Automated Progress Monitoring of Masonry Activity Using Photogrammetric Point Cloud

Abstract

Information retrieval and automated progress estimation of ongoing construction projects have been an area of interest for researchers in the field of civil engineering. It is done using 3D point cloud asbuilt and as-planned model. Advancements in the field of photogrammetry and computer vision have made 3D reconstruction of buildings easy and affordable. But the high variability of construction sites, in terms of lighting conditions, material appearance, etc. and error-prone data collection techniques tend to make the reconstructed 3D model erroneous and incorrect representation of the actual site. This eventually affects the result of progress estimation step. To overcome these limitations, this paper presents a novel approach for improving the results of 3D reconstruction of a construction site by employing two-step process for the reconstruction as compared to the traditional approach. In the proposed method, the first step is to obtain an as-built 3D model of the construction site using 3D scanning techniques or photogrammetry in the form of point cloud data. In the second step, the model is passed through pre-trained machine learning binary classification model for identifying and removing erroneous data points in the captured point cloud. Erroneous points are removed by identifying the correct building points. This processed as-built model is compared with an as-planned model for progress estimation. Based on the proposed method, experiments are carried out using commercially available stereo vision camera for 3D reconstruction.

Application of computer vision for construction progress monitoring

“…Presently, engineers and project managers monitor the working status of a construction labourer by looking at images (Braun, et al, 2015). Progress estimation is the comparison of planned schedules with as-built schedules or models to determine project progress (Pushkar, Senthilvel, and Varghese, 2018). In the stage following project estimation, data visualisation and reporting is performed to make the decision-making process smooth (Han and Fard, 2017).…”

“…Pushkar, Senthilvel, and Varghese (2018), B -Omar andNehdi (2016), C -Meredith and Mantel (2009), D -Daniel, Kavgic, and Ergen (2019), E -Braun, et al (2015, F -Kim, Son, and Kim (2013), G -Fard, Mora, andSavarese (2015), H -Alizadehsalehi and Yitmen (2018), I -Fard and Peña-Mora (2007), J -Han and Fard (2017), K -Braun, et al (2020), L -Wang, et al (2021 …”

BIM-Integrated Collaborative Robotics for Application in Building Construction and Maintenance

“…There has been great interest in BIM-enabled autonomous mobile scanning systems in the construction field in recent years. Developments range from research projects [49][50][51] to startups launched on the market [52][53][54][55]. These usually involve using algorithms for object recognition and Scan-vs-BIM or Scan-to-BIM methods [49].…”

Application of computer vision for construction progress monitoring: a qualitative investigation

“…According to the literature, CPM can be divided into several stages. Pushkar et al (2018), Omar and Nehdi (2016), Alizadehsalehi and Yitmen (2018) and Han and Fard (2017) have categorised CPM into four stages, namely, "Data acquisition", "Information retrieval", "Progress estimation and comparison" and "Results visualisation/reporting". Daniel et al (2019) and Fard et al (2009) have identified one more stage, namely, "Initial planning/Scheduling", in addition to the stages in CPM identified by the previous authors.…”

“…The authors explained that the collected data and information must be verified manually to ensure accurate verification. Progress estimation is the comparison of planned models with as-built models to determine progress discrepancies (Pushkar et al , 2018). In the stage following project estimation, data visualisation and reporting is performed to make a smooth decision-making process (Han and Fard, 2017).…”