Fuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine Learning
| Autor: | Manuel Sampaio, Gonçalo Pereira, Manuel Parente, João Moutinho |
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| Rok vydání: | 2021 |
| Předmět: |
Truck
IoT Technology Decision support system Computer science Context (language use) Machine learning computer.software_genre construction machinery remote monitoring fuel consumption Data logger Synchronization (computer science) Wireless General Materials Science Civil and Structural Engineering Estimation business.industry Building and Construction Geotechnical Engineering and Engineering Geology Computer Science Applications machine learning Fuel efficiency Artificial intelligence business computer cyberphysical systems |
| Zdroj: | Infrastructures, Vol 6, Iss 157, p 157 (2021) Infrastructures Volume 6 Issue 11 |
| ISSN: | 2412-3811 |
| Popis: | 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 datalogger 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. |
| Databáze: | OpenAIRE |
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