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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
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| Series: | Infrastructures |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2412-3811/6/11/157 |
| _version_ | 1797509915500085248 |
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| author | Gonçalo Pereira Manuel Parente João Moutinho Manuel Sampaio |
| author_facet | Gonçalo Pereira Manuel Parente João Moutinho Manuel Sampaio |
| author_sort | Gonçalo Pereira |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:24:30Z |
| format | Article |
| id | doaj.art-374396ad00444268b00a5ed108436096 |
| institution | Directory Open Access Journal |
| issn | 2412-3811 |
| language | English |
| last_indexed | 2024-03-10T05:24:30Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj.art-374396ad00444268b00a5ed1084360962023-11-22T23:46:05ZengMDPI AGInfrastructures2412-38112021-11-0161115710.3390/infrastructures6110157Fuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine LearningGonçalo Pereira0Manuel Parente1João Moutinho2Manuel Sampaio3BUILT CoLAB, 4150-003 Porto, PortugalBUILT CoLAB, 4150-003 Porto, PortugalBUILT CoLAB, 4150-003 Porto, PortugalISEP, 4200-072 Porto, PortugalDecision 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.https://www.mdpi.com/2412-3811/6/11/157cyberphysical systemsIoTmachine learningconstruction machinery remote monitoringfuel consumption |
| spellingShingle | Gonçalo Pereira Manuel Parente João Moutinho Manuel Sampaio Fuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine Learning Infrastructures cyberphysical systems IoT machine learning construction machinery remote monitoring fuel consumption |
| title | 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_short | 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 |
| topic | cyberphysical systems IoT machine learning construction machinery remote monitoring fuel consumption |
| url | https://www.mdpi.com/2412-3811/6/11/157 |
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