Main Gap in Knowledge
Accuracy in measuring the progress of construction is critical in a successful building project. Yet, the automated methods that are used in measuring the progress of such development, as revealed in previous studies, have exhibited limitations mainly because of incomplete data. The objective of this paper is to develop an accurate and fully automated approach for measuring the progress of construction (Anderson, David & Charles 75). The procedure employs the use of 4D BIM in concert with 3D data that is obtained through the use of remote sensing technologies. The methods have three phases, which include alignment of as-built data with the planned model, marching as-built data to BIM information, and revising the status of as-built data (Granberg, Douglas & Riemer 67). The accuracy of the completed building is validated using the 3D data that was acquired from the actual project site. The purpose is to collect data directly from the site to ensure that there is a useful measurement of the progress of construction.
Objectives of the Research
This research aims to present a new automated approach that facilitates recognition of construction progress using two new information sources. These are the unordered collection of daily construction photos and building information models (BIM) (Anderson, David & Charles 79). Subsequently, the research envisions an integrated performance of a building through a visualized interface, which provides contextually relevant information, and on-demand data for constructors. The methods are being increasingly utilized as binding components in establishing construction, engineering, or architectural contracts. The objectives are based on the understanding that there are challenges for contractors to understand building progress because of the complex and dispersed nature of information available. The situation is mostly attributed to a lack of integration among the great information tools and sources available. Thus, this makes it challenging to align the most preliminary information with the physical systems of the model.
Main Process to Achieve the Objective
The process involves first collected sets of uncalibrated and unordered site photographs. This approach is based on Multiview stereo, voxel coloring, and structure-from-motion, as well as labeling of algorithms. The algorithm is designed such that it is capable of calibrating cameras, photo-realistically reconstructs a dense as-built point cloud model in 4D and 3D formats (Lee, Hyun-Soo & Park 112). It also labels and transverses the occupancy scenes. The benefit of this strategy is that it can explicitly account for the occlusions while allowing input images to capture widely within and around the construction environment. Subsequently, an Industry Foundation Class-based (IFC-based) BIM is fused into the as-built scene using robust registration steps. It is then traversed and marked according to the anticipated progress visibility (Lee, Hyun-Soo & Park 116). The level is then followed by the proposal of a machine-learning scheme that is built upon a Bayesian probabilistic model. The purpose of the program is to facilitate automatic detection of monitoring of physical progress at the activity schedule levels. The system is critical as it enables exploration of reconstructed and expected elements with an image-based and iterative, 3D views in which the deviations are color-coded automatically over the IFC-based BIM (Scott 109). Therefore, the intention is to present the underlying hypothesis and the algorithms for generating the integrated 4D as-built and as-planned models, as well as automatically monitor the progress.
The approach utilizes imaging technologies that have previously focused on generating 3D information on various objects on-site to analyze the project progress. Some of these technologies are range imaging, lesser scanning and photogrammetry, and video-Grammarly (Construction Specifications Institute 119). The methods are unique from each other as each has particular ways about costs, progressing time, and capturing speed. For example, photogrammetry technologies have high accuracy in generating 3D models for the construction site based on digital photos (Mahmoud & Ghavamirad, 216). At the same time, the availability of cost-friendly time-lapse and point and shoot cameras and smartphones has increased the images taken from the construction site. As a result, the as-built 3D model is then compared to 3D CAD models that would facilitate the automatic calculation of the percentage of completed sections of each component (Feniosky & Savarese 86). In this way, therefore, the collected data is then assessed and used for measuring the project progress.
Equally, several researchers have applied photo-geometry for collecting geometric orientations, and measurements for building elements, details that are then used for recording as-built model information (Acton 103). For example, Holt, Olomolaiye & Harris (98) designed tracking systems suited for the production of 3D images for scanned objects. The collected information is then utilized in estimating the amount of work that has so far been completed within a particular time interval (Construction Specifications Institute 108). Hence, the research confirms that gaps still exist in the use of automated technologies in reducing limitations associated with each of the tools employed in quantifying contractor evaluation. Adopting a tool that enhances accuracy in monitoring construction progress is critical for contractors. Besides, it is often advisable for evaluators to combine several methods to improve efficiency in evaluation.
Acton, Ph.D., Mathematics-Advances in Research and Application. Atlanta: ScholarlyMedia LLC, 2012. Print.
Anderson, D. A., David R. Luhr, and Charles E. Antle. Framework for development of performance-related specifications for hot-mix asphaltic concrete. Washington, D.C: Transportation Research Board, National Research Council, 1990. Print.
Construction Specifications Institute. The CSI construction product representation practice guide. Hoboken, New Jersey: John Wiley & Sons, Inc, 2013. Print.
Feniosky & Savarese, Silvio. Automated Progress Monitoring Using Unordered Daily Construction Photographs and IFC-Based Building Information Models. Journal of Computing in Civil Engineering. 29. 2012. Print.
Granberg, Douglas D., and Caleb Riemer. Performance-based construction contractor prequalification. Washington, D.C: Transportation Research Board, 2009. Print.
Holt, D., G., Olomolaiye, O., P. & Harris, C., F. Evaluating Prequalification Criteria in Contractor Selection. 1994. Print.
Lee, J., Hyun-Soo, L. & Park, M. Contractor Liquidity Evaluation Model for Successful Public Housing Projects. Retrieved on 5th March 2020 from https://ascelibrary.org/doi/10.1061/%28ASCE%29CO.1943-7862.0001569
Mahmoud & Ghavamirad, Farzan. Contractor selection at the prequalification stage: Current evaluation and shortcomings. Jurnal Teknologi. 2015. Print.
Scott, Sidney. Best-value procurement methods for highway construction projects. Washington, DC: Transportation Research Board of the National Academies, 2006. Print.