In the face of complex industrial projects, conventional project planning methodologies often fall short, necessitating innovative frameworks to ensure precise planning and efficient execution. This study introduces an ontology-based approach inspired by Advanced Work Packaging (AWP) principles, catering specifically to industrial construction projects, with a primary focus on power plant projects. By deploying Protégé software, the developed ontology is populated using an extract of historical data from a power plant project, thereby enabling the generation of class taxonomy instances. The framework encompasses key steps: ontology development, ontology implementation, and verification. The ontology facilitates rapid data retrieval and provides a robust platform for extracting, organizing, and utilizing historical project data, which in turn significantly refines the planning and execution processes. Our findings show the ontology’s capability in identifying construction work areas (CWAs) and construction work packages (CWPs) based on historical data for utilization in early project stages. The experts’ acknowledgment of the enhanced efficiency in knowledge retrieval highlights the framework’s potential to improve the usage of historical data in early project stages.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

An Advanced Work Packaging Inspired Ontology for Industrial Construction Projects

  • Mohamed ElMenshawy,
  • Lingzi Wu,
  • Ulrich Hermann,
  • Travis Zubick,
  • Simaan AbouRizk

摘要

In the face of complex industrial projects, conventional project planning methodologies often fall short, necessitating innovative frameworks to ensure precise planning and efficient execution. This study introduces an ontology-based approach inspired by Advanced Work Packaging (AWP) principles, catering specifically to industrial construction projects, with a primary focus on power plant projects. By deploying Protégé software, the developed ontology is populated using an extract of historical data from a power plant project, thereby enabling the generation of class taxonomy instances. The framework encompasses key steps: ontology development, ontology implementation, and verification. The ontology facilitates rapid data retrieval and provides a robust platform for extracting, organizing, and utilizing historical project data, which in turn significantly refines the planning and execution processes. Our findings show the ontology’s capability in identifying construction work areas (CWAs) and construction work packages (CWPs) based on historical data for utilization in early project stages. The experts’ acknowledgment of the enhanced efficiency in knowledge retrieval highlights the framework’s potential to improve the usage of historical data in early project stages.