Fuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine Learning

Decision support and optimization tools to be used in construction often require an accurate estimation of the cost variables to maximize their benefit. Heavy machinery is traditionally one of the greatest costs to consider mainly due to fuel consumption. These typically diesel-powered machines have...

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Autores principales:	Gonçalo Pereira, Manuel Parente, João Moutinho, Manuel Sampaio
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	cyberphysical systems IoT machine learning construction machinery remote monitoring fuel consumption Technology T
Acceso en línea:	https://doaj.org/article/374396ad00444268b00a5ed108436096
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spelling	oai:doaj.org-article:374396ad00444268b00a5ed1084360962021-11-25T17:58:51ZFuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine Learning10.3390/infrastructures61101572412-3811https://doaj.org/article/374396ad00444268b00a5ed1084360962021-11-01T00:00:00Zhttps://www.mdpi.com/2412-3811/6/11/157https://doaj.org/toc/2412-3811Decision support and optimization tools to be used in construction often require an accurate estimation of the cost variables to maximize their benefit. Heavy machinery is traditionally one of the greatest costs to consider mainly due to fuel consumption. These typically diesel-powered machines have a great variability of fuel consumption depending on the scenario of utilization. This paper describes the creation of a framework aiming to estimate the fuel consumption of construction trucks depending on the carried load, the slope, the distance, and the pavement type. Having a more accurate estimation will increase the benefit of these optimization tools. The fuel consumption estimation model was developed using Machine Learning (ML) algorithms supported by data, which were gathered through several sensors, in a specially designed <i>datalogger</i> with wireless communication and opportunistic synchronization, in a real context experiment. The results demonstrated the viability of the method, providing important insight into the advantages associated with the combination of sensorization and the machine learning models in a real-world construction setting. Ultimately, this study comprises a significant step towards the achievement of IoT implementation from a Construction 4.0 viewpoint, especially when considering its potential for real-time and digital twins applications.Gonçalo PereiraManuel ParenteJoão MoutinhoManuel SampaioMDPI AGarticlecyberphysical systemsIoTmachine learningconstruction machinery remote monitoringfuel consumptionTechnologyTENInfrastructures, Vol 6, Iss 157, p 157 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cyberphysical systems IoT machine learning construction machinery remote monitoring fuel consumption Technology T
spellingShingle	cyberphysical systems IoT machine learning construction machinery remote monitoring fuel consumption Technology T Gonçalo Pereira Manuel Parente João Moutinho Manuel Sampaio Fuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine Learning
description	Decision support and optimization tools to be used in construction often require an accurate estimation of the cost variables to maximize their benefit. Heavy machinery is traditionally one of the greatest costs to consider mainly due to fuel consumption. These typically diesel-powered machines have a great variability of fuel consumption depending on the scenario of utilization. This paper describes the creation of a framework aiming to estimate the fuel consumption of construction trucks depending on the carried load, the slope, the distance, and the pavement type. Having a more accurate estimation will increase the benefit of these optimization tools. The fuel consumption estimation model was developed using Machine Learning (ML) algorithms supported by data, which were gathered through several sensors, in a specially designed <i>datalogger</i> with wireless communication and opportunistic synchronization, in a real context experiment. The results demonstrated the viability of the method, providing important insight into the advantages associated with the combination of sensorization and the machine learning models in a real-world construction setting. Ultimately, this study comprises a significant step towards the achievement of IoT implementation from a Construction 4.0 viewpoint, especially when considering its potential for real-time and digital twins applications.
format	article
author	Gonçalo Pereira Manuel Parente João Moutinho Manuel Sampaio
author_facet	Gonçalo Pereira Manuel Parente João Moutinho Manuel Sampaio
author_sort	Gonçalo Pereira
title	Fuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine Learning
title_short	Fuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine Learning
title_full	Fuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine Learning
title_fullStr	Fuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine Learning
title_full_unstemmed	Fuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine Learning
title_sort	fuel consumption prediction for construction trucks: a noninvasive approach using dedicated sensors and machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/374396ad00444268b00a5ed108436096
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Fuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine Learning

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