Barati, K; Shen, X; Li, N and Carmichael, D G (2022) Automatic mass estimation of construction vehicles by modeling operational and engine data. Journal of Construction Engineering and Management, 148(3): 4021208, ISSN 0733-9364
Abstract
Earthmoving operations employ heavy-duty vehicles, including trucks and scrapers, to transport soil and rock on and off construction sites. Such construction activities are commonly scheduled and paid for based on the amount of earth moved. A number of metric and volumetric tools and techniques, including weighbridges, load-volume scanners (LVS), and strain gauges, have been developed to measure the payload of vehicles. These methods are costly, time-consuming, and labor-intensive, and may affect the production rate and cost of construction projects. This study develops an automatic mass estimation technique for on-road construction vehicles considering both operational and engine data. Acceleration rate, speed, and road slope are investigated as the operational variables, while engine load is considered as an engine attribute to estimate vehicle mass. A global positioning system-Aided inertial navigation system (GPS-INS) and an engine data logger are integrated to collect the field data. Experiments are conducted on several construction vehicles to collect a wide range of data under various operational conditions. After assuring the quality of field data obtained in this study, artificial neural networks (ANNs) were developed to model the mass of construction equipment based on operational and engine parameters. The model was validated by comparing the estimated mass data with the actual values measured by a weighbridge in the experiment. The results show that the proposed model achieves greater than 90% accuracy in predicting the mass of on-road construction vehicles.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial neural network; earthmoving construction; engine load; experimental studies; mass; on-road equipment; operational parameters |
| Index terms: | construction project, duty, experiment, accuracy, slope, artificial neural network, construction site, road construction, acceleration, estimate, earthmoving, modelling, construction equipment, estimation, construction activity |
| Subjects: | data collection methods, contractual role, work location, modelling and simulation, construction operations, analytical methods, financial and cost management, civil engineering, production management, professional development, construction equipment, project controls, geotechnical engineering |
| Topics: | Cost Management, Research Practice, Project Management, Information Management, Engineering Principles, Plant and Equipment, Contract Administration, Time Control, Site Management |
| Descriptive scope: | 3 PCE |
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