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19 →- ArticleYalnız künye2026A novel multi-objective optimization model for the time–cost–environmental impact trade-off in construction projects using a strength Pareto-based whale optimization algorithm
Yılmaz, Mehmet; Dede, Tayfun
The construction industry, a key contributor to global economic development, significantly impacts environmental pollution through greenhouse gas emissions. While conventional construction project management prioritizes cost and time reduction, environmental considerations are increasingly critical. This study addresses the time-cost-environmental impact trade-off problem by introducing a strength Pareto-based whale optimization algorithm (SP2WOA). The algorithm enhances solution diversity and achieves well-distributed Pareto fronts by integrating strength Pareto operators with the whale optimization algorithm. Its effectiveness is demonstrated through three diverse construction projects. Compared to previous algorithms (non-dominated sorting genetic algorithm-II, opposition-based multi-objective differential evolution, golden ratio-based opposite teaching-learning-based optimization and enhanced multi-objective grasshopper optimization algorithm), SP2WOA achieves superior balances between time, cost and environmental impact, using 33-60% fewer function evaluations. Statistical analysis using Wilcoxon rank tests confirms SP2WOA's superiority in both hypervolume and spread metrics. The results demonstrate that SP2WOA offers a valuable tool for sustainable construction project management.
- ArticleYalnız künye2026Multi-objective time-cost-safety risk trade-off optimization for the construction scheduling problem
Yılmaz, Mehmet; Dede, Tayfun
Purpose – The purpose of this study is to enable the planning of construction projects with simultaneous consideration of time, cost and safety risks. It also aims to improve the decision-making process by evaluating the effectiveness of the Rao-2 algorithm in solving multi-objective time-cost-safety risk problems. In the end, this model is designed to support project managers in enhancing management approaches by addressing project challenges and constraints more efficiently. Design/methodology/approach – In this study, the Rao-2 algorithm, along with Grey Wolf Optimization (GWO) and Whale Optimization algorithm (WOA), were improved using the crowding distance-based non-dominated sorting method. Rao-2 was first compared to GWO and WOA. Subsequently, it was compared with well-established algorithms in the literature, including genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE). The C-metric, hypervolume and spread metrics were employed for performance measurement. The performance of the algorithms was evaluated on four case studies consisting of 11, 13, 18 and 25 activities. Findings – The results revealed that Rao-2 performs better than other algorithms as the number of activities increases, when compared using the Hypervolume, Spread and C-metric measures. In terms of performance measures, the GWO algorithm outperformed Rao-2 in some evaluation metrics for the instance involving 11 activities. However, as the number of activities grew, the Rao-2 method consistently generated higher-quality Pareto fronts and outperformed GWO and WOA in all evaluation metrics. The solutions generated by Rao-2 were also superior to those obtained from GA, PSO and DE in all case studies, further demonstrating the capability of our framework to produce a wide range of optimal solutions with high diversity across different case studies. Originality/value – This research demonstrates that Rao-2 not only improves solution quality when generating Pareto fronts but also achieves better results with fewer function evaluations compared to GA, PSO and DE. The algorithm's efficiency makes it particularly well-suited for optimizing time, cost and safety risks in large-scale construction projects, which in turn positions Rao-2 as a better choice for such projects by producing superior results compared to other algorithms. By providing high-quality solutions with reduced computational demands, Rao-2 offers a faster and more resource-efficient tool for decision-making, contributing to advancements in both the theory and practice of construction project management.
- Conference ObjectYalnız künye2026Application of Arithmetic Optimization Algorithm (AOA) for Time–Cost–Quality–Environmental Impact Optimization in Construction Projects
Yılmaz, Mehmet; Dede, Tayfun; Rao, Ravipudi Venkata
This study aims to investigate the relationship between time, cost, quality, and environmental impact in construction projects using arithmetic optimization algorithm. Project managers have prioritized time and cost concepts in traditional project planning. It is seen that other factors affecting the success of the project are ignored. However, with the impact of technological and sociocultural changes, awareness of sustainability and environmental factors has increased. For this reason, there are studies showing that environmental impact is taken into account at least as much as time, cost, and quality in project management. Therefore, this study evaluates the performance of the arithmetic optimization algorithm (AOA) on the many-objective time–cost–quality–environmental impact problem. The performance of the AOA was compared with the adaptive selection slime mold algorithm (ASSMA), which is known to give good results in the literature. The results show that the AOA outperforms the ASSMA in terms of cost, environmental impact, and quality with 25% less function evaluation.
