Mani Golparvar-Fard

Bio: Mani Golparvar-Fard is an academic researcher from University of Michigan. The author has contributed to research in topics: Construction management & Augmented reality. The author has an hindex of 2, co-authored 2 publications receiving 184 citations. Previous affiliations of Mani Golparvar-Fard include Virginia Tech & Columbia University.

Integrated Sequential As-Built and As-Planned Representation with D4AR Tools in Support of Decision-Making Tasks in the AEC/FM Industry

Abstract: The significant advancement in digital imaging and widespread popularity of digital cameras for capturing a comprehensive visual record of construction performance in the architecture, engineering, construction, and facility management (AEC/FM) industries have triggered an extensive growth in the rate of site photography, allowing hundreds of images to be stored for a project on a daily basis. Meanwhile, collaborative AEC technologies centering around building information models (BIMs) are becoming widely applied to support various architectural, structural, and preconstruction decision-making tasks. These models, if integrated with the as-built perspective of a construction, have great potential to extensively add value during the construction phase of a project. This paper reports recent developments from research efforts in (1) automated acquisition of as-built point-cloud models from unordered daily site photo collections and geo-registration of site images, (2) automated generation of four-dimensiona...

Interactive Visual Construction Progress Monitoring with D 4 AR — 4D Augmented Reality — Models

Abstract: Visualization of construction progress helps construction project managers to study spatial aspects of as-built and as-planned performances, identify progress discrepancies, better utilize resources and equipments in different locations and make timely corrective decisions. This needs as-built performance model to be generated and superimposed over as-planned allowing progress discrepancies to be visualized. In this paper we discuss methodology of generating, analyzing and visualizing progress with D 4 AR (4 Dimensional Augmented Reality) models using daily construction photographs and 4D as-planned model. We use three construction cases to illustrate how the as-built progress models could be sparsely reconstructed using daily site photographs and how the reconstructed model is superimposed over the as-planned model. We also demonstrate how the D 4 AR model is applied in these ongoing construction projects. As a result of this research, automatic and more frequent visual progress data collection, analysis and reporting will become possible.

Automated Progress Monitoring Using Unordered Daily Construction Photographs and IFC-Based Building Information Models

Abstract: Accurate and efficient tracking, analysis and visualization of as-built (actual) status of buildings under construction are critical components of a successful project monitoring. Such information directly supports control decision-making and if automated, can significantly impact management of a project. This paper presents a new automated approach for recognition of physical progress based on two emerging sources of information: (1) unordered daily construction photo collections, which are currently collected at almost no cost on all construction sites; and (2) building information models (BIMs), which are increasingly turning into binding components of architecture/engineering/construction contracts. First, given a set of unordered and uncalibrated site photographs, an approach based on structure-from-motion, multiview stereo, and voxel coloring and labeling algorithms is presented that calibrates cameras, photorealistically reconstructs a dense as-built point cloud model in four dimensions (th...