Foi publicado o artigo “Fuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine Learning” da co-autoria de Gonçalo Pereira, Manuel Parente e João Moutinho, do BUILT CoLAB, e de Manuel Sampaio, aluno de mestrado do ISEP.
Este artigo faz agora parte do Special Issue “Artificial Intelligence in Infrastructure Geotechnics” da revista Infrastructures, uma revista científica internacional, de acesso aberto e revista por pares, sobre infra-estruturas e que é publicada mensalmente pelo MDPI
Resumo do Artigo:
«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.»
Consulte o artigo completo aqui.