Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Grouping Pavement Condition Variables for Performance Modeling Using Self‐Organizing Maps
Different modeling techniques have been employed for the evaluation of pavement performance, determination of structural capacity, and performance predictions. The evaluation of performance involves the functional analysis of pavements based on the history of the riding quality. The riding comfort and pavement performance can be conveniently defined in terms of roughness and pavement distresses. Thus different models have been developed relating roughness with distresses to predict pavement performance. These models are too complex and require parsimonious equations involving fewer variables. Artificial neural networks have been used successfully in the development of performance‐prediction models. This article demonstrates the use of an artificial intelligence neural networks self‐organizing maps for the grouping of pavement condition variables in developing pavement performance models to evaluate pavement conditions on the basis of pavement distresses.
Grouping Pavement Condition Variables for Performance Modeling Using Self‐Organizing Maps
Different modeling techniques have been employed for the evaluation of pavement performance, determination of structural capacity, and performance predictions. The evaluation of performance involves the functional analysis of pavements based on the history of the riding quality. The riding comfort and pavement performance can be conveniently defined in terms of roughness and pavement distresses. Thus different models have been developed relating roughness with distresses to predict pavement performance. These models are too complex and require parsimonious equations involving fewer variables. Artificial neural networks have been used successfully in the development of performance‐prediction models. This article demonstrates the use of an artificial intelligence neural networks self‐organizing maps for the grouping of pavement condition variables in developing pavement performance models to evaluate pavement conditions on the basis of pavement distresses.
Grouping Pavement Condition Variables for Performance Modeling Using Self‐Organizing Maps
Attoh‐Okine, Nii O. (Autor:in)
Computer‐Aided Civil and Infrastructure Engineering ; 16 ; 112-125
01.03.2001
14 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Grouping Pavement Condition Variables for Performance Modeling Using Self-Organizing Maps
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