Structural Engineering

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Yi Liang - One of the best experts on this subject based on the ideXlab platform.

  • GCC - Ontology Based Knowledge Modeling for Structural Engineering Experiment Information Management
    2010 Ninth International Conference on Grid and Cloud Computing, 2010
    Co-Authors: Xiaohui Zhang, Ruihua Di, Yi Liang
    Abstract:

    Structural Engineering experiment plays an important role in the civil infrastructure design and research. The diversity and heterogeneity of information representation among multiple experimental sites makes the experiment information integration difficult and lead to the poor accuracy when making the keyword matching-based information ret rival. Aiming on this issue, an ontology-based knowledge model called SEKM is proposed in this paper. Based on the domain knowledge, SEKM is composed of the concept model SEDO(Structural Engineering Domain Ontology) and the rule base SERB(Structural Engineering Rule Base), and provide the uniform experiment information representation in the Structural Engineering field. To enhance the knowledge representation power of SEKM, an evolution-based rule base optimization method is present, which enrich the rule base with the online analysis of the statistical information about SEKM accessing. SEKM is initially implemented based on OWL 2 specification and has been adopted in the experiment information management by Structural Engineering Experimental Center of Beijing University of Technology.

  • Ontology Based Knowledge Modeling for Structural Engineering Experiment Information Management
    2010 Ninth International Conference on Grid and Cloud Computing, 2010
    Co-Authors: Xiaohui Zhang, Ruihua Di, Yi Liang
    Abstract:

    Structural Engineering experiment plays an important role in the civil infrastructure design and research. The diversity and heterogeneity of information representation among multiple experimental sites makes the experiment information integration difficult and lead to the poor accuracy when making the keyword matching-based information ret rival. Aiming on this issue, an ontology-based knowledge model called SEKM is proposed in this paper. Based on the domain knowledge, SEKM is composed of the concept model SEDO(Structural Engineering Domain Ontology) and the rule base SERB(Structural Engineering Rule Base), and provide the uniform experiment information representation in the Structural Engineering field. To enhance the knowledge representation power of SEKM, an evolution-based rule base optimization method is present, which enrich the rule base with the online analysis of the statistical information about SEKM accessing. SEKM is initially implemented based on OWL 2 specification and has been adopted in the experiment information management by Structural Engineering Experimental Center of Beijing University of Technology.

  • MapReduce based query of Structural Engineering experimental data
    The 2nd International Conference on Information Science and Engineering, 2010
    Co-Authors: Xiaohui Zhang, Ruihua Di, Yi Liang, Cuicui Yang
    Abstract:

    Structural Engineering Cloud Platform (SECP) is a resource sharing platform oriented to Structural Engineering domain, and the share of Structural Engineering experimental data is one of its main functions. In SECP, there are two kinds of experimental data, which are Experimental Description Data (EDD) and Experimental Result Data (ERD). The model of EDD was built based on OWL ontology for shielding the heterogeneity of experimental data from different experimental sites. With the increasing of data scale, the query of EDD has been a bottleneck of the whole management system in SECP. Based on MapReduce, this paper designed and implemented a EDD query system (EDDQS). EDDQS supports query based on SPARQL and there are four core modules named Data Splitter, Query Generator, Job Generator as well as Job Executor. Furthermore, the algorithm of job generation from SPARQL query and job execution algorithms are presented. Finally, the results of our experiments reveal that our MapReduce based query system can handle large amount of EDD efficiently.

  • Grid-Enabled Distributed Structural Engineering Experiment Management
    The Third ChinaGrid Annual Conference (chinagrid 2008), 2008
    Co-Authors: Yi Liang, Ruihua Di, Hao Long, Wang Li, Zheng Zhang, Zijun Weng
    Abstract:

    In this paper, Structural Engineering experiment grid (SEE-grid) is proposed. The goal of SEE-grid is to pool together structure Engineering facilities and experimental data and knowledge among physically and administratively distributed communities. The service-oriented, hierarchical architecture of SEE-grid is present. The key issues of SEE-grid, including the experiment management and data management, are discussed in detail. Finally, the portal of SEE-grid is demonstrated and the result of a geographically distributed Structural Engineering experiment is showed via SEE-grid portal.

