This study deals with vibration-based damage detection in a truss bridge model and suggests a novel methodology based on fuzzy clustering and measured frequency response function (FRF) data reduced by principal component projection. A six-bay truss bridge model is designed and fabricated in laboratory, various connection damages are simulated by loosening the end connecter bolts, and the environmental effects are taken into account by changing in excitation force levels of a shaker. The FRFs of the healthy and the damaged structure are used as initial data. The FRF data normalization is performed for eliminating the effects caused by the environmental and operational variability. Two data projection algorithms, namely principal component analysis (PCA) and kernel principal component analysis (KPCA) are adopted for data compression and the median values of principal components are defined for damage feature extraction. The fuzzy c-means (FCM) clustering algorithm is used to categorize these features for structural damage detection. The illustrated results show that the proposed method can effectively identify the bridge damages simulated by loosening the bolted joints of the truss bridge structure. It is sensitive to the structural damage but it is non-sensitive to the effect of the environmental and operational variations. This makes it quite generic and permits its potential development for real and complex truss bridges in site.

An integrated method is proposed for structural nonlinear damage detection based on time series analysis and the higher statistical moments of structural responses in this study. It combines the time series analysis, the higher statistical moments of AR model residual errors and the fuzzy c-means (FCM) clustering techniques. A few comprehensive damage indexes are developed in the arithmetic and geometric manner of the higher statistical moments, and are classified by using the FCM clustering method to achieve nonlinear damage detection. A series of the measured response data, downloaded from the web site of the Los Alamos National Laboratory (LANL) USA, from a three-story building structure considering the environmental variety as well as different nonlinear damage cases, are analyzed and used to assess the effectiveness and robustness of the new nonlinear damage detection method. Some valuable conclusions are made and related issues discussed as well.

Background Artificial intelligence (AI) has been introduced to interpret the panoramic radiographs (PRs). The aim of this study was to develop an AI framework to diagnose multiple dental diseases on PRs, and to initially evaluate its performance. Methods The AI framework was developed based on 2 deep convolutional neural networks (CNNs), BDU-Net and nnU-Net. 1996 PRs were used for training. Diagnostic evaluation was performed on a separate evaluation dataset including 282 PRs. Sensitivity, specificity, Youden’s index, the area under the curve (AUC), and diagnostic time were calculated. Dentists with 3 different levels of seniority (H: high, M: medium, L: low) diagnosed the same evaluation dataset independently. Mann-Whitney U test and Delong test were conducted for statistical analysis (ɑ=0.05). Results Sensitivity, specificity, and Youden’s index of the framework for diagnosing 5 diseases were 0.964, 0.996, 0.960 (impacted teeth), 0.953, 0.998, 0.951 (full crowns), 0.871, 0.999, 0.870 (residual roots), 0.885, 0.994, 0.879 (missing teeth), and 0.554, 0.990, 0.544 (caries), respectively. AUC of the framework for the diseases were 0.980 (95%CI: 0.976–0.983, impacted teeth), 0.975 (95%CI: 0.972–0.978, full crowns), and 0.935 (95%CI: 0.929–0.940, residual roots), 0.939 (95%CI: 0.934–0.944, missing teeth), and 0.772 (95%CI: 0.764–0.781, caries), respectively. AUC of the AI framework was comparable to that of all dentists in diagnosing residual roots (p > 0.05), and its AUC values were similar to (p > 0.05) or better than (p < 0.05) that of M-level dentists for diagnosing 5 diseases. But AUC of the framework was statistically lower than some of H-level dentists for diagnosing impacted teeth, missing teeth, and caries (p < 0.05). The mean diagnostic time of the framework was significantly shorter than that of all dentists (p < 0.001). Conclusions The AI framework based on BDU-Net and nnU-Net demonstrated high specificity on diagnosing impacted teeth, full crowns, missing teeth, residual roots, and caries with high efficiency. The clinical feasibility of AI framework was preliminary verified since its performance was similar to or even better than the dentists with 3–10 years of experience. However, the AI framework for caries diagnosis should be improved.

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