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dc.contributor.advisorChang, David Y.
dc.creatorAlsugair, Abdullah Mohammed
dc.date.accessioned2024-02-09T20:48:04Z
dc.date.available2024-02-09T20:48:04Z
dc.date.issued1992
dc.identifier.urihttps://hdl.handle.net/1969.1/DISSERTATIONS-1281164
dc.descriptionTypescript (photocopy)en
dc.descriptionVitaen
dc.descriptionMajor subject: Civil Engineeringen
dc.description.abstractThe main objectives of this research are investigating the process planning knowledge of allocating construction resources for earthmoving processes and capturing this knowledge in an Intelligent Resource Allocation System (IRAS). The specific focuses of this research are capturing planning knowledge in selecting an appropriate equipment class, capturing planning experiences of allocating construction resources, and automating simulation studies. IRAS consists of three modules to accomplish the focuses of this research. These modules are an expert system, a pattern classification, and a Goal Driven Simulation System (GDSS). The functions of the expert system module are to capture planning knowledge in selecting a construction technology (e.g. scrapers) and an equipment class (e.g. conventional class of scrapers) and to recommend an appropriate equipment class based on process data (e.g. travel distance). The functions of the pattern classification modules are to capture the experiences of allocating construction resource, to recommend a preliminary resource assignment, and to adapt new experiences. An Artificial Neural Network (ANN) using Backpropagation algorithm is used as the pattern classification tool. A justification for selecting ANN and Backpropagation algorithm is presented. The functions of the GDSS module are to allocate a final resource assignment and to automate simulation studies. GDSS consists of an integration of a simulation program and an analysis program that analyzes a simulation output and synthesizes an appropriate resource assignment. GDSS reduces the user's knowledge requirements in conducting simulation studies to specifying the goal (e.g. moving 300 CY/HR), the model data, and the number of equipment units. Using the goal as the ultimate objective, GDSS conducts a simulation study through alternating between the simulation program and the analysis program until the goal is satisfied and the assigned equipment are effectively utilized. The analysis and simulation programs are developed using C_language. The modules of IRAS prototype have been validated using an earthmoving process. GDSS module is restricted to automate the execution and analysis phases of a simulation study. Research results demonstrated that IRAS prototype shows a viable first phase toward fully automating the resource allocation process.en
dc.format.extentxii, 249 leavesen
dc.format.mediumelectronicen
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.rightsThis thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use.en
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectMajor civil engineeringen
dc.subject.classification1992 Dissertation A464
dc.subject.lcshConstruction industryen
dc.subject.lcshManagementen
dc.subject.lcshData processingen
dc.subject.lcshResource allocationen
dc.subject.lcshExpert systems (Computer science)en
dc.titleAn intelligent resource allocation systemen
dc.typeThesisen
thesis.degree.disciplineCivil Engineeringen
thesis.degree.grantorTexas A&M Universityen
thesis.degree.nameDoctor of Philosophyen
thesis.degree.namePh. Den
thesis.degree.levelDoctorialen
dc.contributor.committeeMemberAnderson, Stuart
dc.contributor.committeeMemberHancher, Donn E.
dc.contributor.committeeMemberKehtarnavaz, Nasser
dc.type.genredissertationsen
dc.type.materialtexten
dc.format.digitalOriginreformatted digitalen
dc.publisher.digitalTexas A&M University. Libraries
dc.identifier.oclc27720509


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