Despite the significant increase in publications on construction project performance (CPP), there is a deficiency of research that rigorously assesses and synthesizes previous studies to delineate the field’s development, themes, and research gaps. This article employs quantitative and qualitative methodologies to critically evaluate studies on CPP published over the last three decades and indexed in the Scopus database. The quantitative approach includes bibliometric searches and scientometric analyses to assess the extent of research interest and achievements. The qualitative methodology aims to conduct thorough content analysis to classify existing material based on prevalent themes. The results demonstrate an exponential growth of interest in this scientific research topic. Project management, construction industry, construction projects, project performance, and critical success factors are top keywords in pertinent research publications. This research field has been investigated in over 50 nations and published in over 100 scholarly journals. The United States and the Journal of Construction Engineering and Management are the primary contributors. Prior studies were categorized according to their objectives into four main research themes: project performance assessment and prediction, project performance improvement, critical success factors, and key performance indicators. This article uncovers research gaps and suggests avenues for further investigations. Researchers and practitioners may utilize the findings of this study to evaluate and implement CPP principles.

The coastal region of the Nile Delta in Egypt is highly susceptible to various natural hazards and climate change, posing significant threats to its transportation infrastructure. Existing risk mapping approaches typically consider vulnerability and hazard-affecting factors but often overlook the resilience of the actual road. As such, this study presents a multihazard risk approach for assessing the resilience of the International Coastal Road (ICR), which is a major road running along the coast of the Mediterranean Sea by integrating the impacts of climate change, including coastal floods, heatwaves, and land subsidence. To achieve this, a combination of remote sensing based datasets and techniques, including Google Earth Engine, synthetic aperture radar, along with the analytic hierarchy process method, are employed to generate a comprehensive risk map for the ICR based on its resilience. The risk index maps for the four possible occurrence scenarios are created and summed to extract the most repeated section of the ICR with a very high-risk index. The resultant risk index map shows that approximately 74% of the ICR is prone to one or more hazards. Furthermore, visual field investigations confirmed the obtained results for most portions of the ICR's susceptibility to multiple hazards.

Budget overruns driven by unpredictable building material prices are a persistent challenge in emerging economies, yet reliable price forecasting tools for structural materials such as steel reinforcement bar (RFT) remain largely unavailable to building project teams. This study develops an Autoregressive Distributed-Lag (ARDL) model to forecast RFT prices in Egypt using macroeconomic leading indicators. A structured filtering pipeline—stationarity testing, Variance Inflation Factor (VIF) screening, and Granger causality testing—reduced 22 candidate variables to nine predictors: producer price index (PPI), loan rate (LR), discount rate (DR), Egyptian Stock Exchange Index (EGX30), money supply (M0), exchange rate (ER), iron ore prices (ORE), American stock index (S&P500), and Hang Seng Index (HSI). ARDL bounds testing confirmed cointegration across look-back periods (LBP) of 3, 6, and 9 months. Comprehensive diagnostics confirmed model stability, homoscedasticity, and the absence of serial correlation. The ARDL was benchmarked against an Ordinary Least Squares (OLS) baseline and a first-difference vector autoregression (VAR) model. Evaluated as genuine multi-step (dynamic) forecasts over the held-out crisis-period window, the 3-month model achieved an out-of-sample mean absolute error of approximately 5.9% in price terms, retaining a clear advantage over the OLS baseline at that horizon. The proposed framework provides quantity surveyors, cost consultants, and project managers with a practical tool for setting RFT budgets at the tendering stage and optimising procurement timing. It is transferable to other building materials (cement, structural steel sections, glazing) across emerging-market construction economies.

