, and caused arching, clogging, and faster-is-slower effects to intensify. Two fall incidents occurred within the first 4 s, resulting in partial door blockage and additional delays. Heatmaps revealed localized congestion zones induced by mobility impairments, and kinetic energy analysis illustrated significant dissipation due to frictional interactions at the exit. The findings underscore the necessity of inclusive modeling to identify critical vulnerabilities in evacuation plans and highlight the importance of design interventions such as wider doorways, alternative accessible exits, and controlled evacuation flow for heterogeneous crowds. This work offers a robust foundation for performance-based inclusive design and supports future extensions into multi-level structures, dynamic assistance modeling, and optimization-based evacuation planning.

Several earned value techniques are used to monitor progress and forecast the cost and time performance of construction projects. However, their forecasting accuracy, particularly across different project types and progress stages, remains understudied. This study evaluates the predictive accuracy of three earned value techniques: Earned Duration, Earned Schedule, and Planned Value—across a diverse dataset of 30 construction projects. The overarching objective is to evaluate the accuracy of these techniques in predicting project completion dates and to discern the most effective method based on project duration percentages. The findings reveal that Earned Schedule provides the most accurate predictions during early project stages, while Earned Duration is more reliable at later stages. The results emphasize the importance of considering project type and progress level when selecting a forecasting method. Practical guidelines are proposed to assist project managers in choosing the most effective technique at different stages of project development. By refining forecasting accuracy and providing strategic guidelines, this research serves as a valuable resource for achieving enhanced project outcomes across diverse construction projects.

Achieving sustainable green building design is essential to reducing our environmental impact and enhancing energy efficiency. Traditional methods often depend heavily on expert knowledge and subjective decisions, posing significant challenges. This research addresses these issues by introducing an innovative framework that integrates building information modeling (BIM), explainable artificial intelligence (AI), and multi-objective optimization. The framework includes three main components: data generation through DesignBuilder simulation, a BO-LGBM (Bayesian optimization–LightGBM) predictive model with LIME (Local Interpretable Model-agnostic Explanations) for energy prediction and interpretation, and the multi-objective optimization technique AGE-MOEA to address uncertainties. A case study demonstrates the framework’s effectiveness, with the BO-LGBM model achieving high prediction accuracy (R-squared > 93.4%, MAPE < 2.13%) and LIME identifying significant HVAC system features. The AGE-MOEA optimization resulted in a 13.43% improvement in energy consumption, CO2 emissions, and thermal comfort, with an additional 4.0% optimization gain when incorporating uncertainties. This study enhances the transparency of machine learning predictions and efficiently identifies optimal passive and active design solutions, contributing significantly to sustainable construction practices. Future research should focus on validating its real-world applicability, assessing its generalizability across various building types, and integrating generative design capabilities for automated optimization.

This study identifies a critical knowledge gap, revealing how the deterioration of roads, compounded by extensive usage and additional factors, poses significant risks to the road networks’ functionality. Without a robust fund allocation and prioritization strategy, the extent of this risk may be overlooked, adversely affecting the performance of essential infrastructure elements. Our research introduces an integrated decision-making model for existing road infrastructures to address this gap. This innovative approach combines a Geographic Information System (GIS)-based road management model with a fund allocation prioritization strategy, enhanced by an optimization engine via a genetic algorithm. The primary aim is to precisely determine Maintenance and Repair (M&R) interventions tailored to the condition states, thereby improving the Pavement Condition Index (PCI) of the road segments. The research is structured around three key objectives: (1) develop a detailed GIS-based road management database incorporating inspection data and attributes of road infrastructure for proactive M&R decision-making; (2) efficiently allocate funds to maintain service delivery on deteriorated roads; and (3) pinpoint the optimal type and timing of M&R interventions to boost the condition and performance of the road segments. Anticipated results will provide asset managers with a comprehensive decision support system for executing effective M&R practices.

