Simulation self-diagnoses
After an initial simulation model is created for a construction process, it still involves a time-consuming and difficult debugging process to identify and correct errors in the model until a valid experiment is obtained. Both comprehensive knowledge and hands-on experience are essential in achievin...
Gespeichert in:
Veröffentlicht in: | Automation in construction 2003-07, Vol.12 (4), p.419-430 |
---|---|
1. Verfasser: | |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 430 |
---|---|
container_issue | 4 |
container_start_page | 419 |
container_title | Automation in construction |
container_volume | 12 |
creator | Shi, Jonathan Jingsheng |
description | After an initial simulation model is created for a construction process, it still involves a time-consuming and difficult debugging process to identify and correct errors in the model until a valid experiment is obtained. Both comprehensive knowledge and hands-on experience are essential in achieving efficiency in this labor-intensive process. This research presents the simulation self-diagnosis methods based on the general role that simulation entities play in advancing a simulation, in which entities dynamically flow in the model and activate activities to operate as the simulation time advances. A valid simulation requires that all entities must flow in correct patterns and all activities must be correctly executed in the experiment. The presented self-diagnosis methodology consists of two separate stages: model compilation and runtime diagnosis. Compiling a model intends to examine the matching relations between modeling elements in the model. Diagnosing an experiment at runtime explores any abnormally executed activities and then to search for corresponding causes. Both stages can pinpoint errors in the model and suggest corresponding corrective measures. An example is used to illustrate the improved debugging process with the enhanced self-diagnosis function. |
doi_str_mv | 10.1016/S0926-5805(03)00015-3 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_27974117</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0926580503000153</els_id><sourcerecordid>27974117</sourcerecordid><originalsourceid>FETCH-LOGICAL-c316t-b292273a1605963421b656d4780c81f185890e80e4966e40d17d1e577c3f16563</originalsourceid><addsrcrecordid>eNqFkEtLAzEUhYMoWKs_wIXgRtHF6L3J5LUSKb6g4KK6DmnmjkSmMzWZCv57p7bo0tXdfOcezsfYCcIVAqrrGViuCmlAXoC4BACUhdhhIzSaF9pY3GWjX2SfHeT8PkAalB2x41lcrBrfx649zdTURRX9W9tlyodsr_ZNpqPtHbPX-7uXyWMxfX54mtxOiyBQ9cWcW8618KhAWiVKjnMlVVVqA8FgjUYaC2SASqsUlVChrpCk1kHUOJBizM43f5ep-1hR7t0i5kBN41vqVtlxbXWJqAdQbsCQupwT1W6Z4sKnL4fg1iLcjwi3XulAuB8RTgy5s22Bz8E3dfJtiPkvXGqFksPA3Ww4GtZ-Rkouh0htoComCr2ruvhP0zc6tW5Y</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>27974117</pqid></control><display><type>article</type><title>Simulation self-diagnoses</title><source>ScienceDirect</source><creator>Shi, Jonathan Jingsheng</creator><creatorcontrib>Shi, Jonathan Jingsheng</creatorcontrib><description>After an initial simulation model is created for a construction process, it still involves a time-consuming and difficult debugging process to identify and correct errors in the model until a valid experiment is obtained. Both comprehensive knowledge and hands-on experience are essential in achieving efficiency in this labor-intensive process. This research presents the simulation self-diagnosis methods based on the general role that simulation entities play in advancing a simulation, in which entities dynamically flow in the model and activate activities to operate as the simulation time advances. A valid simulation requires that all entities must flow in correct patterns and all activities must be correctly executed in the experiment. The presented self-diagnosis methodology consists of two separate stages: model compilation and runtime diagnosis. Compiling a model intends to examine the matching relations between modeling elements in the model. Diagnosing an experiment at runtime explores any abnormally executed activities and then to search for corresponding causes. Both stages can pinpoint errors in the model and suggest corresponding corrective measures. An example is used to illustrate the improved debugging process with the enhanced self-diagnosis function.</description><identifier>ISSN: 0926-5805</identifier><identifier>EISSN: 1872-7891</identifier><identifier>DOI: 10.1016/S0926-5805(03)00015-3</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Applied sciences ; Buildings. Public works ; Computation methods. Tables. Charts ; Computer simulation ; Construction planning ; Construction process ; Construction works ; Exact sciences and technology ; Experimentation ; Project management. Process of design ; Simulation of construction operations ; Site organization ; Structural analysis. Stresses ; Validation and verification</subject><ispartof>Automation in construction, 2003-07, Vol.12 (4), p.419-430</ispartof><rights>2003 Elsevier Science B.V.</rights><rights>2003 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c316t-b292273a1605963421b656d4780c81f185890e80e4966e40d17d1e577c3f16563</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0926580503000153$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27903,27904,65309</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=14761520$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Shi, Jonathan Jingsheng</creatorcontrib><title>Simulation self-diagnoses</title><title>Automation in construction</title><description>After an initial simulation model is created for a construction process, it still involves a time-consuming and difficult debugging process to identify and correct errors in the model until a valid experiment is obtained. Both comprehensive knowledge and hands-on experience are essential in achieving efficiency in this labor-intensive process. This research presents the simulation self-diagnosis methods based on the general role that simulation entities play in advancing a simulation, in which entities dynamically flow in the model and activate activities to operate as the simulation time advances. A valid simulation requires that all entities must flow in correct patterns and all activities must be correctly executed in the experiment. The presented self-diagnosis methodology consists of two separate stages: model compilation and runtime diagnosis. Compiling a model intends to examine the matching relations between modeling elements in the model. Diagnosing an experiment at runtime explores any abnormally executed activities and then to search for corresponding causes. Both stages can pinpoint errors in the model and suggest corresponding corrective measures. An example is used to illustrate the improved debugging process with the enhanced self-diagnosis function.