Artificial intelligence has attracted increasing attention in the construction industry; however, automated time scheduling remains limited in practical applications. Schedule development remains manual, requiring planners to analyze project documents, define activities, estimate durations, and identify relationships based on logical sequence. This process primarily depends on individual experience and skills, making it both time-consuming and prone to human error. From an engineering design perspective, delayed or inconsistent schedule development weakens design-to-construction feedback, limiting the ability to evaluate constructability and time implications of alternative design decisions during early-stage planning. This study proposes an integrated BIM–Natural Language Processing (NLP) framework to automate activity identification, duration estimation, and logical sequencing for construction scheduling. The framework extracts project data from Revit, organizes it into a bill of quantities format, and then generates an activity list, each activity with a unique ID. Using Sentence-BERT (SBERT) embeddings, the framework estimates activity durations based on semantic similarity. The same semantic process is combined with rule-based reasoning to identify logical relationships, including sequences, supported by an Excel-based reference dictionary that includes logical relationships, productivity, and ID structure. Finally, the framework incorporates a crashing module that proportionally adjusts the duration of activities on the longest path to target the project’s completion time without violating relationships. The proposed framework was validated using real construction project data and produced reliable results. By producing a tool-ready schedule directly from design-model information, the proposed workflow enables earlier schedule feedback loops and supports design-informed planning by allowing designers and planners to assess the time consequences of model-driven scope changes. The results demonstrate that integrating BIM and NLP can transform conventional schedules into faster, more consistent processes, thereby supporting the construction industry.

Using recycled aggregate concrete (RAC) has recently been growing rapidly as an alternative to conventional concrete for sustainable development in construction. Nevertheless, there are limitation in computational guidance for compressive strength (CS) of RAC. Thus, this study aims to develop ensemble data-driven models for estimating the CS of RAC. Five ensemble models, namely category-boosting (CatBoost), gradient-boosting (GBoost), extreme gradient-boosting (XGBoost), K-nearest neighbor boosting (KNN), and random forest (RF), were developed, examined and compared for estimating the 28-day CS. A total of 578 datasets of different RAC mixtures were used to develop and test the proposed models. SHapley Additive exPlanations (SHAP) was used to analyze the impact of the used input variables on CS. The multivariate adaptive regression splines (MARS) algorithm was used to formulate the relationship between significant input variables and CS. The results show that CatBoost model outperformed the other proposed models with correlation coefficient (r) and mean absolute error (MAE) scores of 0.92 and 4.36 MPa, respectively. SHAP results of the CatBoost model show the impact of water/cement ratio is highly significant in modelling CS of RAC followed by nominal maximum recycled concrete aggregate (RCA) size, RCA replacement ratio, and bulk density of natural aggregate. Although parent concrete strength and the Los Angeles abrasion index of RCA have limited influence on the CS of RAC, they can be used to enhance the CS when both are greater than 40 MPa and 35 MPa, respectively. The proposed MARS equations can be used as a guideline in the design stage of RAC mixtures.

The transition to a circular economy (CE) in concrete waste management (CWM) is imperative for mitigating resource depletion and carbon emissions within the construction and demolition waste (CDW) sector. This study provides a rigorous two-tier methodological framework, combining a broad bibliometric mapping of 1,574 peer-reviewed studies (Scopus, 2015–2025) with a targeted qualitative synthesis of 53 high-impact core studies. Through bibliometric co-occurrence analysis and systematic screening, the research identifies the evaluation of technical, environmental, and economic strategies for transforming concrete waste (CW) into recycled concrete aggregates (RCA) across four critical building lifecycle stages: design, planning, construction and renovation, and material circularity. The findings are synthesized into a theoretical framework comprising 12 core strategies anchored in the 3Rs principles (reduce, reuse, recycle), focusing on the high-value reintegration of RCA into the production loop. By analyzing the structural performance and durability of RCA-based components, the study differentiates between genuine “closed-loop” circularity–where high-purity RCA is upcycled into structural-grade concrete or 3D-printing materials–and conventional downcycling into low-grade backfill. The framework provides an evidence-based pathway for optimizing the utility of CW through advanced sorting and selective deconstruction practices that ensure the quality of the resulting RCA. The analysis demonstrates that the integrated adoption of these practices allows the construction industry to significantly reduce dependency on virgin aggregates, lower the embodied energy of new structures, and foster long-term economic and environmental sustainability by treating CW as a valuable RCA resource rather than a disposal burden.

