Skip to main content
Top

2025 | OriginalPaper | Chapter

Machine Learning for the Analysis of Equipment Sensor Data in Road Construction Projects

Authors : Raquel Silva, Hugo Fernandes, José Neves, Manuel Parente

Published in: Proceedings of the 5th International Conference on Transportation Geotechnics (ICTG) 2024, Volume 1

Publisher: Springer Nature Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

New trends in digitalization in construction have created opportunities for research and informed decision-making. Concepts like digital twins and sensorization have successfully enabled the direct collection of data from construction processes and equipment. For instance, integrating sensors into trucks transporting construction materials facilitates gathering valuable information about the equipment and the surrounding environment. This previously unattainable data can now be utilized to provide pertinent insights into the decision-making process. On one hand, accurate fuel consumption estimations are required to help optimization in construction and transportation infrastructure projects as they represent a major expense. On the other hand, despite the numerous studies conducted to detect cracks and potholes in road pavements, the classification of road types is frequently overlooked. This study aims to bridge this gap by developing a methodological framework that utilizes vibration data from sensors installed in construction trucks to predict the fuel consumption of heavy vehicles and the road category based on the pavement surface quality through which it is circulated. Given their promising results in prior research, the models Random Forest, Neural Network, and Support Vector Machine were applied to the database. The results demonstrate that vibration-based data acquisition methods combined with machine learning algorithms can accurately predict fuel consumption, identify different road categories, and can be successfully applied on a larger scale.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Qiao Y, Dawson AR, Parry T, Flintsch G, Wang W (2020) Flexible pavements and climate change: a comprehensive review and implications. Sustainability 12(3):1057CrossRef Qiao Y, Dawson AR, Parry T, Flintsch G, Wang W (2020) Flexible pavements and climate change: a comprehensive review and implications. Sustainability 12(3):1057CrossRef
2.
go back to reference Wester A (2023) Utilizing artificial intelligence and machine learning for monitoring and modeling road conditions Wester A (2023) Utilizing artificial intelligence and machine learning for monitoring and modeling road conditions
3.
go back to reference Kong W, Zhong T, Mai X, Zhang S, Chen M, Lv G (2022) Automatic detection and assessment of pavement marking defects with street view imagery at the city scale. Remote Sens 14(16):4037CrossRef Kong W, Zhong T, Mai X, Zhang S, Chen M, Lv G (2022) Automatic detection and assessment of pavement marking defects with street view imagery at the city scale. Remote Sens 14(16):4037CrossRef
4.
go back to reference Perrotta F, Parry T, Neves LC (Dec 2017) Application of machine learning for fuel consumption modelling of trucks. In: 2017 IEEE international conference on big data (big data). IEEE, pp 3810–3815 Perrotta F, Parry T, Neves LC (Dec 2017) Application of machine learning for fuel consumption modelling of trucks. In: 2017 IEEE international conference on big data (big data). IEEE, pp 3810–3815
5.
go back to reference Pereira G, Parente M, Moutinho J, Sampaio M (2021) Fuel consumption prediction for construction trucks: a noninvasive approach using dedicated sensors and machine learning. Infrastructures 6(11):157CrossRef Pereira G, Parente M, Moutinho J, Sampaio M (2021) Fuel consumption prediction for construction trucks: a noninvasive approach using dedicated sensors and machine learning. Infrastructures 6(11):157CrossRef
6.
go back to reference Viswanathan A (2013). Data driven analysis of usage and driving parameters that affect fuel consumption of heavy vehicles Viswanathan A (2013). Data driven analysis of usage and driving parameters that affect fuel consumption of heavy vehicles
7.
go back to reference Amândio AM, das Neves JMC, Parente M (2021) Intelligent planning of road pavement rehabilitation processes through optimization systems. Transp Eng 5:100081 Amândio AM, das Neves JMC, Parente M (2021) Intelligent planning of road pavement rehabilitation processes through optimization systems. Transp Eng 5:100081
8.
go back to reference Boggio-Marzet A, Monzon A, Rodriguez-Alloza AM, Wang Y (2022) Combined influence of traffic conditions, driving behavior, and type of road on fuel consumption. real driving data from Madrid area. Int J Sustain Transp 16(4):301–313 Boggio-Marzet A, Monzon A, Rodriguez-Alloza AM, Wang Y (2022) Combined influence of traffic conditions, driving behavior, and type of road on fuel consumption. real driving data from Madrid area. Int J Sustain Transp 16(4):301–313
9.
go back to reference Almér H (2015) Machine learning and statistical analysis in fuel consumption prediction for heavy vehicles Almér H (2015) Machine learning and statistical analysis in fuel consumption prediction for heavy vehicles
10.
go back to reference Pereira V, Tamura S, Hayamizu S, Fukai H (2018) Classification of paved and unpaved road image using convolutional neural network for road condition inspection system. In: 2018 5th international conference on advanced informatics: concept theory and applications (ICAICTA). IEEE, pp 165–169 Pereira V, Tamura S, Hayamizu S, Fukai H (2018) Classification of paved and unpaved road image using convolutional neural network for road condition inspection system. In: 2018 5th international conference on advanced informatics: concept theory and applications (ICAICTA). IEEE, pp 165–169
11.
go back to reference Sampaio MOVP (2021) Sensorização e machine learning para a previsão de consumo de combustível. Master’s thesis, Instituto Superior de Engenharia do Porto, ISEP Sampaio MOVP (2021) Sensorização e machine learning para a previsão de consumo de combustível. Master’s thesis, Instituto Superior de Engenharia do Porto, ISEP
Metadata
Title
Machine Learning for the Analysis of Equipment Sensor Data in Road Construction Projects
Authors
Raquel Silva
Hugo Fernandes
José Neves
Manuel Parente
Copyright Year
2025
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-97-8213-0_24