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

Abstract

The construction industry is one of the main producers of greenhouse gasses (GHG). With the looming consequences of climate change, sustainability measures including quantifying the amount of air pollution during a construction project have become an important project objective in the construction industry. A major contributor to air pollution during construction projects is the use of heavy equipment. Therefore, efficient operation and management can substantially reduce a project’s carbon footprint and other environmental harms. Using unintrusive and indirect methods to predict on-road vehicle emissions has been a widely researched topic. Nevertheless, the same is not true in the case of construction equipment. This paper describes the development and deployment of a framework that uses machine learning (ML) methods to predict the level of emissions from heavy construction equipment. Data is collected via an Internet of Things (IoT) approach with accelerometer and gyroscope sensors as data collection nodes. The developed framework was validated using an excavator performing real-world construction work. A portable emission measurement system (PEMS) was used along with the inertial sensors to record the amount of CO, NOX, CO2, SO2, and CH4 pollution 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 show that Random Forest with the coefficient of determination (R2) of 0.94, 0.91, and 0.94, and normalized root-mean-square error (NRMSE) of 4.25, 6.42, and 5.17 for CO, NOX, and CO2, respectively, was the best algorithm among different models evaluated in this study.

A TinyML Soft-Sensor Approach for Low-Cost Detection and Monitoring of Vehicular Emissions

“…Rapid urban growth in the last decades has induced important concerns about air quality, especially in large cities [ 33 , 34 ]. This is due to the fact that air pollution may result in respiratory and cardiovascular diseases [ 35 , 36 , 37 ], which has demanded new solutions to monitor and even control the emission of pollutants in the air.…”

Overview of Emerging Technologies for Improving the Performance of Heavy-Duty Construction Machines

“…Tracking the operational efficiency of construction machinery improves productivity and sustainability [47]. Automated assessment of construction equipment efficiency offers crucial information for finding ways to boost productivity and reduce environmental impact [2].…”

“…This can be achieved by evaluating data patterns as inputs and outputs to develop a quantitative evaluation [1]. Models based on such strategies are often data-driven [47]. The data can be used to train a sophisticated prototype model that can understand the complicated links between different input factors and provide relevant output to assist engineers in improving their design processes [3].…”

“…Fuzzy set theory [87], as well as machine learning models such as artificial neural networks (ANNs) [88], [89], random forests (RF) [90], support vector machines (SVM) [91], and Bayesian networks (BN) [92], [93], [94], have been proposed in recent times to forecast and evaluate the risks of deep excavation. ML methods can also be anticipated in challenging situations, such as construction accidents, and assist management in making better choices [47]. In general, machine learning algorithms based on data collected from fieldwork are often more effective at predicting hazards in excavations [91], [92], [94].…”

“…Similarly, by integrating GPS, telematics, and web applications to improve the operational performance of site equipment, [110] determined the productivity of site equipment and estimated its sustainability impact using an accelerometer. An IoT-ML-based framework was developed in [47] to determine the emission levels of an excavator. To calculate cycle times, a machine learning approach was used by [111] to simulate the loading process by classifying raw data collected through GPS, gyroscope, and accelerometer sensors and incorporating them into a smart system placed in front of the loader.…”