Author
David Grau
Other affiliations: University of Alabama, URS Corporation
Bio: David Grau is an academic researcher from Arizona State University. The author has contributed to research in topics: Construction management & Project management. The author has an hindex of 11, co-authored 52 publications receiving 501 citations. Previous affiliations of David Grau include University of Alabama & URS Corporation.
Papers
More filters
TL;DR: In this paper, an extensive field trial was conducted on a power-plant project and data from both a traditional tracking process and an automated tracking process designed for the purposes of this study were collected.
138 citations
TL;DR: In this article, the authors find the challenges in the current practice of deconstruction activities and the gaps between its theory and implementation, and provide insights about how DfD can create opportunities to turn these concepts into strategies that can be largely adopted by the construction industry stakeholders in the near future.
114 citations
TL;DR: Re reuse benefits depended on aggressive reuse rates (>70%) and multiple reuses of steel were needed to offset the embodied environmental impacts during steel production, and the analyses showed that process-based LCA and hybrid LCA can generate conflicting results in a C2C LCA.
53 citations
TL;DR: In this paper, an automated geographic information system (GIS) method using post-event point cloud data collected by terrestrial scanners and preevent aerial images was used to calculate the percentage of roof and wall damage and estimate wind speeds at an individual building scale.
Abstract: There are more than 1,000 tornadoes in the United States each year, yet engineers do not typically design for tornadoes because of insufficient information about wind loads. Collecting building-level damage data in the aftermath of tornadoes can improve the understanding of tornado winds, but these data are difficult to collect because of safety, time, and access constraints. This study presents and tests an automated geographic information system (GIS) method using postevent point cloud data collected by terrestrial scanners and preevent aerial images to calculate the percentage of roof and wall damage and estimate wind speeds at an individual building scale. Simulations determined that for typical point cloud density (>25 points/m2), a GIS raster cell size of 40–50 cm resulted in less than 10% error in damaged roof and wall detection. Data collected after recent tornadoes were used to correlate wind speed estimates and the percent of detected damage. The developed method estimated wind speeds f...
38 citations
TL;DR: In this article, the authors analyzed the feasible utilization of passive radio frequency identification (RFID) technologies to automatically track the flow of structural steel components during shipping and receiving processes in an effort to increase the visibility of engineered components at the interface between supply chain and construction.
38 citations
Cited by
More filters
University of Warwick1, University of Nottingham Malaysia Campus2, London South Bank University3, National University of Malaysia4, Al-Hikmah University5, University of Nottingham6, University of Kent7, Kyushu University8, American University of Sharjah9, Birkbeck, University of London10, University of Sheffield11
TL;DR: In this paper, the authors present a critical review of negative and positive impacts of the pandemic and proffers perspectives on how it can be leveraged to steer towards a better, more resilient low carbon economy.
Abstract: The World Health Organization declared COVID-19 a global pandemic on the 11th of March 2020, but the world is still reeling from its aftermath. Originating from China, cases quickly spread across the globe, prompting the implementation of stringent measures by world governments in efforts to isolate cases and limit the transmission rate of the virus. These measures have however shattered the core sustaining pillars of the modern world economies as global trade and cooperation succumbed to nationalist focus and competition for scarce supplies. Against this backdrop, this paper presents a critical review of the catalogue of negative and positive impacts of the pandemic and proffers perspectives on how it can be leveraged to steer towards a better, more resilient low-carbon economy. The paper diagnosed the danger of relying on pandemic-driven benefits to achieving sustainable development goals and emphasizes a need for a decisive, fundamental structural change to the dynamics of how we live. It argues for a rethink of the present global economic growth model, shaped by a linear economy system and sustained by profiteering and energy-gulping manufacturing processes, in favour of a more sustainable model recalibrated on circular economy (CE) framework. Building on evidence in support of CE as a vehicle for balancing the complex equation of accomplishing profit with minimal environmental harms, the paper outlines concrete sector-specific recommendations on CE-related solutions as a catalyst for the global economic growth and development in a resilient post-COVID-19 world.
432 citations
TL;DR: The research presented in this paper combines the Hough transform and “Scan-vs-BIM” systems in a unified approach for more robust automated comparison of as-built and as-planned cylindrical MEP works, thereby providing the basis for automated earned value tracking, automated percent-built-as-planned measures, and assistance for the delivery of as -built BIM models from as-designed ones.
358 citations
TL;DR: In this paper, a review of the recent literature within the framework of the CE to explore how its key principles (Reduce, Reuse, and Recycle) apply to the management of C&D waste (C&DW).
319 citations
TL;DR: In this paper, the authors evaluate a commercially available Ultra Wideband (UWB) system for real-time, mobile resource location tracking in harsh construction environments and demonstrate the applicability of UWB for the design of construction management support tools.
225 citations
TL;DR: A computer vision based algorithm for recognizing single actions of earthmoving construction equipment, based on a multiple binary SVM classifier and spatio-temporal features, which outperforms previous algorithms for excavator and truck action recognition.
215 citations