Xiaohui Zhang - One of the best experts on this subject based on the ideXlab platform.

  • GCC - Ontology Based Knowledge Modeling for Structural Engineering Experiment Information Management
    2010 Ninth International Conference on Grid and Cloud Computing, 2010
    Co-Authors: Xiaohui Zhang, Ruihua Di, Yi Liang
    Abstract:

    Structural Engineering experiment plays an important role in the civil infrastructure design and research. The diversity and heterogeneity of information representation among multiple experimental sites makes the experiment information integration difficult and lead to the poor accuracy when making the keyword matching-based information ret rival. Aiming on this issue, an ontology-based knowledge model called SEKM is proposed in this paper. Based on the domain knowledge, SEKM is composed of the concept model SEDO(Structural Engineering Domain Ontology) and the rule base SERB(Structural Engineering Rule Base), and provide the uniform experiment information representation in the Structural Engineering field. To enhance the knowledge representation power of SEKM, an evolution-based rule base optimization method is present, which enrich the rule base with the online analysis of the statistical information about SEKM accessing. SEKM is initially implemented based on OWL 2 specification and has been adopted in the experiment information management by Structural Engineering Experimental Center of Beijing University of Technology.

  • Data Management in Structural Engineering Experiment Grid
    2010 Fifth Annual ChinaGrid Conference, 2010
    Co-Authors: Xiaohui Zhang, Ruihua Di, Zijun Weng
    Abstract:

    In Structural Engineering Experiment Grid (SEEGrid), there are huge amounts of heterogeneous data distributed in different experimental sites. This paper summarizes a conceptual model of the experimental data, and based on this conceptual model, Structural Engineering Experimental Data Management Framework (SEEDMF) is proposed to manage the structured data and unstructured. In addition, the heterogeneity of structured data, which can be overcome by SEEDMF, is discussed in detail. Finally, the portal of Engineering Experimental Data Management System(SEEDMS) which is a part of SEE-Grid is demonstrated.

  • ChinaGrid - Data Management in Structural Engineering Experiment Grid
    2010 Fifth Annual ChinaGrid Conference, 2010
    Co-Authors: Xiaohui Zhang, Ruihua Di, Zijun Weng
    Abstract:

    In Structural Engineering Experiment Grid (SEEGrid), there are huge amounts of heterogeneous data distributed in different experimental sites. This paper summarizes a conceptual model of the experimental data, and based on this conceptual model, Structural Engineering Experimental Data Management Framework (SEEDMF) is proposed to manage the structured data and unstructured. In addition, the heterogeneity of structured data, which can be overcome by SEEDMF, is discussed in detail. Finally, the portal of Engineering Experimental Data Management System(SEEDMS) which is a part of SEE-Grid is demonstrated.

  • Ontology Based Knowledge Modeling for Structural Engineering Experiment Information Management
    2010 Ninth International Conference on Grid and Cloud Computing, 2010
    Co-Authors: Xiaohui Zhang, Ruihua Di, Yi Liang
    Abstract:

    Structural Engineering experiment plays an important role in the civil infrastructure design and research. The diversity and heterogeneity of information representation among multiple experimental sites makes the experiment information integration difficult and lead to the poor accuracy when making the keyword matching-based information ret rival. Aiming on this issue, an ontology-based knowledge model called SEKM is proposed in this paper. Based on the domain knowledge, SEKM is composed of the concept model SEDO(Structural Engineering Domain Ontology) and the rule base SERB(Structural Engineering Rule Base), and provide the uniform experiment information representation in the Structural Engineering field. To enhance the knowledge representation power of SEKM, an evolution-based rule base optimization method is present, which enrich the rule base with the online analysis of the statistical information about SEKM accessing. SEKM is initially implemented based on OWL 2 specification and has been adopted in the experiment information management by Structural Engineering Experimental Center of Beijing University of Technology.

