View in EDS

Research on ontology-based construction risk knowledge base development in deep foundation pit excavation.

Saved in:
Bibliographic Details
Title: Research on ontology-based construction risk knowledge base development in deep foundation pit excavation.
Authors: Chen, Yuan, Liang, Bingge, Hu, Hang
Source: Journal of Asian Architecture & Building Engineering; May2025, Vol. 24 Issue 3, p1640-1658, 19p
Abstract: Facing the massive amount of risk management data for deep foundation pit excavation (DFPE) construction, how to store and utilize these data to prevent the occurrence of risk events is an urgent problem that needs to be solved. The goal of this research is to build an ontology knowledge base aimed at standardizing and formalizing risk knowledge in the field of DFPE construction risk. The ontology knowledge base can promote construction risk identification and reduce the occurrence of risk events. A six-step method was applied to define the ontology classes and class hierarchy, determine objects and related data attributes. Ontology instances were created to further evaluate the integrity, correctness and consistency of the developed knowledge base. The case studies showed that through the formalization of knowledge and establishment of rule libraries, rule reasoning could intelligently identify potential risk events and provide risk prevention measures. This research contributed a new perspective to resolve problems of access and integration knowledge and information in DFPE risk management work. By structuring and standardizing knowledge and information, the ontology-based risk knowledge base can facilitate knowledge transfer, sharing and reuse among different project participants. The ontology knowledge base can provide decision support for managers to protect the on-site workers, building structures and the surrounding environments. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Asian Architecture & Building Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
Description
Abstract:Facing the massive amount of risk management data for deep foundation pit excavation (DFPE) construction, how to store and utilize these data to prevent the occurrence of risk events is an urgent problem that needs to be solved. The goal of this research is to build an ontology knowledge base aimed at standardizing and formalizing risk knowledge in the field of DFPE construction risk. The ontology knowledge base can promote construction risk identification and reduce the occurrence of risk events. A six-step method was applied to define the ontology classes and class hierarchy, determine objects and related data attributes. Ontology instances were created to further evaluate the integrity, correctness and consistency of the developed knowledge base. The case studies showed that through the formalization of knowledge and establishment of rule libraries, rule reasoning could intelligently identify potential risk events and provide risk prevention measures. This research contributed a new perspective to resolve problems of access and integration knowledge and information in DFPE risk management work. By structuring and standardizing knowledge and information, the ontology-based risk knowledge base can facilitate knowledge transfer, sharing and reuse among different project participants. The ontology knowledge base can provide decision support for managers to protect the on-site workers, building structures and the surrounding environments. [ABSTRACT FROM AUTHOR]
ISSN:13467581
DOI:10.1080/13467581.2024.2329361