ASCE has honored the following people with the 2018 Walter L. Huber Civil Engineering Research Prizes:
Jose E. Andrade, Ph.D., M.ASCE,for revolutionizing the field of granular geomaterials by replacing heuristic methodologies with rigorous multiscale modeling approaches based on scientific understanding of the mechanics and physics across scales, and for defining new frontiers for the civil engineering profession, including planetary exploration.
Andrade’s research focuses on computational mechanics and the constitutive behavior of porous geomaterials. His doctoral thesis investigated the role of material in homogeneities in controlling the observation of shear band formation in granular soils. Localization of deformations represents a very difficult class of problem that occurs pervasively at large scale in problems ranging from landsliding to sedimentary structures in oil reservoirs. It has proven very difficult to predict the occurrence of shear bands at laboratory scale (in specimens that are macroscopically homogeneous) and, hence, to understand scaling processes. Andrade and Borja (2006) shed new light on the problem through simulations that assume random variations in mesoscale porosity (i.e., comparable to grain diameter) within lab specimens. This work attracted considerable attention when it first appeared and Andrade was awarded the prestigious Zienkiewicz Medal (ICE) 2006. He continued this line of work at Northwestern University by making more realistic simulations of spatial variations in porosity (based on experimental work using x-ray tomography done by others), and has shown how this produces strength anisotropy at the macroscopic scale (in laboratory element tests). He has also been able to show how controlled observations of shear banding in laboratory tests can be used to identify input parameters for characterizing the “granular” (i.e., meso-) scale properties using relatively simple plasticity theories. This has led to the proposal for a multiscale framework to enable more reliable predictions of shear-banding.
Jack W. Baker, Ph.D., M.ASCE,for research to characterize the damaging effects of earthquake ground-motion spectral shape, duration, near-fault directivity, and other features for seismic hazard analysis and performance-based engineering of buildings, bridges, and geographically distributed infrastructure.
Baker has established himself as a leader in the characterization of earthquake ground motions. Employing a logical, methodical approach steeped in fundamental mechanics and advanced probabilistic methods, he has developed improved procedures for characterizing the damage potential of earthquake ground motions, novel procedures for identifying and predicting pulse-like earthquake ground motions, and procedures for more realistically representing spectral characteristics of ground motions for design purposes, ground motion selection, spatial variability, and several other applications. His contributions in each of these areas are both numerous and significant; many of the concepts he developed, implemented, and validated are already widely used in practice.
Yuri Bazilevs, Ph.D., A.M.ASCE,for research to characterize the damaging effects of earthquake ground-motion spectral shape, duration, near-fault directivity, and other features for seismic hazard analysis and performance-based engineering of buildings, bridges, and geographically distributed infrastructure.
In 2005, Bazilevs co-developed isogeometric analysis (IGA), a computational technology that is addressing a long-standing problem in engineering of “bridging the gap” between design and analysis. IGA, a prevalent research direction in computational mechanics today, drives much of the academic research in the field and is also finding its way into industrial-scale applications, including those in civil engineering, through its implementation in a variety of research, open-source, and commercial software. By 2015, ten years after its inception, the field of IGA was generating 250 papers and over 4,700 citations in ISI-indexed literature per year. In comparison, it took FEM, ubiquitous in civil engineering, 20 years to reach a 260 papers-per-year mark, and 30 years to reach a 3,600 citations-per-year mark.
He is also a leading researcher in computational fluid-structure interaction (FSI). He has developed and analyzed many highly cited core and special advanced methods and models in FSI. Advanced FSI is becoming an important research and application area in civil engineering, and includes analysis of tall buildings and bridge decks subjected to high winds, onshore and offshore wind turbines under normal and buildings and bridge decks subjected to high winds, onshore and offshore wind turbines under normal and extreme operating conditions, and retrofit and protection of civil structure subjected to blast loadings. Using the methods and software he developed, Bazilevs is successfully addressing important problems in biomechanics, wind energy, and air blast. His wind-turbine FSI simulations are considered to be the best in the world.
Mani Golparvar-Fard, Ph.D., A.M.ASCE,for his cutting-edge research on computer vision data analytics, resulting in a new class of algorithms and techniques mapping in 3D the current state of production on construction projects and exposing waste at both the project and task levels in the construction industry.
Golparvar-Fard’s research over the past decade has focused entirely on creating the theoretical foundation for a project controls system that improves understanding of how construction performance can be captured, communicated, and analyzed in the form of a production system: a system that predicts the reliability of the weekly work plan and look-ahead schedule, supports root-cause assessment on plan failure at both project and task level, facilitates information flows, and decentralizes decision making.
His research has contributed to many aspects of such a system, particularly his research on computer vision data analytics. Using deep learning techniques, he has also led pioneering research on developing machine learning and predictive analytics that compare the current state of production with plans to gauge deviations and forecast reliability in the future state of production on construction sites. His methods can highlight potential issues in a location-driven scheme, support collaborative decision making that eliminates root causes of waste, provide visual interface collaborative decision making to eliminate root causes of waste, and provide visual interfaces between people and information that enable effective pull flow, decentralize work tracking, and facilitate in-process quality control and hand-overs among contractors. To better understand the root causes for poor coordination of tasks on a jobsite, he has also created nonintrusive computer vision algorithms that detect, track, and analyze worker and equipment activities, form crew-balance and operational charts, and underline critical factors that require attention by project management to improve productivity.
To better appreciate the creativity and uniqueness in his interdisciplinary work, one should note the characteristics of the data behind these algorithms. To ensure that the implementation of such techniques does not take away from actual productivity on construction sites, Golparvar-Fard has built these techniques by extending the value of 4D building information models (BIM) commonly used for constructability reviews as a benchmark for performance. Likewise, his work leverages images and videos, frequently collected and indexed by project participants or professional services via consumer grade, time-lapse, smartphone cameras and unmanned aerial vehicles, to visually document actual performances.
Golparvar-Fard’s work clearly contributes to the theory and techniques in production management and can be expanded to manufacturing, transportation, and automotive disciplines. In addition, his core work in visual recognition and reasoning about the geometry and appearance of building elements, their interdependencies, and their context from images, together with his fundamental work in understanding human-object interactions in spatio-temporal data, have also made remarkable impacts on the areas of computer vision and machine learning, all of which is clear from his publications and their citations. Many researchers in the domains of computing in civil engineering and computer vision have already followed in his footsteps, leading the charge in these important research areas.
Zhen Jason He, Ph.D., A.M.ASCE,for pioneering the development of osmotic bioelectrochemical systems for resource recovery from wastes, significantly contributing to innovative technologies for wastewater treatment, and advancing our understanding of interaction between microbe and solid electron acceptors/donors and its potential applications in environmental engineering.
Dr. He has developed and maintained a well-thought-out and focused research program throughout his career. He has undertaken research of national and international importance and of high impact, making noteworthy contributions to the fields of civil and environmental engineering. His work combines development of new, advanced treatment technologies at the nexus of water-nutrients-energy, with particular emphasis on resource recovery in the form of energy, nutrients, and water by a combination of bio, electrochemical, and membrane processes. The elegant combination of the above-stated processes distinguishes He among his peers. His diverse background and varied approach have led him to expand his research horizons by entering into new and innovative areas. He uses new tools to develop techniques and solutions that undoubtedly have and will continue to contribute to a better, more sustainable environment.
The Walter L. Huber Civil Engineering Research Prizes are awarded to members of the Society, in any grade, for notable achievements in research related to civil engineering. Preference is given to younger members (generally under 40 years of age) of early accomplishment who can be expected to continue fruitful careers in research.