Furkan Uysal

Organized Industrial Zones (OIZs) are highly vulnerable to disasters and business continuity disruptions due to their intensive production activities, critical infrastructure connections, and the concentration of numerous businesses. However, studies that address disaster management and business continuity performance together in OIZs and quantitatively test the relationships among the variables affecting this performance are limited. This study aims to identify the key variables affecting this performance and to test the relationships among them within a holistic structural model. A conceptual framework based on the literature and a questionnaire were developed accordingly; data were collected from 81 participants with knowledge and experience in this field. The proposed model was analyzed using Structural Equation Modeling (SEM). The findings showed that management and leadership have a strong influence on mitigation and preparedness processes, and that disaster management performance can largely be shaped by response and recovery capacity. The results revealed that disaster management performance in OIZs is strengthened not only by mitigation and preparedness activities but also by transforming these activities into effective response and recovery capacity. This paper contributes to the literature by proposing an integrated model and provides practitioners with a framework for developing capacity for mitigation, preparation, coordination, response, and recovery.

Purpose: Construction cost forecasting is vital for early-stage project decisions, including feasibility analysis and bid preparation. This study addresses the challenge of achieving accurate cost predictions using small datasets – an enduring limitation in construction data practices. Design/methodology/approach: The research proposes a decision-making framework that applies a bagging-based ensemble learning (EL) approach using Support Vector Regression (SVR) and Artificial Neural Networks (ANN) as base learners. The framework is validated across four real-world construction datasets, each with fewer than 150 data points. Findings: The ensemble model significantly outperformed single learner models in terms of prediction accuracy. For instance, the Mean Absolute Percentage Error (MAPE) in Dataset 1 decreased from 46.32% (SVR) and 17.84% (ANN) in individual models to 17.84% and 12.92%, respectively, with EL. Similarly, Dataset 2 saw a reduction from 36.62% to 11.41% (SVR) and from 18.59% to 11.41% (ANN). Research limitations/implications: While the proposed method is highly effective for small datasets, its computational efficiency and accuracy on large datasets warrant further exploration. Practical implications: The framework provides a structured, data-driven approach for practitioners and cost estimators to improve early cost predictions when faced with limited data. Originality/value: This study is among the first to develop and validate a decision-making framework for construction cost forecasting specifically tailored for small datasets. It also quantifies the effect of varying ensemble and bag sizes – an area previously underexplored in construction cost forecasting literature.

Reduced Beam Sections (RBS) are used in steel design to promote ductile behavior by shifting inelastic deformation away from critical joints, enhancing seismic performance through controlled energy dissipation. While current design guidelines assist in detailing RBS connections, moment–rotation curves—essential for understanding energy dissipation—require extensive testing and/or modeling. Machine learning (ML) offers a promising alternative for predicting these curves, yet few studies have explored ML-based approaches, and none, to the best of the authors’ knowledge, have applied Explainable Artificial Intelligence (XAI) to interpret model predictions. This study presents an ML framework using Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB), and Ridge Regression (RR) trained on 500 numerical models to predict the moment–rotation backbone curve of RBS connections under cyclic loading. Among all the models applied, the ANN obtained the highest R² value of 99.964%, resulting in superior accuracy. Additionally, Shapley values from XAI are employed to evaluate the influence of input parameters on model predictions. The average SHAP values provide important insights into the performance of RBS connections, revealing that cross-sectional characteristics significantly influence moment capacity. In particular, flange thickness (t_f), flange width (b_f), and the parameter “c” are critical factors, as the flanges contribute the most substantially to resisting bending moments.

Lately, the construction industry has shown a great deal of openness to utilizing novel and innovative technologies. The industry's complexity necessitates cooperative and efficient process management. Utilizing value-adding technologies is crucial in thar regard. Among them, blockchain has recently gained prominence as an enabling technology for smart contracts, and secure data management for the construction industry. This study employs a structural equation model to quantify the relationship between risk management (RM), a key project management activity, and blockchain technology. To this end, a survey was created and distributed to construction professionals. A structural equation modeling (SEM) analysis was performed on the data collected from 103 respondents. The findings show that construction project RM can be significantly affected by key blockchain features. This study contributes to the body of knowledge by presenting a conceptual framework that captures the essential elements of RM and blockchain. The results also empirically show that RM and blockchain are closely related, confirming blockchain's potential and relevance in RM applications. The paper also discusses the risks associated with adopting blockchain in the construction industry. The results of this study can help construction practitioners and policy-makers to create plans for incorporating blockchain into RM workflows.