Assessing changes in mean sea level (MSL) has become increasingly critical due to the significance of climate changes. Soft computing techniques are now widely used to reduce the time and cost associated with traditional MSL estimation methods. Historical MSL data is frequently used to predict future values, yet the application of soft computing models to analyze climate change’s impact on MSL remains relatively unexplored. This study aims to develop and compare various soft computing techniques for modeling MSL fluctuations using meteorological data. Random forest (RF), support vector regression (SVR), K-nearest neighbors (KNN) regression, deep neural network (DNN), Gaussian process regression (GPR), and stacked ensemble methods are employed in this study. The newly developed models are statistically assessed for their effectiveness in modeling MSL at Damietta station, Egypt. Variables environmental data such as surface water temperature, pressure, air temperature (average air temperature, dewpoint, wet-bulb, and heat index), and humidity and wind attributes (speed and direction) are utilized and evaluated in modeling MSL. The results indicate that RF, KNN, and GP outperformed other proposed models in modeling MSL during both training and testing phases. The developed weighted stacked ensemble model, integrating RF, KNN, and GPR, outperformed the base models with a correlation coefficient (R) of 0.88 and normalized root mean square error (RMSE) of 0.056 m. MSL modeling at the study station was particularly sensitive to variations in water temperature, wind speed and direction, and atmospheric pressure. This methodology serves as a valuable framework for climate-driven MSL forecasting in developing coastal regions lacking long-term tide records, directly contributing to UNESCO’s Ocean Decade Challenge 5 on coastal resilience.

Harbour sedimentation represents a major challenge to the environmental sustainability and operational efficiency of coastal infrastructure, as frequent dredging activities increase maintenance costs, ecological disturbance, and carbon emissions. Conventional physical and numerical sediment transport models, while widely applied, are computationally intensive and often unsuitable for early-stage, sustainability-oriented design optimisation. To address these limitations, this study proposes a hybrid artificial intelligence-based optimisation framework integrating Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), and Particle Swarm Optimisation (PSO) for sustainable breakwater and harbour layout design. Hydrodynamic simulations using the Coastal Modelling System (CMS) were conducted to generate a comprehensive dataset describing sediment transport behaviour under varying geometric and structural configurations. An ANN surrogate model was trained to capture nonlinear relationships between breakwater parameters and accumulated sedimentation volume, while GA-based global optimisation and PSO-based validation and local refinement were employed to identify optimal design solutions. Comparative assessment demonstrated consistent convergence of ANN-GA and ANN-PSO solutions within the same design region, with a maximum deviation of 8.46% between design variables and a sedimentation difference of 2.4%. The hybrid ANN-GA-PSO framework achieved the lowest predicted sedimentation volume, representing an improvement of approximately 2.3% relative to the ANN-GA baseline. The proposed framework supports Integrated Coastal Structures Management (ICSM) by enabling proactive, design-stage reduction in long-term sediment accumulation and dredging requirements, offering a scalable pathway toward sustainable and digital-twin-enabled harbour planning.

Decentralized community recycling offers a sustainable pathway for managing 100% cement-based waste (CBW) from renovation activities, providing a viable alternative to centralized recycling facilities and landfilling. This study employs an integrated BIM-Life Cycle Assessment (LCA) framework to evaluate the use of recycled concrete aggregate as a substitute for natural aggregates in non-structural concrete. The CBW was dismantled, segregated, crushed, and graded to produce RCA. Thirty-six specimens were prepared in three mixes: CBW0 (100% NA), CBW1 (100% coarse RCA + 50% fine RCA, Styrene butadiene rubber/Water (SBR/W) = 1:25), and CBW2 (100% coarse RCA + 50% fine RCA, SBR/W = 1:50). A cradle-to-site LCA (A1-A4), following the EN 15,804 + A2 (adapted) baseline, quantified the environmental impacts of 1 m3 of concrete. Relative to conventional concrete (CC), CBW2 achieved the target for grade 20 concrete, reaching 16 MPa after 28 days while reducing overall environmental impacts by 29%. Phase-specific savings reached 23.4% (A1-A3) and 75% (A4). These outcomes demonstrate the potential of decentralized recycling and BIM-LCA integration to balance mechanical performance with environmental efficiency in sustainable construction.

