Pengyong Miao 교수

Bridge censored databases can be used to analyze and assess structural deterioration conditions, but conducting the analysis is difficult. This difficulty occurs because many factors affect deterioration, and the time span of the data for these factors depends on the years in service of the respective bridge. In addition, the values of some factors are not regularly observed. The present study uses the long short-term memory (LSTM) to consider twelve potentially influencing factors to recognize

The freeze-thaw (F-T) properties of ordinary concrete have been extensively studied and related models were well-established. However, these models cannot be used to accurately assess the F-T damage of dune sand and basalt fiber reinforced concrete (DSFC), which is essential for performing targeted repairers. This study established an interpretable machine learning (ML) model to predict the F-T damage indicator D of DSFC through systematic comparisons of eight ML models (four classical models an

Bridge deterioration is affected by various factors. However, neither the relationships between these factors and deterioration are explicitly determined, nor the relative effect of each factor on deterioration is well understood. This study proposed a methodology to resolve these issues by integrating an artificial neural network (ANN) and sensitivity analysis method. The ANN was used to predict deterioration, and the sensitivity analysis method was applied to evaluate the influence of each fac

Inspection data are usually utilized to assess bridge situations for directing further maintenance and preservation. However, due to the complexity of inspection data, mining and fusing valuable information to assess bridge situations remains challenging. To address these issues, a novel inspection data analysis framework was proposed in this study. The framework integrated a gated recursive unit (GRU) model, a semantic segmentation (Seg) model, and a Yolo V4 object detector to analyze both time

Bridge deterioration reflects changes in bridge conditions and is caused by many factors. Predicting bridge deterioration is critical for implementing predictive interventions, but developing such credible models that consider multiple influencing factors remains difficult. This study uses Markov Chain (MC) and Recurrent Neural Network (RNN) to establish deterioration prediction models. Then, comparative studies of these two models are conducted on a censored database regarding the mean deterior

Maintenance continues for structure’s life cycle, which usually costs a lot. Inspection and/or monitoring are widely implemented to investigate the conditions of the structures. Existing databases are sometimes referred to for understanding the performance of structure with inspection/monitoring data. Since structural performance is related to various irregularly time-shifted factors, it is complicated to analyze a database efficiently. To improve the situation, the key data selection (KDS) meth

The deterioration of concrete structures is influenced by various factors. However, neither the interactions among those factors nor their impacts are explicitly determined. Nowadays, deterioration assessment for concrete structures mainly relies on periodic inspections and on data in maintenance databases that are collected during maintenance. However, superficial analyses of databases are insufficient when maintenance strategies are formulated and/or proper intervention work is implemented. In