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Parallel finite element modeling of earthquake ground response and liquefaction

Earthquake Engineering and Engineering Vibration 2004年第1期3卷 23-37页

作者：陆金池彭军 Ahmed Elgamal 杨朝晖 kincho h.law Department of Structural Engineering University of California Department of Civil and Enviromental Engineering Stanford University Department of Civil and Enviromental Engineering Stanford University

Parallel computing is a promising approach to alleviate the computational demand in conducting large-scale finite element *** paper presents a numerical modeling approach for earthquake ground response and liquefaction using the parallel nonlinear finite element program,ParCYCLIC,designed for distributed-memory message-passing parallel computer *** ParCYCLIC,finite elements are employed within an incremental plasticity,coupled solid-fluid formulation,A constitutive model calibrated by physical tests represents the salient characteristics of sand liquefaction and associated accumulation of shear *** elements of the computational strategy employed in ParCYCLIC include the development of a parallel sparse direct solver,the deployment of an automatic domain decomposer,and the use of the Multilevel Nested Dissection algorithm for ordering of the finite element *** results of centrifuge test models using ParCYCLIC are *** results from grid models and geotechnical simulations show that ParCYCLIC is efficiently scalable to a large number of processors.

关键词： parallel finite element domain decomposition liquefaction parallel speedup earthquake site amplification

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Utilizing Statistical Semantic Similarity Techniques for Ontology Mapping——with Applications to AEC Standard Models

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Tsinghua Science and Technology 2008年第S1期13卷 217-222页

作者： Chin-Pang Jack Cheng Gloria T. Lau kincho h. law Engineering Informatics Group Stanford University

The objective of this paper is to introduce three semi-automated approaches for ontology mapping using relatedness analysis techniques. In the architecture, engineering, and construction (AEC) industry, there exist a number of ontological standards to describe the semantics of building models. Although the standards share similar scopes of interest, the task of comparing and mapping concepts among standards is challenging due to their differences in terminologies and perspectives. Ontology mapping is therefore necessary to achieve information interoperability, which allows two or more information sources to exchange data and to re-use the data for further purposes. The attribute-based approach, corpus-based approach, and name-based approach presented in this paper adopt the statistical relatedness analysis techniques to discover related concepts from heterogeneous ontologies. A pilot study is conducted on IFC and CIS/2 ontologies to evaluate the approaches. Preliminary results show that the attribute-based approach outperforms the other two approaches in terms of precision and F-measure.

关键词： ontology mapping similarity analysis information interoperation statistical analysis techniques

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Rapid visual screening of soft-story buildings from street view images using deep learning classification

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Earthquake Engineering and Engineering Vibration 2020年第4期19卷 827-838页

作者： Qian Yu Chaofeng Wang Frank McKenna Stella XYu Ertugrul Taciroglu Barbaros Cetiner kincho hlaw International Computer Science Institute University of CaliforniaBerkeleyCAUSA Department of Civil and Environmental Engineering University of CaliforniaBerkeleyCAUSA Civil and Environmental Engineering University of CaliforniaLos AngelesCAUSA Civil and Environmental Engineering Stanford UniversityCAUSA

Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a region. In the case of multi-story structures, abrupt vertical variations of story stiffness are known to significantly increase the likelihood of collapse during moderate or severe earthquakes. Identifying and retrofitting buildings with such irregularities—generally termed as soft-story buildings—is, therefore, vital in earthquake preparedness and loss mitigation efforts. Soft-story building identification through conventional means is a labor-intensive and time-consuming process. In this study, an automated procedure was devised based on deep learning techniques for identifying soft-story buildings from street-view images at a regional scale. A database containing a large number of building images and a semi-automated image labeling approach that effectively annotates new database entries was developed for developing the deep learning model. Extensive computational experiments were carried out to examine the effectiveness of the proposed procedure, and to gain insights into automated soft-story building identification.

关键词： soft-story building deep learning CNN rapid visual screening street view image

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A Web Service Framework for Environmental and Carbon Footprint Monitoring in Construction Supply Chains

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Journal of Civil Engineering and Architecture 2011年第1期5卷 1-12页

作者： Jack C. P. Cheng kincho h. law Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Civil and Environmental Engineering Stanford University Stanford California USA

With the growing environmental concerns, green supply chain management (GSCM) is gaining significant attention in the construction industry. Tracking and monitoring the environmental effects brought forth by the participating members along a supply chain is important to GSCM. The GreenSCOR model developed by the Supply Chain Council provides a generic framework for measuring the total carbon footprint and environmental footprint in a supply chain. The model is based on the Supply Chain Operations Reference (SCOR) model, which represents a supply chain network in a hierarchically structured manner. This paper describes the GreenSCOR framework and its potential application to the construction industry. This paper also presents a web services approach to incorporate the GreenSCOR model to the implementation of collaborative information systems. Each process element in the SCOR model is represented and delivered as individual web service units, which can be reused and integrated using standard web services technologies. The service units are combined and managed in a prototype web service collaborative framework, called SC Collaborator, which is designed and developed for supporting construction supply chain management. An illustrative example is presented to demonstrate the implementation of the GreenSCOR-based SC Collaborator framework.

关键词：绿色供应链管理 Web服务技术服务框架环境问题建筑行业总碳网络服务可重复使用

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