Integrating Autonomous Site Data Acquisition and Construction Progress

Construction management processes are highly dependent on job site data. Accurate and timely data collection is necessary to support the evaluation and management of projects. In the dynamic environments of construction job sites, monitoring the changes and controlling the status require continuous collection of huge amounts of data. Meanwhile, construction data should be collected using various sources of sensory data to provide enough insights into the current condition of the job site. Data acquisition methods such as laser scanners or 360 images may be used alone or together based on the granularity of data and project progress level. However, using several technologies at the same time may lead to new challenges regarding data fusion and integration.

Introduction

Current practice for acquiring, recording, and managing construction job site data is mostly manual which is labor-intensive, costly, and may have errors. Collecting information using several remote sensing technologies like laser scanners, 360 cameras, GPS, and tag readers may need a complete crew of surveyors who commute between offices and job sites on a routine basis. The completeness and reliability of the collected data rely on the skillfulness and judgment of the surveyors, while manual data collection in hazardous environments increases health and safety risks. Moreover, the collected data may include repetitive information from parts of the job site where there is no visible progress. As a result, a huge amount of useless data would be collected and stored frequently that would be difficult to search and process. In this article, we introduce an innovative solution to be used for automated data acquisition on construction job sites.

Problem Definition

In recent years, automated data collection using various technologies such as drones and robots has been adapted in the construction industry. Boston Dynamics’ Spot is now employed in the construction industry to help the job site personnel in performing tiresome and unfavorable tasks. In Pomerleau’s job sites, Spot is used for capturing data for site progress monitoring, BIM model comparison and quality control, and digital twin generation. Spot can be programmed to conduct a routine site walk and collect spherical images or point clouds of the same locations required to be compared over time. This helps surveyors to assure the data is captured at the same point as the previous ones and facilitates the data collection setup. However, with emerging algorithms in data registration and comparison models, confirming the capture points problem is only solved to some extent. 

Daily data collection in large construction projects brings up another challenge: the volume of the recorded data. Construction projects inherently have a dynamic environment, but the operations’ progress is not always tangible on a daily or even weekly basis. Thereby, acquired data will include repetitive records which do not offer useful insights for the analysis. Storing and searching through such a big pile of data would be too expensive and inefficient at some point and the operators must overwrite the collected data. In that case, the track of historical data would be lost for future references. One solution can be establishing a data-gathering plan based on the project's progress. Nevertheless, project progress can be delayed because of numerous reasons and the baseline schedules need to be updated regularly. So, synchronizing data-gathering plans and project schedules regarding those updates raises new issues.

High-Level Solution

To overcome the mentioned challenges several models have been integrated into an innovative solution named POMiCapture. Figure 1 (below) illustrates the steps of the proposed solution. In the first step, a predefined circulation map is instructed to Spot. The circulation map generation is an inbuilt feature that benefits from different technologies and tools like Lidar and cameras to train Spot with various elements of the job site. Therefore, Spot is aware of the existing conditions on job sites to navigate and collect data efficiently. As was mentioned before, robots possess limited general intelligence to do specific complex tasks requiring perception. So, in the proposed solution, AI algorithms are integrated with Spot to make it more intelligent to collect data while navigating the job site.

Solution Details

Computer vision models backed with deep learning algorithms are employed to detect and recognize the locations for data collection while Spot is navigating the job site. These locations are marked using QR codes by the site superintendent whenever there is progress in the construction jobs. Thereby, each time Spot detects a QR code that is installed on building elements or construction materials, it stops, changes its status to the stabilization mode, and starts acquiring various types of data such as 360 images, RGB-D images, and point clouds. Stabilization mode is another useful feature of Spot to reduce the amount of noise in the collected data and enhance its quality. Moreover, the information of QR codes is extracted and saved in spreadsheets which can be used for other applications (progress monitoring, quality control, etc.). Figure 2 (below) shows an example of how the developed solution makes Spot capable of understanding where and how to collect data autonomously.

Spot robots can retrieve and save the information of recognized QR codes in spreadsheets. Meanwhile, as Spot detects a QR code different loaded data gathering equipment can be initialized automatically and simultaneously. The whole framework is managed to integrate and deploy on a local network connected to a local server. However, it can be set to transfer data to the cloud and have remote access to the gathered data.

Business Benefits

Automated data gathering with POMiCapture provides information for BIM owners to monitor project progress. It eliminates taking long flights and going through hours of traffic commuting to the job site and collecting data by different groups. On a large scale, a great number of traveling hours and consequent road traffic emissions are avoided. Moreover, POMiCapture can gather data in environments that may contain hazardous contaminants that might be damaging for humans. This will increase job site safety besides decreasing the other safety risks for construction workers.

Conclusion

In order to capture all necessary data to keep decision-makers well informed about the construction progress, usually data from several sources need to be acquired and fused. Manual data acquisition in construction job sites is time-consuming, labor-intensive, and costly. Also, capturing repetitive data without a data-gathering plan results in a huge amount of data which mostly are unusable. POMiCapture provides a solution to make data collection intelligent and efficient on construction job sites. It integrates AI algorithms with Spot autonomous robots to acquire data with various sensors wherever there is progress in the operations. 

POMiCapture is offering many other applications to improve construction project management, increase productivity, and enhance job site safety. POMiCapture can walk daily on the outdoor job site and search for QR code-tagged equipment and localize them. Project managers can track assets to provide insight into equipment usage and productivity. Meanwhile, they are kept posted by any changes in the operation’s progress. 

Acknowledgments: The authors would like to thank Pomerleau for providing financial resources and access to equipment and job sites. This project is done under collaboration of Pomerleau’s aXLab and Boston Dynamics. The Spot autonomous robot is provided by Boston Dynamics.

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