- Book ChapterYalnız künye2026Optimization of Time–Cost-Environmental Impact Problems in Construction Projects with Grey Wolf Algorithm
Dede, Tayfun; Yılmaz, Mehmet
In the conventional construction project management, the main focus is generally on the completion time and budget of the project. One of the main objectives of many decision-makers is to complete the project quickly and stay within the specified cost. However, the concepts of sustainability and environmental impact have recently gained prominence on a global scale, which has necessitated greater consideration of the environmental impacts of construction projects. Various scenarios and alternatives are generated during the project planning phase and each of these alternatives has different impacts on the total duration, cost and environmental impact of the project. Therefore, it has become critical for decision makers to identify the most appropriate option. The options selected for the activities can directly affect the overall success, efficiency and sustainability of the project. In the literature, many optimization algorithms have been proposed to solve such multi-objective problems. In this study, the effect of the grey wolf optimization algorithm, inspired by the hunting behavior of grey wolves in nature, on multi-objective time–cost-environmental impact problems encountered in the planning phase of construction projects is investigated. The performance and efficiency of the algorithm are compared with the algorithms in the literature.
- ArticleAçık Erişim2026A data-driven approach to building design and contract pricing using explainable machine learning
Aslay, Semi Emrah
This study aims to quantify architectural design features and predict and explain their relationship with contract prices. This work focuses on the random forest algorithm, one of the machine learning algorithms. This algorithm is enhanced through parameter optimizations and compared with Shapley Additive Explanations (SHAP) to measure each architectural feature's contribution to the model's prediction. The combined evaluation of feature importance and SHAP significantly enhanced the model's interpretability. The results showed that the roof area and the number of doors had a significant impact on project costs among the building's architectural features. The performance evaluation metric outputs obtained, with R2 at 0.8725, NSE at 0.8709, MAE at 0.6510, and RMSE at 0.9615, align with the developed prediction model. The study reveals the relationship between contract costs and their sub-variables using predictive models and explainable machine learning methods to build architectural features.
- ArticleYalnız künye2025Optimizing Multiobjective Time-Cost-Quality Problems in Construction Projects: Efficacy of Strength Pareto-Based Rao Algorithms
Yilmaz, Mehmet; Dede, Tayfun
In construction project management, the optimization of time, cost, and quality trade-off is essential for maximizing overall project benefits. While various algorithms have been proposed to address this multiobjective optimization problem, there remains a significant research gap in the efficiency and effectiveness of selection operators in guiding the search process toward optimal solutions. Additionally, many existing methods are computationally expensive, making them impractical for large-scale projects. Therefore, this study proposes use of strength Pareto-based Rao algorithms (SP2-Rao-1 and -2) as an approach to solving multiobjective time-cost-quality problems in construction projects with better optimization performance at lower computation costs. Furthermore, to explore the impact of the selection operators that determine the candidates for subsequent iterations in multiobjective optimization tasks, Rao algorithms with nondominated sorting and a crowding distance operator (NDSII-Rao-1 and -2) were also implemented. The performance of the strength Pareto-based Rao algorithms was evaluated using two case studies with 18 and 60 activities, respectively. The developed SP2-Rao series were compared with opposition-based multiple objective differential evolution (OMODE) and multiobjective artificial bee colony with differential evolution (MOABCDE) algorithms, which have previously been used to solve similar problems. The findings indicated that the SP2-Rao series surpassed earlier techniques in addressing multiobjective construction time-cost-quality problems, achieving greater efficiency and effectiveness with reduced function evaluations. Moreover, the SP2-Rao algorithms produced significantly better results compared to the NDSII-Rao algorithms.
- ArticleYalnız künye2025Integrated risk management in construction project: utilizing NDSII-BMR and NDSII-BWR for time-cost-safety trade-offs
Dede, Tayfun; Sin, Theint Shwe; Yılmaz, Mehmet; Uçan, Hacı Abdullah; Rao, Ravipudi Venkata
Purpose – This research introduces novel algorithms (NDS-II-BMR and NDS-II-BWR) that combine the simplicity of best-mean-random (BMR) and best-worst-random (BWR) approaches with the opting mechanisms of NDS-II. Design/methodology/approach – This study expands on earlier comparison assessments by adding a broader range of algorithms, including MOPSO, NSGAII, PESA2 and WOA, to provide a more comprehensive evaluation approach for the proposed NDSII-BMR and NDSII-BWR algorithms. In addition to typical performance measurements, this study utilizes sophisticated statistical validation through the Wilcoxon signed-rank test to determine the statistical significance of performance differences across this varied range of multi-objective optimization approaches. Findings – The results demonstrate the effectiveness of NDSII-BMR and NDSII-BWR in optimizing costs and decreasing safety risks while minimizing time efficiency. These outcomes underscore the applicability of these models for managing complex construction projects in real-world scenarios. Originality/value – This study employs multi-objective functions to minimize the total project time and cost while reducing overall safety risks. The proposed model integrates the NDS-II framework with innovative strategies, best-mean-random (BMR) and best-worst-random (BWR).
- Conference ObjectYalnız künye2025Jaya Algorithm for Optimization of 2D Reinforced Concrete Frame Structure
Grzywiński, Maksym; Dede, Tayfun; Atmaca, Barbaros; Aslay, Semi E.