</description><subject>Applied sciences</subject><subject>Buildings. Public works</subject><subject>Computation methods. Tables. Charts</subject><subject>Computer simulation</subject><subject>Construction planning</subject><subject>Construction process</subject><subject>Construction works</subject><subject>Exact sciences and technology</subject><subject>Experimentation</subject><subject>Project management. Process of design</subject><subject>Simulation of construction operations</subject><subject>Site organization</subject><subject>Structural analysis. Stresses</subject><subject>Validation and verification</subject><issn>0926-5805</issn><issn>1872-7891</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><recordid>eNqFkEtLAzEUhYMoWKs_wIXgRtHF6L3J5LUSKb6g4KK6DmnmjkSmMzWZCv57p7bo0tXdfOcezsfYCcIVAqrrGViuCmlAXoC4BACUhdhhIzSaF9pY3GWjX2SfHeT8PkAalB2x41lcrBrfx649zdTURRX9W9tlyodsr_ZNpqPtHbPX-7uXyWMxfX54mtxOiyBQ9cWcW8618KhAWiVKjnMlVVVqA8FgjUYaC2SASqsUlVChrpCk1kHUOJBizM43f5ep-1hR7t0i5kBN41vqVtlxbXWJqAdQbsCQupwT1W6Z4sKnL4fg1iLcjwi3XulAuB8RTgy5s22Bz8E3dfJtiPkvXGqFksPA3Ww4GtZ-Rkouh0htoComCr2ruvhP0zc6tW5Y</recordid><startdate>20030701</startdate><enddate>20030701</enddate><creator>Shi, Jonathan Jingsheng</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope></search><sort><creationdate>20030701</creationdate><title>Simulation self-diagnoses</title><author>Shi, Jonathan Jingsheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-b292273a1605963421b656d4780c81f185890e80e4966e40d17d1e577c3f16563</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Applied sciences</topic><topic>Buildings. Public works</topic><topic>Computation methods. Tables. Charts</topic><topic>Computer simulation</topic><topic>Construction planning</topic><topic>Construction process</topic><topic>Construction works</topic><topic>Exact sciences and technology</topic><topic>Experimentation</topic><topic>Project management. Process of design</topic><topic>Simulation of construction operations</topic><topic>Site organization</topic><topic>Structural analysis. Stresses</topic><topic>Validation and verification</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shi, Jonathan Jingsheng</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Automation in construction</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shi, Jonathan Jingsheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Simulation self-diagnoses</atitle><jtitle>Automation in construction</jtitle><date>2003-07-01</date><risdate>2003</risdate><volume>12</volume><issue>4</issue><spage>419</spage><epage>430</epage><pages>419-430</pages><issn>0926-5805</issn><eissn>1872-7891</eissn><abstract>After an initial simulation model is created for a construction process, it still involves a time-consuming and difficult debugging process to identify and correct errors in the model until a valid experiment is obtained. Both comprehensive knowledge and hands-on experience are essential in achieving efficiency in this labor-intensive process. This research presents the simulation self-diagnosis methods based on the general role that simulation entities play in advancing a simulation, in which entities dynamically flow in the model and activate activities to operate as the simulation time advances. A valid simulation requires that all entities must flow in correct patterns and all activities must be correctly executed in the experiment. The presented self-diagnosis methodology consists of two separate stages: model compilation and runtime diagnosis. Compiling a model intends to examine the matching relations between modeling elements in the model. Diagnosing an experiment at runtime explores any abnormally executed activities and then to search for corresponding causes. Both stages can pinpoint errors in the model and suggest corresponding corrective measures. An example is used to illustrate the improved debugging process with the enhanced self-diagnosis function.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/S0926-5805(03)00015-3</doi><tpages>12</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0926-5805 |
ispartof | Automation in construction, 2003-07, Vol.12 (4), p.419-430 |
issn | 0926-5805 1872-7891 |
language | eng |
recordid | cdi_proquest_miscellaneous_27974117 |
source | ScienceDirect |
subjects | Applied sciences Buildings. Public works Computation methods. Tables. Charts Computer simulation Construction planning Construction process Construction works Exact sciences and technology Experimentation Project management. Process of design Simulation of construction operations Site organization Structural analysis. Stresses Validation and verification |
title | Simulation self-diagnoses |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-27T21%3A57%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Simulation%20self-diagnoses&rft.jtitle=Automation%20in%20construction&rft.au=Shi,%20Jonathan%20Jingsheng&rft.date=2003-07-01&rft.volume=12&rft.issue=4&rft.spage=419&rft.epage=430&rft.pages=419-430&rft.issn=0926-5805&rft.eissn=1872-7891&rft_id=info:doi/10.1016/S0926-5805(03)00015-3&rft_dat=%3Cproquest_cross%3E27974117%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=27974117&rft_id=info:pmid/&rft_els_id=S0926580503000153&rfr_iscdi=true |