CII Congratulates CETI Award Winners
Category: Intelligent & Automated Construction Job Site
This keenly-contested category focuses on the intelligent and automated construction job site that significantly reduces construction time and cost, produces high-fidelity documentation of the as-built facility, diminishes errors and rework, improves safety and security and secures highly efficient supply of materials and products to the construction job site. The award will go to the project that best embodies the benefits of emerging intelligent technologies and/or new applications that are fully-sensed and wirelessly networked to monitor compliance with cost, schedule, material placement and quality, worker pace and accuracy, technical performance, and safety.
Winner: AI Based Human Proximity Alert and Vehicle Control
The target of the project was to provide an AI Based control unit to be placed on construction site vehicles (Cranes, Trucks, etc.) in order to detect the presence of a human in the proximity of the vehicle. In case it is moving, the on-board system would automatically apply a break to stop the vehicle (or just warn) in case the presence is too close.
Contact Information:
This project was a joint effort by Rami Santina (CCT) and Dr. Daniel Asmar (AUB).
The project was managed by Rayan Jreije, Manager, Visual Project Controls Organization, CCC. rayan@ccc.gr
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Category: Outstanding Student Research Project
Outstanding Student Research Project (undergrad and graduate students - as well as doctoral candidates who received their degree less than one year from submitting application)
Winner: Ruodan Lu
Automated Bridge Information Model Generation System
The project provides a step-change in the way we inspect existing highway bridges by using the point cloud data (PCD) to automatically create a 3D virtual bridge model to help solve today's bridge management challenges.
The major challenge that highway bridge owners face with regard to the data needed for rapid repair, maintenance and retrofit of their bridges with minimum cost is the gap between the qualified data available in Bridge Management Systems (BMS) and the reliable information needed for decision-making.
The global infrastructure market is poised for an explosive adoption of Bridge Information Modelling (BrIM), which provides a shared knowledge resource for information to support a reliable basis for decision making during a bridge’s life-cycle. However, the BrIM models are still not being used in BMS despite laser scanning technology has been implemented for PCD collection over the past years. The reason for this is that the cost and effort for modelling existing bridges from PCD currently outweighs the perceived benefits of the resulting models.
Numerous research efforts were pursued towards automating the PCD-to-BrIM models process, which can drastically reduce the manual effort and cost involved. Recent advances have enabled bottom-up techniques to achieve the automatic generation of surface primitives to create solid models. However, the limitations of these methods are (1) computational expensive and (2) lack robustness with regard to imperfect data due to clutters/occlusions.
This project proposes an innovative BrIM model generation method. The following novelties differentiate the proposed method from previous research:
• It follows a heuristic top-down strategy which uses human expertise as object detection guidance
• It starts by processing the entire bridge PCD instead of generating surfaces based on individual points
• It is robust to noisy and missing data and can infer key geometry properties of detected objects in the absence of any additional relevant data
The project plans to target the needs of the bridge owners by providing a cost-effective software solution for automated compilation of solid 3D geometry from PCD to help monitor and inspect existing bridges while decreasing the cost of repairs and maintenance.
Contact Information:
Ruodan Lu, PhD candidate, University of Cambridge, rl508@cam.ac.uk
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Category: Outstanding Researcher (+10 years)
Winner: William J. O'Brien
This nomination recognizes the significant body of research work performed by Dr. William (Bill) O'Brien at The University of Texas at Austin that advances the aims of Fiatech/CII members and their broader research agenda for capital projects. Dr. O'Brien has made significant advancements in understanding and implementing technologies and work processes that improve capital projects. His work spans from IT implementation to alignment, all within a systems framework. Most recently, he has been the lead academic on the CII/COAA and Fiatech efforts on Advanced Work Packaging (AWP). His research on Advanced Work Packaging was featured in a cover story by Engineering News-Record, designated a Best Practice by the Construction Industry Institute, and has been adopted as a corporate execution standard by major owner operators. More broadly, his research has demonstrated impact and has been repeatedly supported by the National Science Foundation, National Institute of Standards and Technology, Construction Industry Institute, Fiatech and the Texas Department of Transportation. He is the author or co-author of over 100 journal articles, book chapters, and technical reports. This includes the widely referenced Construction Supply Chain Management Handbook, where he was lead editor. At Texas, Dr. O’Brien has sought to introduce leading mobile computing and visualization software to the classroom. These efforts were recognized by a 2008 University of Texas Innovative Instructional Technology Program (IITAP) Gold Award. His research has been recognized by the Fiatech consortium, where he was the recipient of 2007 and 2011 CETI Awards in the Technology and Knowledge-Enabled Workforce category. In 2012, Dr. O’Brien received the Outstanding Researcher Award from the Construction Industry Institute. Dr. O'Brien's research was described by CII as follows: "His fellow team members enthusiastically praise his insight, ingenuity, and ésprit de corps. His consistently solid research design, his practical approach to industry problems, his attention to detail, and his precise communication of his findings make the research he produces remarkably useful and user-friendly." (CII Outstanding Researcher Award 2012.)