In this paper, we present a novel memetic algorithm (MA) for the solution of the resource leveling problem (RLP). The evolutionary framework of the MA is based on integration of a genetic algorithm and simulated annealing methods along with a resource leveling heuristic. The main objective of the proposed algorithm is to integrate complementary strengths of different optimization methods and incorporate the individual learning as a separate process for achieving a successful optimization method for the RLP. The performance of the MA is compared with the state-of-the-art leveling methods. For small instances up to 30 activities, mixed-integer linear models are presented for two leveling metrics to provide a basis for performance evaluation. The computational results indicate that the new integrated framework of the MA outperforms the state-of-the-art leveling heuristics and meta-heuristics and provides a successful method for the RLP. The limitations of popular commercial project management software are also illustrated along with the improvements achieved by the MA to reveal potential contributions of the proposed integrated framework in practice.

The transportation demands of people are increasing day by day depending on the population, and the number of vehicles in traffic is causing various problems. To meet the energy needs of vehicles, there is a huge burden on countries in terms of fossil fuels. In addition, the use of fossil fuels in vehicles has a serious impact on environmental pollution. Various studies have been carried out to prevent unnecessary fuel consumption and emissions. Behavior of drivers, who are important components of traffic, are carefully examined in the context of this subject. Driver distraction causes various environmental problems as well as traffic safety issues. In this study, the negative situations that arise as a result of drivers waiting at traffic lights dealing with their mobile phones are discussed. Roadside observations are made for drivers at considered intersections in Erzurum Province, Turkey. As a result of these observations, delays at selected intersections due to mobile phone use are calculated. Unnecessary fuel consumption and emissions due to delays are also analyzed. An annual fuel consumption of approximately 177.025 L and emissions of 0.294 (kg) NO_X and 251.68 (kg) CO₂ occur at only selected intersections. In addition, a second roadside observation is made in order to analyze driver behavior and the most preferred type of mobile phone usage is determined. It is seen that drivers mostly exhibit the “Talking” and “Touchscreen” action classes. Considering the economic conditions and environmental pollution sensitivities of countries, attempts have been made to raise awareness about fuel consumption and emissions at traffic lights.

Conceptual cost estimation is an important step in project feasibility decisions when there is not enough information on detailed design and project requirements. Methods that enable quick and reasonably accurate conceptual cost estimates are crucial for achieving successful decisions in the early stages of construction projects. For this reason, numerous machine learning methods proposed in the literature that use different learning mechanisms. In recent years, the case-based reasoning (CBR) method has received particular attention in the literature for conceptual cost estimation of construction projects that use similarity-based learning principles. Despite the fact that CBR provides a powerful and practical alternative for conceptual cost estimation, one of the main criticisms about CBR is its low prediction performance when there is not a sufficient number of cases. This paper presents a bootstrap aggregated CBR method for achieving advancement in CBR research, particularly for conceptual cost estimation of construction projects when a limited number of training cases are available. The proposed learning method is designed so that CBR can learn from a diverse set of training data even when there are not a sufficient number of cases. The performance of the proposed bootstrap aggregated CBR method is evaluated using three data sets. The results revealed that the prediction performance of the new bootstrap aggregated CBR method is better than the prediction performance of the existing CBR method. Since the majority of conceptual cost estimates are made with a limited number of cases, the proposed method provides a contribution to CBR research and practice by improving the existing methods for conceptual cost estimating.

Summary: In this article, we present a parallel graphical processing unit (GPU)‐based genetic algorithm (GA) for solving the resource‐constrained multi‐project scheduling problem (RCMPSP). We assumed that activity pre‐emption is not allowed. Problem is modeled in a portfolio of projects where precedence and resource constraints affect the portfolio duration. We also assume that the durations, availability of resources are deterministic and portfolio has a static nature. The objective in this article is to find a start time for each activity of the project so that the portfolio duration is minimized, while satisfying precedence relations and resource availabilities within a reasonable amount of time for small and large problem instances. In order to compare the efficiency of the proposed parallel GPU‐based GA, problem is solved together with a CPU and a GPU. The results showed that GPU‐based parallel GA has high potential for improving the performance of GAs for the RCMPSP particularly, for large‐scale problems.