  • MapReduce based query of Structural Engineering experimental data
    The 2nd International Conference on Information Science and Engineering, 2010
    Co-Authors: Xiaohui Zhang, Ruihua Di, Yi Liang, Cuicui Yang
    Abstract:

    Structural Engineering Cloud Platform (SECP) is a resource sharing platform oriented to Structural Engineering domain, and the share of Structural Engineering experimental data is one of its main functions. In SECP, there are two kinds of experimental data, which are Experimental Description Data (EDD) and Experimental Result Data (ERD). The model of EDD was built based on OWL ontology for shielding the heterogeneity of experimental data from different experimental sites. With the increasing of data scale, the query of EDD has been a bottleneck of the whole management system in SECP. Based on MapReduce, this paper designed and implemented a EDD query system (EDDQS). EDDQS supports query based on SPARQL and there are four core modules named Data Splitter, Query Generator, Job Generator as well as Job Executor. Furthermore, the algorithm of job generation from SPARQL query and job execution algorithms are presented. Finally, the results of our experiments reveal that our MapReduce based query system can handle large amount of EDD efficiently.

Ruihua Di - One of the best experts on this subject based on the ideXlab platform.

  • GCC - Ontology Based Knowledge Modeling for Structural Engineering Experiment Information Management
    2010 Ninth International Conference on Grid and Cloud Computing, 2010
    Co-Authors: Xiaohui Zhang, Ruihua Di, Yi Liang
    Abstract:

    Structural Engineering experiment plays an important role in the civil infrastructure design and research. The diversity and heterogeneity of information representation among multiple experimental sites makes the experiment information integration difficult and lead to the poor accuracy when making the keyword matching-based information ret rival. Aiming on this issue, an ontology-based knowledge model called SEKM is proposed in this paper. Based on the domain knowledge, SEKM is composed of the concept model SEDO(Structural Engineering Domain Ontology) and the rule base SERB(Structural Engineering Rule Base), and provide the uniform experiment information representation in the Structural Engineering field. To enhance the knowledge representation power of SEKM, an evolution-based rule base optimization method is present, which enrich the rule base with the online analysis of the statistical information about SEKM accessing. SEKM is initially implemented based on OWL 2 specification and has been adopted in the experiment information management by Structural Engineering Experimental Center of Beijing University of Technology.

  • Data Management in Structural Engineering Experiment Grid
    2010 Fifth Annual ChinaGrid Conference, 2010
    Co-Authors: Xiaohui Zhang, Ruihua Di, Zijun Weng
    Abstract:

    In Structural Engineering Experiment Grid (SEEGrid), there are huge amounts of heterogeneous data distributed in different experimental sites. This paper summarizes a conceptual model of the experimental data, and based on this conceptual model, Structural Engineering Experimental Data Management Framework (SEEDMF) is proposed to manage the structured data and unstructured. In addition, the heterogeneity of structured data, which can be overcome by SEEDMF, is discussed in detail. Finally, the portal of Engineering Experimental Data Management System(SEEDMS) which is a part of SEE-Grid is demonstrated.

  • ChinaGrid - Data Management in Structural Engineering Experiment Grid
    2010 Fifth Annual ChinaGrid Conference, 2010
    Co-Authors: Xiaohui Zhang, Ruihua Di, Zijun Weng
    Abstract:

    In Structural Engineering Experiment Grid (SEEGrid), there are huge amounts of heterogeneous data distributed in different experimental sites. This paper summarizes a conceptual model of the experimental data, and based on this conceptual model, Structural Engineering Experimental Data Management Framework (SEEDMF) is proposed to manage the structured data and unstructured. In addition, the heterogeneity of structured data, which can be overcome by SEEDMF, is discussed in detail. Finally, the portal of Engineering Experimental Data Management System(SEEDMS) which is a part of SEE-Grid is demonstrated.