Selecting the appropriate excavation support system (ESS) is critical for ensuring construction projects’ safety and cost-effectiveness. Several dynamic factors influence this decision-making process, such as the groundwater table, excavation depth, proximity to neighboring buildings, and soil characteristics. In practice, the selection often depends heavily on the subjective judgment of experienced professionals in the construction industry. Although the analytical hierarchy process (AHP) is frequently employed to evaluate alternatives using multiple criteria, it fails to adequately account for the subjectivity and uncertainty in converting the decision-maker’s intuition into exact numerical values. To overcome this challenge, this study proposes an enhanced method known as fuzzy AHP. This approach is designed to capture the subjective experiences of experts better and effectively incorporate the uncertainties present in the decision-making process, ultimately aiding in identifying the most suitable ESS. A case study of excavation projects is also included to demonstrate the practical application of the proposed model. By presenting this approach, the study aims to raise awareness within the construction industry about the critical factors to consider when selecting the best excavation support technique for specific projects.

The process of designing reinforced concrete (RC) buildings has traditionally relied on manually evaluating a limited number of layout alternatives—a time-intensive process that may not always yield the most functionally efficient solution. This research introduces a parametric algorithmic model for the automated optimization of RC buildings with solid slab systems. The model automates and optimizes the layout process, yielding measurable improvements in spatial efficiency while maintaining compliance with structural performance criteria. Unlike prior models that address structural or architectural parameters separately, the proposed framework integrates both domains through a unified generative design approach within a BIM environment, enabling automated evaluation of structurally viable and architecturally coherent slab layouts. Developed within the parametric visual programming environment in Dynamo for Revit, the model employs a generative design (GD) engine to explore and refine various design alternatives while adhering to structural constraints. By leveraging a BIM-based framework, this method enhances efficiency, optimizes resource utilization, and systematically balances structural and architectural requirements. The model was validated through three case studies, demonstrating cost reductions between 2.7% and 17%, with material savings of up to 13.38% in concrete and 20.87% in reinforcement, achieved within computational times ranging from 120 to 930 s. Despite the current development being limited to vertical load scenarios and being most suitable for regular slab-based configurations, the results demonstrated the model’s effectiveness in optimizing grid dimensions and reducing material quantities and costs, and highlighted its ability to streamline early-stage design processes.

Timely approvals and payments to the project participants are crucial for successful completion of construction projects. However, the construction industry faces persistent delays and non-payments to contractors. Despite the desirable benefits of automated payments and enhanced access to digitized data progress, most payment applications rely on centralized control mechanisms; inefficient procedures; and documentation that takes time to prepare, review, and approve. As such, there is a need for a reliable payment automation system that guarantees timely execution of payments upon the detection of completed works. Therefore, this study used a cutting-edge approach to automate construction payments by integrating blockchain-enabled smart contracts and scan-to-Building Information Modeling (BIM). In this approach, scan-to-BIM provides accurate, real-time building progress data, which serve as the source of verifiable off-chain data. A chain-link is then used to securely relay these data to the blockchain system. Blockchain-enabled smart contracts automate the execution of payments upon meeting contract conditions. The proposed approach was implemented on a real case study project. The actual site scan was captured using a photogrammetry 360° camera, which uses a combination of structured light and infrared depth sensing technology to capture 3D data and create detailed 3D models of spaces. This study leveraged accurate, real-time building progress data to automate payments using blockchain-enabled smart contracts upon work completion, thus reducing payment disputes by tying payments to verifiable construction progress, leading to faster release of payments. The findings show that this approach provides a transparent basis for payment, enhancing trust and allowing precise project progress tracking.

