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, 29(10), pp. 4153-4176. ISSN 0969-9988
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 (Formula presented.)) 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | construction; construction equipment; construction planning |
| Index terms: | deep learning, purchasing, construction equipment, forecasting, efficiency, data mining, construction planning, modelling, heavy construction, testing, decision tree, investigation, accuracy, neural network, decision support, forest, manager, performance metric, performance evaluation, machine learning, dataset, management decision, methodology, construction project |
| Subjects: | procurement strategy, infrastructure and transport systems, data management, decision analysis, production management, construction equipment, professional development, performance measurement, performance management, decision-making and reasoning, research methods, environmental science, artificial intelligence, data science, professional practice, prediction and forecasting, construction planning, analytical methods, practitioner, data collection methods |
| Topics: | Roles and Professions, Risk Management, Plant and Equipment, Sustainability, Procurement, Information Management, Engineering Principles, Project Management, Research Practice, Site Management, Digital Applications, Quality Management |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here