Automated Productivity Models for Earthmoving Operations

Earthmoving operations have significant importance, particularly for civil infrastructure projects. The performance of these operations should be monitored regularly to support timely recognition of undesirable productivity variances. Although productivity assessment occupies high importance in earthmoving operations, it does not provide sufficient information to assist project managers in taking the necessary actions in a timely manner. Assessment only is not capable of identifying problems encountered in these operations and their causes. Many studies recognized conditions and related factors that influence productivity of earthmoving operations. These conditions are mainly project-specific and vary from one project to another. Most of reported work in the literature focused on assessment rather than analysis of productivity. This study presents three integrated models that automate productivity measurement and analysis processes with capabilities to detect different adverse conditions that influence the productivity of earthmoving operations. The models exploit innovations in wireless and remote sensing technologies to provide project managers, contractors, and decision makers with a near-real-time automated productivity measurement and analysis. The developed models account for various uncertainties associated with earthmoving projects. The first model introduces a fuzzy-based standardization for customizing the configuration of onsite data acquisition systems for earthmoving operations. While the second model consists of two interrelated modules. The first is a customized automated data acquisition module, where a variety of sensors, smart boards, and microcontrollers are used to automate the data acquisition process. This module encompasses onsite fixed unit and a set of portable units attached to each truck used in the earthmoving fleet. The fixed unit is a communication gateway (Meshlium®), which has integrated MySQL database with data processing capabilities. Each mobile unit consists of a microcontroller equipped with a smart board that hosts a GPS module as well as a number of sensors such as accelerometer, temperature and humidity sensors, load cell and automated weather station. The second is a productivity measurement and analysis module, which processes and analyzes the data collected automatically in the first module. It automates the analysis process using data mining and machine learning techniques; providing a near-real-time web-based visualized representation of measurement and analysis outcomes. Artificial Neural Network (ANN) was used to model productivity losses due to the existence of different influencing conditions. Laboratory and field work was conducted in the development and validation processes of the developed models. The work encompassed field and scaled laboratory experiments. The laboratory experiments were conducted in an open to sky terrace to allow for a reliable access to GPS satellites. Also, to make a direct connection between the data communication gateway (Meshlium®), initially installed on a PC computer to observe the received data latency. The laboratory experiments unitized 1:24 scaled loader and dumping truck to simulate loading, hauling and dumping operations. The truck was instrumented with the microcontroller equipped with an accelerometer, GPS module, load cell, and soil water content sensor. Thirty simulated earthmoving cycles were conducted using the scaled equipment. The collected data was recorded in a micro secure digital (SD) card in a comma separated value (CSV) format. The field work was carried out in the city of Saint-Laurent, Montreal, Quebec, Canada using a passenger vehicle to mimic the hauling truck operational modes. Fifteen Field simulated earthmoving cycles were performed. In this work two roads with different surface conditions, but of equal length (1150 m) represented the haul and return roads. These two roads were selected to validate the developed road condition analysis algorithm and to study the model’s capability in determining the consequences of adverse road conditions on the haul and return durations and thus on the tuck and fleet productivity. The data collected from the lab experiments and field work was used as input for the developed model. The developed model has shown perfect recognition of the state of truck throughout the fifteen field simulated earthmoving cycles. The developed road condition analysis algorithm has demonstrated an accuracy of 83.3% and 82.6% in recognizing road bumps and potholes, respectively. Also, the results indicated tiny variances in measuring the durations compared with actual durations using time laps displayed on a smart cell telephone; with an average invalidity percentage AIP% of 1.89 % and 1.33% for the joint hauling and return duration and total cycle duration, respectively

Earthmoving construction automation with military applications: Past, present and future

© ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things. All rights reserved. Amongst increasing innovations in frontier engineering sciences, the advancements in Robotic and Autonomous Systems (RAS) has brought about a new horizon in construction applications. There is evidence of the increasing interest in RAS technologies in the civil construction sector being reflected in construction efforts of many military forces. In particular, Army or ground-based forces are frequently called upon to conduct construction tasks as part of military operations, tasks which could be partially or fully aided by the employment of RAS technologies. Along with recent advances in the Internet of Things (IoT) and cyber-physical system infrastructure, it is essential to examine the current maturity, technical feasibility, and affordability, as well as the challenges and future directions of the adoption and application of RAS to military construction. This paper presents a comprehensive survey and provides a contemporary and industry-independent overview on the state-of-the-art of earthmoving construction automation used in defence, spanning current world’s best practice through to that which is predicted over the coming years

