Automated Progress Controlling and Monitoring Using Daily Site Images and Building Information Modelling

Hadi Mahami,Saeid Nahavandi,Ali Hosseininaveh Ahmadabadian,Farnad Nasirzadeh

doi:10.3390/buildings9030070

Hadi Mahami, Saeid Nahavandi + Show 2 more

Open Access

https://doi.org/10.3390/buildings9030070

Copy DOI

Abstract

This research presents a novel method for automated construction progress monitoring. Using the proposed method, an accurate and complete 3D point cloud is generated for automatic outdoor and indoor progress monitoring throughout the project duration. In this method, Structured-from-Motion (SFM) and Multi-View-Stereo (MVS) algorithms coupled with photogrammetric principles for the coded targets’ detection are exploited to generate as-built 3D point clouds. The coded targets are utilized to automatically resolve the scale and increase the accuracy of the point cloud generated using SFM and MVS methods. Having generated the point cloud, the CAD model is generated from the as-built point cloud and compared with the as-planned model. Finally, the quantity of the performed work is determined in two real case study projects. The proposed method is compared to the Structured-from-Motion (SFM)/Clustering Multi-Views Stereo (CMVS)/Patch-based Multi-View Stereo (PMVS) algorithm, as a common method for generating 3D point cloud models. The proposed photogrammetric Multi-View Stereo method reveals an accuracy of around 99 percent and the generated noises are less compared to the SFM/CMVS/PMVS algorithm. It is observed that the proposed method has extensively improved the accuracy of generated points cloud compared to the SFM/CMVS/PMVS algorithm. It is believed that the proposed method may present a novel and robust tool for automated progress monitoring in construction projects.

Highlights

Project progress monitoring and control is one of the most important tasks of construction project management [1]
Monitoring of the actual state of the project can enable decision makers to assess the deviations from the as-planned state and adopt corrective actions if the project is behind schedule [2]
The results achieved by the proposed photogrammetric Multi-View Stereo method and SFM/Clustering Multi-Views Stereo (CMVS)/Patch-based Multi-View Stereo (PMVS) approach are compared based on three criteria, being the accuracy of the generated model, the amount of generated noises and the number of points of the generated point cloud

Summary

Introduction

Project progress monitoring and control is one of the most important tasks of construction project management [1]. Monitoring of the actual state of the project can enable decision makers to assess the deviations from the as-planned state and adopt corrective actions if the project is behind schedule [2]. Current practices of predicting the performance of a construction project require inspections that are still mainly manual, time consuming and can contain errors [3]. The traditional, manual construction progress assessment with human presence is still dominating [4]. Computer technologies have great potential to improve management practices in the construction industry [5]. The construction industry has been a slow adopter of novel technologies [6]. Automation of construction progress monitoring, Buildings 2019, 9, 70; doi:10.3390/buildings9030070 www.mdpi.com/journal/buildings

Objectives

Results

Conclusion