Work

Mountain Gazelle Optimization algorithm for efficient rebar cutting: balancing material cost and waste in construction projects

Year: 2025

Type: article

Abstract: Rebar is one of the primary materials in the construction industry, contributing significantly to the total material cost of projects. To meet structural design requirements, standard rebar bars must be cut into specific lengths, which inevitably results in steel waste and is a major source of material wastage in construction. This study proposes the application of a mordern optimization algorithm, the Mountain Gazelle Optimization (MGO), which is part of the swarm intelligence algorithms, to the stage of planning optimal cutting schemes for various rebar layout options. The goal is to optimize the total cost of raw materials and reduce material waste, providing an optimal solution for decision-making and contributing to the sustainable development of the industry. To evaluate the effectiveness of the MGO algorithm, this study used the results of previous research as benchmarks and applied them to real-world project rebar requirements. The results indicate that the proposed algorithm outperforms existing algorithms, affirming the competitive advantage of MGO in solving the rebar cutting optimization problem

Source: KSCE Journal of Civil Engineering

DOI: 10.1016/j.kscej.2025.100322

Authors: Vu Hong Son Pham, Thi Thanh Hai Truong

Institutions: Vietnam National University Ho Chi Minh City, Ho Chi Minh City University of Technology

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Responsible consumption and production

Open Access status: gold

Estimated APC paid: $1,800