Accurately estimating the energy consumption of residential buildings is essential for planning and meeting future demand effectively. However, accurately estimating buildings' energy consumption is time-consuming and a complex process due to the big number of variables. Artificial neural network (ANN) is successfully used to estimate energy consumption in residential buildings. This research aims to develop an integrated ANNmeta-heuristic model to accurately estimate building energy consumption in early-design stage while simplifying the energy consumption calculation process. Particle swarm optimization (PSO), imperialist competitive algorithm (ICA), teaching-learning-based optimization (TLBO), and ant colony optimization (ACO) are used and integrated with the ANN to improve the accuracy of energy consumption estimation. Sixteen variables including building dimensions, building orientation, envelope constructions, and temperature set points are simulated and used in developing and evaluating the proposed models. Also, six cases with different combinations of the input variables are designed and experimented with to simplify the calculation. The results show that building dimensions and solar heat gain coefficient have a significant impact on building energy prediction. Comparison of the developed models shows that the normalized root-mean-square error of the best model, ANN-TLBO, is 0.033 kWh for the best used case. The ANN-TLBO model is highly recommended for buildings energy consumption estimate with a coefficient of determination equals 93.6 %.

PurposeConstruction companies typically look at important factors affecting building performance in order to make long-term improvements. In order to assess and forecast the performance of green buildings (GBs), this study considers the interrelationships among the performance measures and their dynamic behavior over time when assessing building performance. This consideration fills the gap in literature, where performance metrics were measured mostly individually and as steady values.Design/methodology/approachFollowing a comprehensive assessment of the literature and conversations with subject matter experts, 15 performance measures were identified. Five are qualitative, and 10 are quantitative. The analytic network process was used to calculate the priority weights for these performance indices. A system dynamics (SD) model was developed to simulate the interdependency among these indices and their dynamics over time. A case study of a commercial building was used to validate the model applicability.FindingsThe findings of the model simulation show the impact of the interrelationships among the studied key performance indicators (KPIs). For example, while sustainability indices like energy and water exhibited delayed effects, schedule and productivity served as triggering indications with rapid implications. Multiple KPIs were affected by early-stage interventions, especially innovation, as shown by scenario analysis (S1-S4). Innovation improvements, for instance, enhanced cost, schedule and environmental performance in Scenario S4. These findings confirm the model's ability to capture both short-term and long-term dynamics in GB projects.Originality/valueThe developed SD model facilitates the early detection of performance gaps and offers practical advice to improve sustainability results. The model allows users to explore "what-if" scenarios and assess the potential impact of managerial decisions on the success and sustainability of GB projects which maximize the opportunities of success of GB projects.

Purpose The main purpose of this study is to identify critical factors affecting the quality of infrastructure projects and to develop a new index to quantify projects’ quality. Design/methodology/approach Through the literature review and interviews with experts, 77 factors were identified and classified under four main groups. Then, a questionnaire survey was performed to identify the most important factors. Afterward, the analytic hierarchy process (AHP) was used to determine the relative weights of the primary factors that were chosen. The performance of the developed model is demonstrated using a real-life case study located in the new administrative capital, Egypt. Findings Cost of quality group was found to have the highest priority vector (PV) of 0.38. Moreover, top management, project and activity ranked in the second, third and fourth places with a corresponding PV of 0.24, 0.21 and 0.17. Originality/value Managers and contractors can benefit from the proposed model to enhance the overall quality performance of infrastructure projects.

The construction industry is a major contributor to environmental degradation, primarily due to the substantial volumes of construction waste (CW) generated on-site. As sustainability becomes a global imperative aligned with the UN 2030 Agenda, identifying and mitigating the root causes of CW is essential. This study adopts a cross-disciplinary approach to explore the drivers of CW and support more effective, sustainable waste reduction strategies. A systematic literature review was conducted to extract 25 key CW source factors from academic publications. These were analyzed using Social Network Analysis (SNA) to reveal their structural relationships and relative influence. The results indicate that the lack of structured on-site waste management planning, accumulation of residual materials, and insufficient worker training are among the most influential CW drivers. Comparative analysis with industry data highlights theoretical–practical gaps and the need for improved alignment between research insights and site implementation. This paper recommends the adoption of tiered waste management protocols as part of contractual documentation, integrating Building Information Modeling (BIM)-based residual material traceability systems, and increasing attention to workforce training programs focused on material handling efficiency. Future research should extend SNA frameworks to sector-specific waste patterns (e.g., pavement or demolition projects) and explore the intersection between digital technologies and circular economy practices. The study contributes to enhancing waste governance, promoting resource efficiency, and advancing circularity in the built environment by offering data-driven prioritization of CW sources and actionable mitigation strategies.

