Abstract
Sustainable concrete mix design requires balancing mechanical performance with environmental and resource objectives, a multi-objective problem traditionally addressed through heuristic or trial-and-error methods. This study presents a surrogate-assisted multi-objective genetic optimization framework to model sustainable concrete design as a multi-player game, where compressive strength, CO2 emissions, energy consumption, and material usage are treated as rational players. Random Forest regressors serve as surrogate models for each objective, enabling efficient exploration of the design space. Two optimization approaches are compared: a weighted-fitness genetic algorithm (GA) and a NSGA-II-based multi-objective NSGA-II knee-point optimization with knee-point detection. The GA produces a compromise mix emphasizing strength, but at the cost of environmental and resource objectives, achieving 141.3 kg/cm2 strength, 433.1 kg CO2, and 2373.7 kWh energy. In contrast, the NSGA identifies a Pareto-optimal knee-point mix yielding 478.7 kg/cm
Key Words
CO2 Emission; Environment; Game Theory; Genetic Algorithm (GA); Multi-Objectives Optimization; Nash-Type Compromise Solution; Nsga-ii; Random Forest (RF); Sustainable Concrete Mixture
Address
(1) Razan Haedar Al Marahla:
Civil Engineering Department, Al-Zaytoonah University of Jordan, Amman, Jordan;
(2) Mohammad Zakaria Masoud:
Electrical Engineering Department, Al-Zaytoonah University of Jordan, Amman, Jordan;
(3) Ayman Mohammad Nasir:
Mechanical Engineering Department, Al-Zaytoonah University of Jordan, Amman, Jordan.
Abstract
Since traditional methods for detecting surface bubble defects usually suffer from drawbacks such as slow speed and low accuracy, a new You Only Look Once version 5s (YOLOv5s) model, incorporating Slim-neck and simple parameter-free attention module (SimAM), is proposed to address this challenge. Initially, data augmentation strategies are employed to expand the original dataset, which comprises 1,835 images, into a new dataset of 7,340 photographs, thereby enhancing the generalizability of the newly proposed YOLOv5s model. Subsequently, a lightweight Slim-Neck network, grounded in a group shuffling convolution (GSConv) module, is integrated into the backbone network to reduce the complexity of the newly proposed YOLOv5s model while maintaining a high level of target detection accuracy. Furthermore, a SimAM mechanism is embedded within the neck network, enabling the refined model to focus more intensively on the key characteristics of surface bubbles in fair-faced concrete. To validate the effectiveness and accuracy of the proposed YOLOv5s model, a dataset including 7,340 bubble defect images is investigated. The results demonstrate that the proposed model successfully strikes a commendable balance between the detection speed and accuracy. Additionally, it offers a promising and effective tool for detecting surface bubbles in real-world fair-faced concrete structures.
Address
(1) Shi-Han Xu, Jing-Liang Liu:
College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China;
(2) Shi-Han Xu:
College of Civil Engineering, Fuzhou University, Fuzhou 350108, China;
(3) Wen-Ting Zheng:
College of Civil Engineering, Fujian University of Technology, Fuzhou 350118, China;
(4) Zi-Xuan Chen:
Faculty of Science and Technology, University of Macau, Macau, China.