Contact Information:
William O'Brien, Professor Organization, UT-Austin, wjob@mail.utexas.edu
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Category: Outstanding Early Career Researcher (1-10 years)
Winner: Baabak Ashuri
Project Data Analytics – Improving our understanding about design development process enabling improvements in design productivity
Despite substantial investments in Virtual Design and Construction (VDC) technologies, the Architecture, Engineering and Construction (AEC) industry has not improved productivity as much as other industries, such as manufacturing. Poor design management is a major reason for growing project costs, delays in project delivery and low-quality project outcomes. While the design phase typically accounts for about 5 to 10 percent of the total project cost, rectifying conflicts resulting from faulty design decisions accounts for an additional 5.1 to 7.6 percent of the total project cost. The U.S. construction industry spends about $70 billion annually to resolve design-related issues.
Dr. Ashuri has researched design productivity from a novel data mining perspective. Dr. Ashuri’s research capitalizes on large volumes of data available in the event logs of computer programs used by designers from numerous disciplines to create building information models. He connects the disciplines of design management and data analytics to take advantage of the fine-grained, objective and cost-effective data provided in electronic design logs. Dr. Ashuri identified fundamental attributes describing individual designers’ preferences in the execution of design tasks from rigorous process mining of a substantially large dataset of actual design log files.
Dr. Ashuri analyzed structural features of collaborative networks at macro, meso and micro levels within a design organization through dynamic social network analysis. The identification of process pattern attributes and sociological network properties are used to establish links between design process and design productivity. Dr. Ashuri created predictive models to explain the variability in design productivity.
Dr. Ashuri’s research is the first attempt to reveal the relationship between the productivity and execution patterns of an individual designer using fine-grained, objective and cost-effective data captured non-intrusively from the actual design process. Dr. Ashuri’s research creates empirically-verifiable links between design productivity and the electronic traces that designers have left behind, while taking into account unique characteristics of the project and distinctive attributes of the collaborative design team. Revealing these fundamental relationships will help us create more streamlined process to expedite project delivery.
Contact Information:
Ashuri, Baabak baabak.ashuri@coa.gatech.edu
Mani Golparvar, Associate Professor, University of Illinois, MGOLPAR@ILLINOIS.EDU
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Category: Outstanding Early Career Researcher (1-10 years)
Winner: Burcin Becerik-Gerber
Dr. Burcin Becerik-Gerber’s research focuses on the development of novel methods for the acquisition, modeling, and analysis of the data needed for cognitive (responsive and adaptive) built environments that can perceive, sense, and reason what’s happening and collaborate with their users, and support decision-making, problem solving, management of resources and learning. She develops algorithms, frameworks and visualization techniques to improve built-environment efficiency, sustainability, maintainability and resiliency while increasing user satisfaction by taking into account building occupants’ needs, preferences, and requirements. This user-centered aspect of her work distinguishes her from many others who focus on smart buildings and cities. Her work focuses on both the building objectives (reduced energy consumption, improved building performance, etc.) and user objectives (e.g., increased comfort in commercial and residential buildings, improved learning in educational settings, safety, and security on construction sites). By innovating on sensing, actuation, communication and visualization systems, Dr. Becerik-Gerber creates different experiences with our built environments, which inevitably will change our everyday experiences, causing novel interactions between buildings and their occupants. Her research delivers on buildings “in” the near future, as opposed to the buildings “of” the future, that become interactive entities that provide “user-centered” and “dynamic” spaces for their inhabitants by taking care of operational and repetitive tasks while freeing people to engage in healthier, more comfortable and creative activities. Some of the examples of her work include physiological sensing of thermal comfort, and integration of multiple occupants’ thermal comfort preferences into HVAC systems control logic; or design of a kinetic façade that dynamically learns its users’ comfort preferences and organizes itself for providing comfortable and healthy visual conditions while optimizing the building’s energy consumption; or an appliance automation system that recognizers a user’s activities (watching TV, working on a computer, etc.) and learns and tailors the level of automation to match the workload in order to lead to less error-prone operations and more satisfaction with the automation; or personification of a building through the use of virtual humans (social dialog, rapport building tactics, etc.) in order to promote pro-environmental behaviors in buildings.
