Machine Learning-Driven Ontological Knowledge Base for Bridge Corrosion Evaluation

Abstract

In bridge maintenance, assessing structural performance requires adherence to rules outlined in safety and regulatory standards which can be effectively and formally represented in both human and machine-readable formats using ontologies. However, ontology-based semantic inference alone falls short when faced with the complicated mathematical operations required for structural analysis. The increasing digitization of bridge engineering has opened doors to data-driven prediction methods. Machine learning (ML)-based models, in particular, have the capacity to learn from historical data and forecast future structural performance with remarkable accuracy. This paper introduces an innovative approach that integrates ML models with an ontological knowledge base for evaluating bridge corrosion. Web Ontology Language and Semantic Web Rule Language are combined to develop the knowledge base. Random forest algorithm is used to train the ML model with a good agreement (coefficient of determination of 0.989 and root mean square error of 1.200). A Python-based module is designed to seamlessly integrate ML predictions with ontologybased semantic inference. The proposed approach not only infers the corrosion ratings based on the rules defined in the Network Rail standard, but also infers the structural safety performance based on predicted structural response under the action of corrosion. To demonstrate the effectiveness of the developed method in enabling accurate and rational evaluations, a real bridge in the UK is showcased as a practical application.

Physics-Informed Knowledge-Driven Decision-Making Framework for Holistic Bridge Maintenance

“…Chai and Wang (2022) developed a framework integrating computer vision and ontology to automate and standardize the assessment of concrete surface quality. Jiang et al (2023b) presented a Bridge Corrosion Evaluation Ontology (BCEO) designed to assess the extent and severity of corrosion on railway bridges. Hamdan et al (2021) proposed a semantic modeling approach for the automated detection and interpretation of bridge damage, consisting of two main ontologies: Damage Topology Ontology (DOT) (Hamdan et al 2019) and Bridge Topology Ontology (BROT) (Hamdan et al 2020).…”

Offshore Wind Power Foundation Corrosion Rate Prediction Model Based on Improved SHO Algorithm

“…Usually, the requirement is satisfied when the cumulative contribution c is greater than 85% [14]. Using Equation ( 4) to determine the principal components, for any sample x, the reconstructed nonlinear sample F i can be obtained by performing a high-dimensional mapping of its eigenvalue α and eigenvector u i as follows:…”

“…L. Chen, J. Yang, and X. Lu proposed a genetic algorithm to predict the corrosion rate of metal in a place in South China [13]. Y. Jiang, H. Li, G. Yang, C. Zhang, and K. Zhao established a corrosion rate prediction model of the grounding networks by taking six influencing factors, such as resistivity, water content, salt content, and so on, as input parameters [14]. The corrosion sample capacity in the above literature is less than 100, which is a typical small sample, and the use of a BP neural network is not appropriate.…”

The Intention of Bridge Asset Management Implementation in Indonesia

“…This system helps managers make better decisions by considering different factors. Additionally, advanced technologies like Building Information Modeling (BIM) and Unmanned Aerial Systems (UAS) can improve bridge inspections and management, especially with BMS [8][9][10][11]. These technologies play a crucial role in making BMS implementation more efficient, particularly in tasks like inspections and data management.…”