Automated Estimation of Construction Equipment Emission using Inertial Sensors and Machine Learning Models

TL;DR

This paper presents a novel machine learning framework using inertial sensors and IoT data to accurately estimate construction equipment emissions, aiding environmental management in construction projects.

Contribution

It introduces a new IoT-based framework employing ML models to predict construction equipment emissions, validated with real-world data and showing high accuracy.

Findings

Random Forest achieved R2 of 0.94 for CO and CO2 emissions.

The framework effectively predicts multiple pollutant levels from inertial sensor data.

The approach enables real-time emission monitoring for construction equipment.

Abstract

The construction industry is one of the main producers of greenhouse gasses (GHG). Quantifying the amount of air pollutants including GHG emissions during a construction project has become an additional project objective to traditional metrics such as time, cost, and safety in many parts of the world. A major contributor to air pollution during construction is the use of heavy equipment and thus their efficient operation and management can substantially reduce the harm to the environment. Although the on-road vehicle emission prediction is a widely researched topic, construction equipment emission measurement and reduction have received very little attention. This paper describes the development and deployment of a novel framework that uses machine learning (ML) methods to predict the level of emissions from heavy construction equipment monitored via an Internet of Things (IoT) system comprised of accelerometer and gyroscope sensors. The developed framework was validated using an excavator performing real-world construction work. A portable emission measurement system (PEMS) was employed along with the inertial sensors to record data including the amount of CO, NOX, CO2, SO2, and CH4 pollutions emitted by the equipment. Different ML algorithms were developed and compared to identify the best model to predict emission levels from inertial sensors data. The results showed that Random Forest with the coefficient of determination (R2) of 0.94, 0.91 and 0.94 for CO, NOX, CO2, respectively was the best algorithm among different models evaluated in this study.