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

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

Unmanned aerial vehicles (UAVs) for inspection in construction and building industry

Digital data capture is a key component of Industry 4.0 practices. In the past few decades Unmanned Aerial Vehicles (UAVs) have entered the construction industry to capture site data and to cover topographic as well as different types of inspection matters. Photographs, live video, photogrammetric digital elevation models and 3D point clouds can be generated using different photogrammetry facilities, cameras and lasers attached to either a fixed wing or rotorcraft UAVs. UAVs have the ability to deliver information by monitoring, 3Dmaping, measuring, analysing, as well as recording on-site activities. This paper presents the state of art of UAVs usage in construction and building industry and evaluates their applications by experimental case studies. The challenges of using UAVs and their links to BIM will be also discussed. This study found that visual imaging is currently the most popular use of UAVs on construction sites to ensure integrity of structural inspection, however, 3D models derived from LiDAR and photogrammetry techniques are surpassing more traditional methods as they are still significantly cheaper and faster to use. UAVs is also used to monitor workers on site to identify what resources they need in order to carry out their tasks more efficiently and also for the purposes of their health and safety. Despite the approved efficiency of using UAVs on sites to provide better visualization of the working environment, there are still key issues to be tackled such as: the limited flight time of UAVs and its weight. Structural/site investigations have shown that there are some defects on the use of aerial vehicles, with the most important to be the cost along with the precision of the results which may vary depending on the technologies used. There is further study required into the combination of UAVs derived data and its inclusion into BIM, as barriers remain regarding translatable data platforms. There are also some ethical concerns of surveying workers on site and how to protect their privacy

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

Big Data in Construction Management Research

-acceptedVersio

Geographic Information Systems and Science

Geographic information science (GISc) has established itself as a collaborative information-processing scheme that is increasing in popularity. Yet, this interdisciplinary and/or transdisciplinary system is still somewhat misunderstood. This book talks about some of the GISc domains encompassing students, researchers, and common users. Chapters focus on important aspects of GISc, keeping in mind the processing capability of GIS along with the mathematics and formulae involved in getting each solution. The book has one introductory and eight main chapters divided into five sections. The first section is more general and focuses on what GISc is and its relation to GIS and Geography, the second is about location analytics and modeling, the third on remote sensing data analysis, the fourth on big data and augmented reality, and, finally, the fifth looks over volunteered geographic information.info:eu-repo/semantics/publishedVersio

Automatic estimation of excavator actual and relative cycle times in loading operations

This paper proposes a framework to automatically determine the productivity and operational effectiveness of an excavator. The method estimates the excavator\u27s actual, theoretical, and relative cycle times in the loading operation. Firstly, a supervised learning algorithm is proposed to recognize excavator activities using motion data obtained from four inertial measurement units (IMUs) installed on different moving parts of the machine. The classification algorithm is offline trained using a dataset collected via an excavator operated by two operators with different levels of competence in different operating conditions. Then, an approach is presented to estimate the cycle time based on the sequence of activities detected using the trained classification model. Since operating conditions can significantly influence the cycle time, the actual cycle time cannot solely reveal the machine\u27s performance. Hence, a benchmark or reference is required to analyze the actual cycle time. In the second step, the theoretical cycle time of an excavator is automatically estimated based on the operating conditions, such as swing angle and digging depth. Furthermore, two schemes are presented to estimate the swing angle and digging depth based on the recognized excavator activities. In the third step, the relative cycle time is obtained by dividing the theoretical cycle time by the actual cycle time. Finally, the results of the method are demonstrated by the implementation on two case studies which are operated by inexperienced and experienced operators. The obtained relative cycle time can effectively monitor the performance of an excavator in loading operations. The proposed method can be highly beneficial for worksite managers to monitor the performance of each machine in worksites

USING UNMANNED AIRCRAFT SYSTEMS FOR CONSTRUCTION VERIFICATION, VOLUME CALCULATIONS, AND FIELD INSPECTION

The construction field is one of the most variable industries due to continuous technological advances impacting this industry. Innovative research is currently being applied to construction disciplines such as surveying and design in order to optimize labor, costs, and time. However, there remains a need to improve productivity and safety for construction projects. Unmanned Aircraft Systems (UAS) have been implemented as a means to offer a new alternative for onsite data collection, due to the limitations of traditional surveying methods such as GPS and total station, which are usually an exhausting manual process. The main goal of this research was to use UAS technologies to improve the efficiency of construction surveying and inspection activities. Different UAS flights were performed to verify a variety of measurements obtained from building plans. The comparison of volume calculations for an aggregate pile was determined using full point cloud data to generate a 3D model, which was compared with the 3D models obtained using GPS point and extracted point cloud. The model obtained using the full point cloud data showed greater accuracy as compared with the traditional surveying models since its generated surface was more similar to the actual surface of the pile. Field inspection of a bridge’s typical structures was accomplished by using a point cloud model, as well as photogrammetric models under daylight and twilight conditions. The highest linear measurement variation for field inspection was almost 1/3 foot in a 33-foot length. This outcome yielded a generally acceptable degree of accuracy for inspection tasks. In addition, photogrammetric models can provide high-quality pictures for visual inspection of other bridge components, such as the assessment of the Rip Rap located at the beginning of the bridge selected for this research