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Deep and machine learning approaches for forecasting the residual value of heavy construction equipment: a management decision support model

Odey Alshboul (Civil Engineering, The Hashemite University, Zarqa, Jordan)
Ali Shehadeh (Civil Engineering, Yarmouk University, Irbid, Jordan)
Maha Al-Kasasbeh (Civil Engineering, The Hashemite University, Zarqa, Jordan)
Rabia Emhamed Al Mamlook (Industrial Engineering and Engineering Management, Western Michigan University, Kalamazoo, Michigan, USA) (Mechanical Engineering, Al-Zawiyah University, Zawia, Libya, USA)
Neda Halalsheh (Civil Engineering, The Hashemite University, Zarqa, Jordan)
Muna Alkasasbeh (Central Michigan University, Mt. Pleasant, Michigan, USA)

Engineering, Construction and Architectural Management

ISSN: 0969-9988

Article publication date: 8 September 2021

Issue publication date: 7 December 2022

307

Abstract

Purpose

Heavy equipment residual value forecasting is dynamic as it relies on the age, type, brand and model of the equipment, ranking condition, place of sale, operating hours and other macroeconomic gauges. The main objective of this study is to predict the residual value of the main types of heavy construction equipment. The residual value of heavy construction equipment is predicted via deep learning (DL) and machine learning (ML) approaches.

Design/methodology/approach

Based on deep and machine learning regression network integrated with data mining, random forest (RF), decision tree (DT), deep neural network (DNN) and linear regression (LR)-based modeling decision support models are developed. This research aims to forecast the residual value for different types of heavy construction equipment. A comprehensive investigation of publicly accessible auction data related to various types and categories of construction equipment was utilized to generate the model's training and testing datasets. In total, four performance metrics (i.e. the mean absolute error (MAE), mean squared error (MSE), the mean absolute percentage error (MAPE) and coefficient of determination (R2)) were used to measure and compare the developed algorithms' accuracy.

Findings

The developed algorithm's efficiency has been demonstrated by comparing the deep and machine learning predictions with real residual value. The accuracy of the results obtained by different proposed modeling techniques was comparable based on the performance evaluation metrics. DT shows the highest accuracy of 0.9111 versus RF with an accuracy of 0.8123, followed by DNN with an accuracy of 0.7755 and the linear regression with an accuracy of 0.5967.

Originality/value

The proposed novel model is designed as a supportive tool for construction project managers for equipment selling, purchasing, overhauling, repairing, disposing and replacing decisions.

Keywords

Citation

Alshboul, O., Shehadeh, A., Al-Kasasbeh, M., Al Mamlook, R.E., Halalsheh, N. and Alkasasbeh, M. (2022), "Deep and machine learning approaches for forecasting the residual value of heavy construction equipment: a management decision support model", Engineering, Construction and Architectural Management, Vol. 29 No. 10, pp. 4153-4176. https://doi.org/10.1108/ECAM-08-2020-0614

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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