Contact Information:
Burcin Becerik-Gerber, Associate Professor of Civil and Environmental Engineering, becerik@usc.edu
Stephen Schrank, Early Career Chair in Civil and Environmental Engineering
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Category: Technology & Knowledge-enabled Workforce
This award marks the most successful technologies and educational tools used to assure accurate performance, provide real-time alerts, and deliver on-demand guidance and training to a capital industries’ workforce. A strong contender would demonstrate vibrant approaches to promote workforce technology adoption, assimilation, and pervasive use and/or feature a new or improved capital industry workforce-specific technology such as a wearable computer, heads-up displays, wireless communications, or smart tools/equipment. The candidate would also illustrate new workforce paradigms, craft and managerial skills-development, and/or empirical quality, efficiency and productivity benefits.
Winner: SmartHat 4.0: The Next Generation of Self-Monitoring Alert and Reporting Technology for Hazard Avoidance and Training
About 60,000 construction workplace deaths occur worldwide every year causing loss of lives, significant collateral damages, and severe tragic consequences to many families. While slips, trips and falls account for about one third of all fatalities in many of the developed countries, equipment is involved in about 25% of all fatal cases. Reasons are often attributed to personal behavior or poorly planned and/or inadequately provided work environments. Few technological means are available to date that rigorously assist in the safety planning effort, allow workers to recognize and report hazards, and provide personalized feedback.
For a long time have the construction industry leaders in safety been focusing on “zero accidents” and other best practices in corporate safety. These have successfully changed organizational culture, supervision, preconditions, and unsafe personal behavior. However, the same industry leaders are recognizing their safety records are not improving any more as fast as they like. This challenge comes to an industry that is known in public to be highly fragmented and slow in applying innovation. It seems existing approaches have made their impact.
Since 2002, Wolf and Teizer (and their research students) have been assuming that significant improvements can be gained in construction safety once technology is applied to existing safety management practices (Figure 1). They have researched BIM-based automated safety rule checking to detect and eliminate hazards before construction starts, remote sensing and Internet of Things (IoT) approaches that provide workers with (near) real-time feedback, and other technology to objectively measure, analyze, and visualize unsafe behavior. Latter, for example, is used in predictive analytics and has turned into novel processes for personalized education and learning. Some of their research and development has inspired commercial products.
As the construction industry accounts for a large amount of the yearly gross domestic products and employments in most countries, a key message might be that advances in emerging technologies offer substantial opportunities to improve construction safety while help meeting other national challenges, such as becoming more efficient in work tasks. Thus, investing in construction safety and specifically targeting some of the most critical objectives in a construction projects’ lifecycle will directly impact a project’s bottom line. As empirical industry studies report “safe jobsites are also productive”, research yet has to prove it.
Contact Information:
Research: Dr. Jochen Teizer, jochen.teizer@rub.de
Media: Dr. Julia Weiler, julia.weiler@uv.rub.de
Dean: Prof. Dr.-Ing. Peter Mark, dekanat-bi@rub.de
Sonny Astani, Department of Civil and Environmental Engineering
USC Viterbi School of Engineering
Date posted: March 19, 2018