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Vibration-based structural damage detection is one of the most promising venues for building smart and automated structural health monitoring applications; however, its applicability is impeded by a large amount of collected vibration data, and the performance could be undermined by degraded data. Therefore, this study develops a robust framework, dubbed AutoBoost-SDD, that can effectively handle contaminated vibration data and provide reliable monitoring results within reasonable computational resources. The proposed method consists of three key components. Firstly, multi-domain feature extraction techniques are utilized to convert high-dimensional raw data into informative feature vectors. Secondly, the auto-encoder deep learning architecture is leveraged to refine feature vectors of contaminated data. Finally, a tree-based boosting machine learning algorithm, namely LightGBM, is employed to assess the structures' operational states using learned output from the second step. The viability and performance of the proposed framework are illustrated via three case studies involving numerical data of a 5-degree of freedom system, a 2D frame structure, and experimental data of a largescale 18-story frame structure from the literature. The results show that the AutoBoost-SDD framework is able to provide reasonable detection results despite the presence of various contaminations, including noisy, missing, and anomalous data. [ABSTRACT FROM AUTHOR]

Output-only structural health monitoring is a highly active research direction because it is a promising methodology for building digital twin applications providing near-real-time monitoring results of the structure. However, one of the technical bottlenecks is how to work effectively with multiple high-dimensional vibration signals. To address this question, this study develops a two-stage data-driven framework based on various advanced techniques, such as time-series feature extractions, self-learning, graph neural network, and machine learning algorithms. At first, multiple features in statistical, time, and spectral domains, are extracted from raw vibration data; then, they subsequently enter a graph convolution network to account for the spatial correlation of sensor locations. After that, the high-performance adaptive boosting machine learning algorithm is leveraged to assess structures' health states. This method allows for learning a lower-dimensional yet informative representation of vibration data; thus, the subsequent monitoring tasks could be performed with reduced time complexity and economical computational resources. The performance of the proposed method is qualitatively and quantitatively demonstrated through two examples involving both numerical and experimental structural data. Furthermore, comparison and robustness studies are carried out, showing that the proposed approach outperforms various machine learning/deep learning-based methods in terms of accuracy and noise/missing-robustness. [ABSTRACT FROM AUTHOR]

The effect of fly ash (FA) dosage on concrete's compressive strength and stress-strain relationship is investigated in two steps in this article. First, an experimental program was conducted on concrete mixtures designed with 0% (control batch of 30 MPa mean cylinder compressive strength), 10, 20, 30, and 40% of ordinary Portland cement (OPC) mass replaced by FA, which is taken from a new source in an Asia country. The test results showed that compared to other investigated dosages, concrete using 20% FA/OPC mass-replacement gained the most improvement in the 28-day compressive strength and tensile split strength, as well as the compressive strength development. Second, a probabilistic investigation was conducted using Dropout Neural Network, Bayesian Neural Network, and Gaussian Process models. These artificial intelligence-based models were compared to other models reviewed from the literature, showing relatively good results in terms of the statistical metric R2, which are 0.92, 0.9, and 0.88, respectively. The three models were tested and validated with a dataset of 1032 experimental results on FAC collected from the literature. When testing with the experimental results obtained in the first step, a good correlation between the predicted values and the experimental results was observed within the confidence interval of (5%, 95%), showing the reliability of the proposed models. Thus, the stressstrain relationship of fly ash concrete can also be investigated in a probabilistic manner. It is proved in this study that among the proposed models, Dropout Neural Network has the best balance between performance and time complexity. [ABSTRACT FROM AUTHOR]