Precast concrete curbs (PCCs) are crucial for infrastructure, ensuring durability in traffic management and landscaping. This study promotes circular economy (CE) principles by recycling damaged PCCs, reduced to less than 50

PurposeThis study aims to present a significant advancement in the optimization of sustainable infrastructure projects (IPs) by introducing a multiobjective framework that integrates traditional metrics of time and cost with social and environmental impacts. This paper focuses on choosing the best alternatives to complete the project with the least time, cost, environmental and social impact.Design/methodology/approachThe proposed decision-support framework is structured into three submodels: a duration and cost submodel, an environmental impact submodel and a social impact submodel. Each submodel quantifies one dimension of project sustainability. Nondominated sorting genetic algorithm-II is used to generate Pareto-optimal solutions that balance project time, cost, environmental impacts and social costs. To support decision-making among these optimal solutions, Shannon entropy is applied to objectively calculate the weights of each objective based on their variability, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to rank and select the best alternative. The framework's effectiveness is validated through a real-world case study involving a major IP in the new administrative capital, Egypt.FindingsThe model generated 500 Pareto-optimal solutions for "Link 2 Road" project, revealing key trade-offs between sustainability objectives. The fastest solution achieved 254 days duration with minimal social costs (LE 4,562,449), while the most environmental friendly option reduced emissions by 32% at 593 days. A balanced TOPSIS-optimized solution combined competitive metrics: 283 days (11% faster than average), LE 54,735,194 cost, high environmental performance (Infrastructure Environmental Index, IEI, of 967.9) and significantly reduced social impacts (LE 4,961,804, 45% below worst-case). These results demonstrate how the framework enables data-driven decisions across all sustainability dimensions. These results provide infrastructure managers with actionable data to: (1) quantify sustainability trade-offs during planning, (2) justify green construction investments and (3) align projects with sustainable development goals while controlling costs and schedules fundamentally changing how sustainability is operationalized in infrastructure delivery.Originality/valueThis study offers significant value by bridging a critical gap in construction management literature through its integrated approach to achieve sustainability. The introduction of the IEI and Infrastructure Social Cost Index provides standardized metrics for quantifying environmental and social impacts. The framework serves as a practical tool for project managers and policymakers, facilitating the selection of construction methods that align with sustainability goals while maintaining cost and schedule efficiency. By embedding environmental and social considerations into early project planning, this research promotes a proactive approach to sustainable infrastructure development, contributing to global efforts in green construction and responsible resource management.

Sedimentation is one of the most critical environmental issues facing harbors' authorities that results in significant maintenance and dredging costs. Thus, it is essential to plan and manage the harbors in harmony with both the environmental and economic aspects to support Integrated Coastal Structures Management (ICSM). Harbors' layout and the permeability of protection structures like breakwaters affect the sediment transport within harbors' basins. Using a multi-step relational research framework, this study aims to design a novel prediction model for estimating the sedimentation quantities in harbors through a comparative approach based on artificial intelligence (AI) algorithms. First, one hundred simulations for different harbor layouts and various breakwater characteristics were numerically performed using a coastal modeling system (CMS) for generating the dataset to train and validate the proposed AIbased models. Second, three AI approaches namely: Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Artificial Neural Networks (ANN) were developed to predict sedimentation quantities. Third, a comparison between the developed models was conducted using quality assessment criteria to evaluate their performance and choose the best one. Fourth, a sensitivity analysis was performed to provide insights into the factors affecting sedimentation. Lastly, a decision support tool was developed to predict harbors' sedimentation quantities. Results showed that the ANN model outperforms other models with mean absolute percentage error (MAPE) equals 4%. Furthermore, sensitivity analysis demonstrated that the main breakwater inclination angle, porosity, and harbor basin width affect significantly sediment transport. This research makes a significant contribution to the management of coastal structures by developing an AI data-driven framework that is beneficial for harbors' authorities. Ultimately, the developed decision-support AI tool could be used to predict harbors' sedimentation quantities in an easy, cheap, accurate, and practical manner compared to physical modeling which is time-consuming and costly. (c) 2022 Shanghai Jiaotong University. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )