http://yetl.yabesh.ir/yetl1/handle/yetl/19001 Sun, 26 Apr 2026 01:30:50 GMT 2026-04-26T01:30:50Z http://localhost:80/yetl1/bitstream/id/436304/ http://yetl.yabesh.ir/yetl1/handle/yetl/19001 http://yetl.yabesh.ir/yetl1/handle/yetl/4309379 Comparison of Machine-Learning Algorithms for Estimating Cost of Conventional and Accelerated Bridge Construction Methods during Early Design Phase Hadil Helaly; Khaled El-Rayes; Ernest-John Ignacio; Hee Jae Joan The use of accelerated bridge construction methods such as prefabricated bridge elements, lateral slide, and self-propelled modular transporter has increased in recent years to minimize on-site construction time and related traffic disruptions, and to improve safety, quality, and sustainability. This paper presents the development and evaluation of six novel machine-learning models for estimating the cost of conventional and accelerated bridge construction methods during the early design phase. The models were developed in four phases that focused on (1) collecting a data set of 413 conventional and accelerated bridge projects; (2) preprocessing the collected data to ensure its quality and reliability by identifying predicted and predictor variables, classifying predictor variables, cleaning data, transforming predictor variables, and splitting data into training and testing data sets; (3) training the models using ordinary least squares, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, random forest, gradient boosting, and extreme gradient boosting; and (4) evaluating and validating the performance of the developed models. The outcome of the validation phase showed that the extreme gradient boosting model outperformed the other machine-learning models in terms of the metrics mean absolute percentage error, mean absolute error, and median absolute error; and the gradient boosting model outperformed the other models in the metric root mean square error. The developed machine-learning models and their improved cost estimating accuracy are expected to provide much-needed support to bridge planners and enable them to accurately estimate, compare, and select the most cost-effective construction method for their planned bridge construction projects during the early design phase. Wed, 01 Jan 2025 00:00:00 GMT http://yetl.yabesh.ir/yetl1/handle/yetl/4309379 2025-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4309378 Flow Shop Scheduling for Prefabricated Components Production Considering Parallel Machines and Buffer Constraints Miao Yu; Wenxin Ruan; Ying Zhou; Yu Zhao This study introduces a novel scheduling approach to enhance the efficiency of prefabricated component production by integrating parallel equipment scheduling with intra- and inter-process constraints. It employs a homogeneous parallel equipment setup to maximize the productivity of individual production lines. The model further accounts for limited buffer spaces and prefabricated components’ release times. Additionally, it incorporates factors such as the expiration of release agents and concrete setting times, which are crucial for the practical application of the scheduling strategy. A numerical analysis, informed by a case study from a prefabricated factory in Shenyang, China, demonstrates that this approach substantially reduces production time. Specifically, the proposed scheduling strategy results in a 26.47% reduction in total time compared to traditional manual scheduling, with the incorporation of multiple buffer constraints improving the accuracy of scheduling predictions. The model addresses the impact of multiple production constraints on productivity to formulate more precise scheduling plans for prefabricated component production across diverse production environments. Wed, 01 Jan 2025 00:00:00 GMT http://yetl.yabesh.ir/yetl1/handle/yetl/4309378 2025-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4309376 Multiobjective Ant Colony System Algorithm for Component-Level Construction Schedule Optimization Zhaozheng Shen; Jie Wu Automatic generation of construction schedules has emerged as a key solution to address the inefficiencies and instabilities arising from the over-reliance on empirical judgment. However, traditional construction scheduling has been predominantly limited to regional levels, inadequately addressing the lean construction requirements of component-based prefabricated steel frame (PSF) structures. To bridge this gap, this study formulates an optimization model for the component-level resource-constrained project scheduling problem for PSF structures (C-RCPSP-PSF), which realizes the automatic extraction of precedence relationships from building information modeling three-dimensional (BIM 3D) models and the minimization of construction duration, costs, and carbon emissions. To address the C-RCPSP-PSF model, a novel multiobjective ant colony system (MOACS) algorithm is developed that utilizes three distinct colonies to individually tackle the objectives and combines taboo lists and global archives to enhance the search. Experimental results show the superior convergence and diversity of the MOACS over that of other competitive multiobjective optimization algorithms in solving the proposed model. Wed, 01 Jan 2025 00:00:00 GMT http://yetl.yabesh.ir/yetl1/handle/yetl/4309376 2025-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4309375 Multiobjective Trade-Off Optimization of Time, Cost, Quality, and Carbon Emission in the Building Construction Stage Haixin Wang; Xin Chen; Jiaqi Wang; Wenfeng Guan; Shengsong Wei For a long time, the production management of the construction industry has been mostly based on experience, and there are often problems such as construction delays, high costs, quality defects, and serious pollution. In order to solve these problems, this paper introduces the quantitative methods of time, cost, quality, and carbon emission in the construction stage, constructs an optimization model of duration, cost, quality, and carbon emissions, and uses the ant colony optimization algorithm to solve the problem. Finally, through model simulation, the Pareto optimal solution is obtained, which proves the effectiveness of the method proposed in this paper. The research results of this paper can provide a feasible solution for decision-makers at construction sites, guiding on-site construction to achieve better production efficiency. At the same time, it offers a reference for construction management under the premise of low carbon in the construction industry. In addition, the method for quantifying carbon emissions during the construction phase proposed in this study can also provide a theoretical reference for future research on carbon emissions in the construction sector. Currently, most research focuses on macro-level carbon emissions of buildings across their life cycle, with limited attention to emissions during construction. Therefore, this paper leverages multiobjective and combinatorial optimization theories, incorporates actual construction processes, quantifies subobjectives, formulates multiple execution modes for each construction process, and establishes a multimode optimization model for construction duration, cost, quality, and carbon emissions. The ant colony algorithm is then used to solve the problem. Through this method, a feasible construction plan can be provided to construction site managers, who can guide the construction according to the plan and improve the extensive management of construction sites to reduce resource waste. The optimization of the construction process that takes carbon emissions into account also provides practical guidance for the sustainable development of the construction industry and the achievement of China’s dual carbon goals. Wed, 01 Jan 2025 00:00:00 GMT http://yetl.yabesh.ir/yetl1/handle/yetl/4309375 2025-01-01T00:00:00Z