Tony Yang

Bio: Tony Yang is an academic researcher from University of British Columbia. The author has contributed to research in topics: Earthquake engineering & Seismic analysis. The author has an hindex of 14, co-authored 120 publications receiving 964 citations. Previous affiliations of Tony Yang include University of California, Berkeley & Sun Yat-sen University.

Seismic Performance Evaluation of Facilities: Methodology and Implementation

Abstract: Performance-based earthquake engineering aims to quantify performance of facilities using metrics that are of immediate use to both engineers and stakeholders. A rigorous yet practical implementation of a performance-based earthquake engineering methodology is developed and demonstrated for an idealized building. The methodology considers seismic hazard, structural response, resulting damage, and repair costs associated with restoring the building to its original condition, using a fully consistent, probabilistic analysis of the associated parts of the problem. The methodology can be generalized to consider other performance measures such as casualties and down time, though these have not been pursued. The proposed procedure is consistent with common building design, construction, and analysis practices such that it can be readily adopted in earthquake engineering practice today.

Seismic loss estimation of non-ductile reinforced concrete buildings

Abstract: SUMMARY Non-ductile reinforced concrete buildings represent a prevalent construction type found in many parts of the world. Due to the seismic vulnerability of such buildings, in areas of high seismic activity non-ductile reinforced concrete buildings pose a significant threat to the safety of the occupants and damage to such structures can result in large financial losses. This paper introduces advanced analytical models that can be used to simulate the nonlinear dynamic response of these structural systems, including collapse. The state-of-the-art loss simulation procedure developed for new buildings is extended to estimate the expected losses of existing non-ductile concrete buildings considering their vulnerability to collapse. Three criteria for collapse, namely first component failure, side-sway collapse, and gravity-load collapse, are considered in determining the probability of collapse and the assessment of financial losses. A detailed example is presented using a seven-story non-ductile reinforced concrete frame building located in the Los Angeles, California. Copyright © 2012 John Wiley & Sons, Ltd.

Application of the FEMA-P58 methodology for regional earthquake loss prediction

Abstract: Earthquake-induced building collapses and casualties have been effectively controlled in the last two decades. However, earthquake-induced economic losses have continued to rise. Following the objective and procedure of next-generation performance-based seismic design, the economic loss prediction method proposed by FEMA-P58 is extended to regional earthquake loss prediction in this study. The engineering demand parameters for a large number of buildings within a region are efficiently obtained through nonlinear time history analysis using multi-story concentrated-mass shear models. The building data, including structural and nonstructural components, are obtained through field investigation, structural and architectural drawings, and default database published in the FEMA-P58 document. A case study of Tsinghua University campus in Beijing is performed to demonstrate the implementation and advantage using proposed FEMA-P58 method for regional earthquake loss prediction. The results show the advancement in loss simulation for a region, and in identifying the influence of the different ground motion characteristics (e.g., velocity pulse) on the regional loss.

The Environmental Protection Agency

Abstract: The Environmental Protection Agency (EPA) provides access to information on a variety of topics related to the environment and strives to inform citizens of health risks. The EPA also has an extensive library network that consists of 26 libraries throughout the United States, which provide access to a plethora of information to EPA employees, scientists, and researchers. The EPA implemented a reorganization project to digitize their materials so they would be more accessible to a wider range of users, but this plan was drastically accelerated when the EPA was threatened with a budget cut. It chose to close and reduce the hours and services of some of their libraries. As a result, the agency was accused of denying users the “right to know” by making information unavailable, not providing an adequate strategic plan, and discarding vital materials. This case study explores the background of the digitization project, the practices implemented, and the critiques of the project.

Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix

Abstract: Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.

Minimum Design Loads for Buildings and Other Structures

Abstract: Minimum Design Loads for Buildings and Other Structures provides requirements for general structural design and includes means for determining dead, live, soil, flood, wind, snow, rain, atmospheric ice, and earthquake loads, as well as their combinations, which are suitable for inclusion in building codes and other documents. This Standard, a revision of ASCE/SEI 7-05, offers a complete update and reorganization of the wind load provisions, expanding them from one chapter into six. The Standard contains new ultimate event wind maps with corresponding reductions in load factors, so that the loads are not affected, and updates the seismic loads with new risk-targeted seismic maps. The snow, live, and atmospheric icing provisions are updated as well. In addition, the Standard includes a detailed Commentary with explanatory and supplementary information designed to assist building code committees and regulatory authorities. Standard ASCE/SEI 7 is an integral part of building codes in the United States. Many of the load provisions are substantially adopted by reference in the International Building Code and the NFPA 5000 Building Construction and Safety Code. Structural engineers, architects, and those engaged in preparing and administering local building codes will find this Standard an essential reference in their practice. Note: New orders are fulfilled from the second printing, which incorporates the errata to the first printing.

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review.

Abstract: Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.