Statistical Considerations and Graphical Presentation of the Residual Value of Heavy Construction Equipment

2007 ◽  
Author(s):  
Gunnar Lucko ◽  
John C. Hildreth ◽  
Michael C. Vorster
Keyword(s):  
Construction Equipment ◽  
Residual Value ◽  
Heavy Construction ◽  
Graphical Presentation

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

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Odey Alshboul ◽  
Ali Shehadeh ◽  
Maha Al-Kasasbeh ◽  
Rabia Emhamed Al Mamlook ◽  
Neda Halalsheh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Linear Regression ◽  
Performance Metrics ◽  
Construction Equipment ◽  
Percentage Error ◽  
Residual Value ◽  
Content Type ◽  
The Mean ◽  
Heavy Construction

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

scholarly journals Analyses of Heavy Construction Equipment Accidents and Safety Prevention

2018 ◽  
Vol 1 (1) ◽  
pp. 1136-1139
Author(s):  
Niyazi Bilim ◽  
Bilgehan Kekec ◽  
Atiye Bilim
Keyword(s):  
Occupational Accidents ◽  
Construction Equipment ◽  
Workplace Injuries ◽  
Cause And Effect ◽  
Security Technologies ◽  
Equipment Safety ◽  
Heavy Equipment ◽  
Heavy Construction

Equipment-related occupational accidents are very higher when compared to all other type occupational accidents in the worldwide. Various types and styles of many equipment are used in construction industries (construction and mining). This equipment are usually huge and heavy, so the consequences of accidents are severe. Occupational accidents related this equipment might occur due to operating faulty, carelessly and unserviceably. In this study, the causes of occupational accidents related with heavy equipment in construction workplaces are presented and cause-and-effect relationships of occupational accidents are investigated based on the statistics. Heavy equipment safety types are analysed for incident prevention by statistics. In addition, the detailed information are presented about the precautions to prevent such accidents. As a result, all stakeholders should pay attention to the use of new security technologies and to reduce human default to prevent workplace injuries depending on the equipment.

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