Identifying and Ranking the Factors Affecting Construction Labor Productivity Using Artificial Neural Network and Grasshopper Optimization Algorithm

Mohammadi Golafshani, Emadaldin; Goodarzizad, Payam; Falsafi Divband, Vahid

Identifying and Ranking the Factors Affecting Construction Labor Productivity Using Artificial Neural Network and Grasshopper Optimization Algorithm

Document Type : Original Article

Authors

Emadaldin Mohammadi Golafshani ¹

Payam Goodarzizad ²

Vahid Falsafi Divband ²

¹ Department of Civil Engineering, Islamic Azad University, Research and Science Branch, Tehran, Iran

² Islamic Azad University, Science and Research Branch, Tehran, Iran

Abstract

Construction industry plays a crucial role in Iran’s economy and any improvement in this industry will eventually lead to country’s economic development. On the other hand, the construction industry is mainly dependent on human resources that is one of the essential parts of every project costs. Consequently, identifying and ranking the factors affecting labor productivity can help the project managers reduce the effect of factors decreasing the productivity, which can reduce the project costs and guarantee the success of the project. In this paper, by reviewing the literature and consultation with experts, nineteen of the most important factors with huge impact on labor productivity with the focus on concrete pouring operations related to the building of commercial-office complex projects in Iran were identified. Moreover, a combined model based on artificial neural network (ANN) and Grasshopper optimization algorithm (GOA) was used for ranking them. As a result, labor experience and skill, motivation of labor, the amount of pay, site accidents, proper supervision, and weather condition were listed as the most influencing factors of the construction labor productivity.

Keywords

Labors productivity

Feature selection

Artificial neural network

Grasshopper optimization algorithm