In contemporary building design and management, catering to the needs of individuals with disabilities presents a multifaceted challenge. Buildings tailored to accommodate individuals with disabilities, featuring accessibility features are integral in various contexts, which are essential to ensure equitable access and usability for individuals with disabilities. However, research in disability-friendly building construction and management has been relatively limited due to the diverse and evolving needs of demographic. Factors like designing efficient wheelchair routes, maintaining escalators and elevators, and managing hearing aid systems all impact a building’s operation. This paper utilizes simulation modeling in optimizing buildings designed for individuals with disabilities which presents a paradigm shift in inclusive building design, resulting in substantial improvements in accessibility and efficiency. The model creates a network representation of the building, incorporating delays and queue systems to simulate people and resource flow, accounting for bottlenecks and constraints to determine the optimal resource allocation and operational timing for disability-friendly buildings. By assessing various scenarios and conducting optimization analyses, the model identifies the best combination of resources and schedules to minimize delays, enhance accessibility, and ensure the building functions optimally, meeting regulatory requirements and the needs of individuals with disabilities. Through the implementation of the model, Equipment and Machinery resources optimization significantly saves duration by 15.17 and 14.29%, respectively. Overall optimization results show a duration reduction from 1450 to 930 days, saving 35.86% and a productivity limits improvement varies between 30 and 36%. These gains translate into cost savings, reducing operational expenses and potentially speeding up return on investment.

The necessity for automatic monitoring tools led to using 3D sensing technologies to collect accurate and precise data onsite to create an as-built model. This as-built model can be integrated with a BIM-based planned model to check the project’s status based on algorithms. This article investigates the construction progress monitoring (CPM) domain, including knowledge gaps and future research direction. Synthesis literature was conducted on 3D sensing technologies in CPM depending on crucial factors, including the scanning environment, assessment level, and object recognition indicators’ performance. The scanning environment is important to determine the volume of data acquired and the applications conducted in the environment. The level of assessment between as-planned and as-built models is another crucial factor that could precisely help define the knowledge gaps in this domain. The performance of object recognition indicators is an essential factor in determining the quality of studies. Qualitative and statistical analyses for the latest studies are then conducted. The qualitative analysis showed a shortage of articles performed on 5D assessment. Then, statistical analysis is conducted using a meta-analytic regression model to determine the development of the performance of object recognition indicators. The meta-analytic model presented a good sign that the performance of those indicators is effective where [p-value is = 0.0003 < 0.05]. The study is also envisaged to evaluate the collected studies in prioritizing future works from the limitations within these studies. Finally, this is the first study to address ranking studies of 3D sensing technologies in the CPM domain integrated with BIM.

Mixed Reality (MR) that combines elements of both augmented reality (AR) and virtual reality (VR) has great potential for use in the construction industry. However, its usage in construction projects in developing countries has not been widely researched. This study aims to examine the major drivers of, and barriers to, the adoption of MR technologies (MRTs) in the construction sector of developing countries. A mixed methodology that included both qualitative and quantitative data analysis was used. The literature review revealed 37 barriers to, and 41 drivers of, MR adoption. A questionnaire was then distributed to 220 randomly selected respondents from the pertinent construction industry, representing all major stakeholders. The relative importance index (RII) was used to rank the barriers and drivers in terms of significance. The results showed that the primary barriers to MR adoption are the high cost of initial investment, public perception of the technology being immature, limited demand, and difficulty accessing relevant experts’ knowledge. The key drivers of MR adoption include improved project knowledge, reduced overall project costs, low-cost and realistic training scenarios, reduced damage and development costs, and enhanced user experience. These findings provide insights into the major barriers and drivers of MR in the construction sector of developing countries and will help pertinent companies to focus their research and development (R&D) efforts on overcoming these barriers and promote their adoption to move towards the much sought-after construction automation and digitalization.

Falls from height (FFH) are common safety hazards on construction sites causing monetary and human loss. Accordingly, ensuring safety at heights is a prerequisite for implementing a strong safety culture in the construction industry. However, despite multiple safety management systems, FFH are still rising, indicating that compliance with safety standards and rules remains low or neglected. Building information modelling (BIM) is used in this study to develop a safety clauses visualization system using Autodesk Revit’s application programming interface (API). The prototype digitally stores and views clauses of safety standards, such as the Operational Health and Safety Rules 2022 and Introduction to Health and Safety in Construction by NEBOSH 2008, in the BIM environment. This facilitates the safety manager’s ability to ensure that the precautionary measures needed to work at different heights are observed. The developed prototype underwent a focus group evaluation involving nine experts to assess its effectiveness in preventing FFH. It successfully created a comprehensive safety clause library that allows safety managers to provide relevant safety equipment to workers before work execution. It also enhances the awareness of construction workers of all safety requirements vis-à-vis heights. Moreover, it creates a database of safety standards that can be viewed and expanded in future by adding more safety standards to ensure wider applicability.