  • Ontology Based Knowledge Modeling for Structural Engineering Experiment Information Management
    2010 Ninth International Conference on Grid and Cloud Computing, 2010
    Co-Authors: Xiaohui Zhang, Ruihua Di, Yi Liang
    Abstract:

    Structural Engineering experiment plays an important role in the civil infrastructure design and research. The diversity and heterogeneity of information representation among multiple experimental sites makes the experiment information integration difficult and lead to the poor accuracy when making the keyword matching-based information ret rival. Aiming on this issue, an ontology-based knowledge model called SEKM is proposed in this paper. Based on the domain knowledge, SEKM is composed of the concept model SEDO(Structural Engineering Domain Ontology) and the rule base SERB(Structural Engineering Rule Base), and provide the uniform experiment information representation in the Structural Engineering field. To enhance the knowledge representation power of SEKM, an evolution-based rule base optimization method is present, which enrich the rule base with the online analysis of the statistical information about SEKM accessing. SEKM is initially implemented based on OWL 2 specification and has been adopted in the experiment information management by Structural Engineering Experimental Center of Beijing University of Technology.

  • MapReduce based query of Structural Engineering experimental data
    The 2nd International Conference on Information Science and Engineering, 2010
    Co-Authors: Xiaohui Zhang, Ruihua Di, Yi Liang, Cuicui Yang
    Abstract:

    Structural Engineering Cloud Platform (SECP) is a resource sharing platform oriented to Structural Engineering domain, and the share of Structural Engineering experimental data is one of its main functions. In SECP, there are two kinds of experimental data, which are Experimental Description Data (EDD) and Experimental Result Data (ERD). The model of EDD was built based on OWL ontology for shielding the heterogeneity of experimental data from different experimental sites. With the increasing of data scale, the query of EDD has been a bottleneck of the whole management system in SECP. Based on MapReduce, this paper designed and implemented a EDD query system (EDDQS). EDDQS supports query based on SPARQL and there are four core modules named Data Splitter, Query Generator, Job Generator as well as Job Executor. Furthermore, the algorithm of job generation from SPARQL query and job execution algorithms are presented. Finally, the results of our experiments reveal that our MapReduce based query system can handle large amount of EDD efficiently.

Kimberly Lammert - One of the best experts on this subject based on the ideXlab platform.

  • Reviving Art and Practice in Structural Engineering
    2020
    Co-Authors: Kimberly Lammert
    Abstract:

    Structural Engineering is both an art and applied science. Traditionally, students of Structural Engineering were exposed to the art along with the science of the profession by teachers who themselves were practitioners of that art. Over the past thirty years, the art of Structural Engineering has been almost completely replaced by science, which many times is performed for its own sake. This growing emphasis on "studying" rather than "creating" is leading to a crisis in the Structural Engineering profession. This paper makes a case for reform by reviving art and practice in Structural Engineering edu- cation, and calls for separation of Structural Engineering education from general civil en- gineering undergraduate programs.

  • Reviving Art and Practice in Structural Engineering Education
    Leadership and Management in Engineering, 2008
    Co-Authors: Thomas Sputo, Kimberly Lammert
    Abstract:

    Structural Engineering is both an art and applied science. Traditionally, students of Structural Engineering were exposed to the art along with the science of the profession by teachers who themselves were practitioners of that art. Over the past thirty years, the art of Structural Engineering has been almost completely replaced by science, which many times is performed for its own sake. This growing emphasis on “studying” rather than “creating” is leading to a crisis in the Structural Engineering profession. This paper makes a case for reform by reviving art and practice in Structural Engineering education, and calls for separation of Structural Engineering education from general civil Engineering undergraduate programs.

Kathryn Lindsay - One of the best experts on this subject based on the ideXlab platform.

Related terms

  • structural engineering education
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