Reinforced concrete skeleton frames are preferred for different sizes and types of structures, due to their features such as high strength, durability, and easy fabrication. This study aims to present the efficiency of the Jaya algorithm for determining the optimal design solution for two-dimensional reinforced concrete frame structures. A three-bay, and three-story frame structure is taken as a numerical example. Turkish standard has been taken into account in the design. The Jaya algorithm is a meta-heuristic that is capable of solving both constrained and unconstrained optimization problems. It is a population-based method that repeatedly modifies a population of individual solutions. It is a gradient-free optimization algorithm.
- ArticleYalnız künye2025Explaining building energy efficiency prediction through architectural and engineering solutions considering environmental impacts using a hybrid model
Aslay, Semi Emrah
The aim of this study is to investigate building energy efficiency by integrating hybrid modelling approaches and interpretable models into architectural design processes and engineering solutions while considering environmental impacts. At the same time, it is aimed to achieve the most efficient building design possible. Separate analyses were carried out using a total of 6,913 data from Rutland (185) and Salford (6718) cities. The data were grouped into carbon emission information, architectural information, lighting information, personal heating information, and main heating system information to form a dataset. Light Gradient Boosting Machine (LightGBM) was preferred as the base model and Particle Swarm Optimisation (PSO) method was applied for hyperparameter optimisation. The hybrid model created in this way is called PSO-LightGBM. The optimization process was carried out using software in both R Studio and Python environments, utilizing seven different hyperparameters. Apart from the hybrid model used as a method, 2 different SHAP analyses, neural network based, and tree based, were performed to clearly explain the parameter relationships. The PSO-LightGBM hybrid model provided more successful predictions compared to the basic LightGBM model. While R2 values improved between 0.82 and 0.90 in the Rutland dataset, this value increased from 0.8687 to 0.8901 for test data and from 0.8538 to 0.9091 for training data in the Salford dataset. R2 values show an improvement of 7% in the Rutland dataset and maximum 6% in the Salford dataset. When the reduction in error rates is evaluated, it is found that the greatest improvement is in the Mean Squared Error (MSE) metric. MSE decreased by 17% in the Rutland dataset and by 4% in the Salford dataset. According to the SHAP Analysis results, CO2 emissions have the largest impact on energy consumption, while primary fuel types, number heated rooms and individual heating systems are other important parameters. While the tree-based SHAP model is more sensitive to physical parameters, the neural network model is more sensitive to indirect relationships. In both analyses, the communal heating system type has the lowest impact. In order to improve building energy efficiency, high efficiency individual boiler systems should be preferred, architectural approaches that optimise the number of heated rooms and smart heating solutions should be used, and central heating systems should be modernised. The results highlight the effectiveness of hybrid modelling approaches with SHAP analyses based on different baselines to ensure the integration of environmental impacts, architectural design processes and engineering solutions in terms of building energy efficiency. Furthermore, the findings contribute to the importance of interdisciplinary work in buildings to improve energy efficiency. Future studies can focus on the development of building energy performance prediction models that can be explained by integrating various learning algorithms and different optimisation techniques, using regions with high cooling demand, large datasets and different building types.
- ArticleYalnız künye2025Building energy prediction model with AI-based PSO-ANN approach integrating architectural and HVAC processes
Aslay, Semi Emrah
Nowadays, due to the increasing energy demand, the importance of research on energy consumption estimation in buildings is increasing. This study aims to estimate and interpret energy consumption in buildings by integrating the processes of architecture and heating-ventilation-air conditioning (HVAC) components with an approach that combines environmental impact. For this purpose, the data were obtained from residential buildings in Nottingham and City of London, and the hybrid artificial neural network algorithm and Shapley Additive Explanations (SHAP) analyses were used as methods. To improve the performance of the Artificial Neural Network (ANN) model, a hybrid method was created with Particle Swarm Optimisation (PSO) (PSO-ANN). In order to ensure the interpretability of the model, both tree-based and neural network-based SHAP method were used. The outputs of this AI-supported method were compared and interpreted. The hybrid model developed in this study achieved an R-squared value of 0.99 in the test data sets in both data groups, which is a significant improvement compared to standard ANN models (0.95–0.97). In addition, when the training data is also included, the overall improvement rate is calculated as 3–5 %. According to the results of the analyses, CO₂ emission rate and total area are the factors that affect the energy consumption the most. In addition, the positive effects of heating control systems and lighting factors on energy efficiency are evaluated, and their importance is emphasized. These results provide an effective prediction model that combines building energy consumption and environmental performance and integrates the processes of architecture and HVAC components.
Tam Kayıt
| Alan | Değer | Dil |
|---|---|---|
| dc.date.accessioned | 2026-10-21T10:22:18.039971482Z | |
| dc.date.available | 2026-10-21T10:22:18.039971482Z | |
| dc.identifier.uri | https://rdlopen.rawdatalibrary.net/r/ebyu/28375 | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.title | İnşaat Bölümü | tr |
| dspace.entity.type | OrgUnit | |
| organization.identifier.code | myo-erzincan-insaat | |
| organization.unitType | department | |
| rdl.unit.alias | \binsaat bolumu\b |