Scholarlink

<p>Structural damage detection (SDD) is essential for the safety and operational reliability of civil structures. This paper proposes an optimized machine learning (ML) technique for damage detection of the ASCE benchmark building, which is based on simulated structural data provided by an ANSYS numerical model. The building model is tested under several damage scenarios, and time-domain acceleration data are gathered under impact excitation. Relevant statistical features are retrieved from the simulation results and used as inputs for the model. The K-Nearest Neighbours (KNN) technique is used as the base classifier. Hyperparameter optimization is performed using particle swarm optimisation (PSO) and grid searching (GS) techniques. The results show that optimisation considerably enhances the technique’s damage classification accuracy, with the PSO-KNN technique achieving high accuracy and computational efficiency. Moreover, the results are compared by applying principal component analysis (PCA) by selecting important features. The results are also compared with traditional KNN, which yielded lower accuracy, thereby highlighting the necessity of employing optimization techniques. Furthermore, the outcomes are then compared with those obtained using the ANN technique. Additionally, the robustness of the technique is verified under a noisy dataset. The study indicates that combining ANSYS-based numerical modelling with optimized ML techniques creates a strong foundation for reliable structural state evaluations in SDD applications.</p>

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

Optimized machine learning technique for health monitoring of an ASCE benchmark building using simulated data

  • 期刊论文

  • 发表日期：

    2025年11月28日

查看全文
  • P. Manikanta,
  • Takkellapati Sujatha,
  • CH. Ajay,
  • Maloth Naresh,
  • Rachamallu Vaishnava Kumar

摘要

Structural damage detection (SDD) is essential for the safety and operational reliability of civil structures. This paper proposes an optimized machine learning (ML) technique for damage detection of the ASCE benchmark building, which is based on simulated structural data provided by an ANSYS numerical model. The building model is tested under several damage scenarios, and time-domain acceleration data are gathered under impact excitation. Relevant statistical features are retrieved from the simulation results and used as inputs for the model. The K-Nearest Neighbours (KNN) technique is used as the base classifier. Hyperparameter optimization is performed using particle swarm optimisation (PSO) and grid searching (GS) techniques. The results show that optimisation considerably enhances the technique’s damage classification accuracy, with the PSO-KNN technique achieving high accuracy and computational efficiency. Moreover, the results are compared by applying principal component analysis (PCA) by selecting important features. The results are also compared with traditional KNN, which yielded lower accuracy, thereby highlighting the necessity of employing optimization techniques. Furthermore, the outcomes are then compared with those obtained using the ANN technique. Additionally, the robustness of the technique is verified under a noisy dataset. The study indicates that combining ANSYS-based numerical modelling with optimized ML techniques creates a strong foundation for reliable structural state evaluations in SDD applications.