Technology capabilities for an automated and connected earthwork roadmap

Purpose: The development of communication and artificial intelligence technologies has raised interest in connectivity and increased autonomy of automated earthmoving equipment for earthwork. These changes are motivating work to reduce uncertainties, in terms of improving equipment object detection capability and reducing strikes and accidents on site. The purpose of this study is to illustrate industrial drivers for automated earthwork systems; identify the specific capabilities which make the transformation happen; and finally determine use cases that create value for the system. These three objectives act as components of a technology roadmap for automated and connected earthwork and can guide development of new products and services. Design/methodology/approach: This paper used a text mining approach in which the required data was captured through a structured literature review, and then expert knowledge was used for verification of the results. Findings: Automated and connected earthwork can enhance construction site and its embraced infrastructure, resilience by avoiding human faults during operations. Automating the monitoring process can lead to reliable anticipation of problems and facilitate real-time responses to unexpected situation via connectedness capabilities. Research findings are presented in three sections: industrial perspectives, trends and drivers for automated and connected earthwork; capabilities which are met by technologies; and use cases to demonstrate different capabilities. Originality/value: This study combines the results of disintegrated and fragmented research in the area of automated and connected earthwork and categorises them under new capability levels. The identified capabilities are classified in three main categories including reliable environmental perception, single equipment decision-making toward safe outcomes and fleet-level safety enhancement. Finally, four different levels of automation are proposed for earthwork technology roadmap

Predicting fuel energy consumption during earthworks

This research contributes to the assessment of on-site fuel consumption and the resulting carbon dioxide emissions due to earthworks-related processes in residential building projects, prior to the start of the construction phase. Several studies have been carried out on this subject, and have demonstrated the considerable environmental impact of earthworks activities in terms of fuel consumption. However, no methods have been proposed to estimate on-site fuel consumption during the planning stage. This paper presents a quantitative method to predict fuel consumption before the construction phase. The calculations were based on information contained in construction project documents and the definition of equipment load factors. Load factors were characterized for the typical equipment that is used in earthworks in residential building projects (excavators, loaders and compactors), taking into considering the type of soil, the type of surface and the duration of use. We also analyzed transport fuel consumption, because of its high impact in terms of pollution. The proposed method was then applied to a case study that illustrated its practical use and benefits. The predictive method can be used as an assessment tool for residential construction projects, to measure the environmental impact in terms of on-site fuel consumption. Consequently, it provides a significant basis for future methods to compare construction projects.Peer ReviewedPostprint (author's final draft

Estimation of the excavator actual productivity at the construction site using video analysis

Current estimates of the actual productivity of heavy construction machinery at a construction site are not supported by an appropriate and widely used methodology. Recently, for the purpose of estimating the actual productivity of heavy construction machinery, visionbased technologies are used. This paper emphasizes the importance of estimating actual productivity and presents a way (i.e. a research framework) to achieve it. Therefore, the aim of this paper is to propose a simple research framework (SRF) for quick and practical estimates of excavator actual productivity and cycle time at a construction site. The excavator actual productivity refers to the maximum possible productivity in real construction site conditions. The SRF includes the use of a video camera and the analysis of recorded videos using an advanced computer program. In cases of continuous application of SRF, a clear and transparent base for monitoring and control of earthworks can be obtained at an observed construction site

An information fusion approach for filtering GNSS data sets collected during construction operations

Global Navigation Satellite Systems (GNSS) are widely used to document the on- and off-site trajectories of construction equipment. Before analyzing the collected data for better understanding and improving construction operations, the data need to be freed from outliers. Eliminating outliers is challenging. While manually identifying outliers is a time-consuming and error-prone process, automatic filtering is exposed to false positives errors, which can lead to eliminating accurate trajectory segments. This paper addresses this issue by proposing a hybrid filtering method, which integrates experts’ decisions. The decisions are operationalized as parameters to search for next outliers and are based on visualization of sensor readings and the human-generated notes that describe specifics of the construction project. A specialized open-source software prototype was developed and applied by the authors to illustrate the proposed approach. The software was utilized to filter outliers in sensor readings collected during earthmoving and asphalt paving projects that involved five different types of common construction equipmen

Technologies for safe and resilient earthmoving operations: A systematic literature review

Resilience engineering relates to the ability of a system to anticipate, prepare, and respond to predicted and unpredicted disruptions. It necessitates the use of monitoring and object detection technologies to ensure system safety in excavation systems. Given the increased investment and speed of improvement in technologies, it is necessary to review the types of technology available and how they contribute to excavation system safety. A systematic literature review was conducted which identified and classified the existing monitoring and object detection technologies, and introduced essential enablers for reliable and effective monitoring and object detection systems including: 1) the application of multisensory and data fusion approaches, and 2) system-level application of technologies. This study also identified the developed functionalities for accident anticipation, prevention and response to safety hazards during excavation, as well as those that facilitate learning in the system. The existing research gaps and future direction of research have been discussed

Trajectory-based worker task productivity monitoring

Over the past decades labour productivity in construction has been declining. The prevalent approach to estimating labour productivity is through an analysis of the trajectories of the construction entities. This analysis typically exploits four types of trajectory data: a) walking path trajectories, b) dense trajectories (posture), c) physiological rates such as heart rate (beats/minute) and respiratory rate (breaths/minute), and d) sound signals. The output of this analysis is the number of work cycles performed by construction workers. The total duration of these cycles is equal to the labour input of a task. However, all such methods do not meet the requirements for proactive monitoring of labour productivity in an accurate, non-obtrusive, time and cost efficient way for multiple workers. This paper proposes a method to address this shortcoming. It features a promising accuracy in terms of calculating the labour input.ICASE studentship award, supported by EPSRC and LAING O'ROURKE PLC under Grant No. 13440016