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• This paper suggests a grating Moiré fringe-based error correction approach that uses an artificial neural network (ANN) and the quartile method (QM). • The approach uses QM for data cleaning operations to eliminate outliers and creates the ANN model with a structure of fully connected layer-normalization layer-fully connected layer for regression training in order to achieve lightweight and stability. • The proposed QM-ANN error correction model's mean absolute error (MAE) can reach 3.3 nm once the QM data has been cleaned and the ANN model has been trained. Many interference factors of the complex and changeable working environment of the grating sensor are the main factors affecting the accuracy; a grating Moiré fringe error correction model based on the quartile method (QM) and artificial neural network (ANN) is proposed in this paper. Firstly, the grating sensor's signals in the working process are collected, and three kinds of interference signals (temperature, humidity and vibration) from the grating sensor working environment are collected simultaneously. Secondly, the QM is used to detect outliers from the collected data and delete data outliers directly. Subsequently, the processed data are input into the ANN model for training. The model is lightweight and stable, because it adopts the cascaded structure of full connection layer-normalization layer-full connection layer, which does not need deploy the complex convolution layer module etc. Finally, the corrected data are output after being processed by the QM-ANN error correction model. The experimental results show that the mean absolute error (MAE) of the proposed QM-ANN error correction model is better than QM-convolutional neural network (CNN), QM-long short-term memory (LSTM) and ANN, CNN, LSTM models without QM. It shows that the proposed model has better robustness and practicability. [ABSTRACT FROM AUTHOR]

• A new damage detection method is proposed based on the combination of EWT and ANN. • EWT-ANN is used for damage detection and localization of a steel truss bridge. • Energy, RMS, shape factor, kurtosis, and entropy features of signals are assessed. • EWT-ANN is effectively able in detecting, locating, and classifying the damage. This paper aims to present a computational and experimental investigation on the performance of a new damage detection approach based on empirical wavelet transform (EWT) and an artificial neural network (ANN) named EWT-ANN to detect the damage existence, intensity, and location in a laboratory-scale model of a steel truss bridge in healthy and damaged states when exposed to white noise and impact excitations. In addition, five signal features of the decomposed mode components by EWT including energy, root mean square (RMS), shape factor, kurtosis, and entropy are extracted and defined as the targets of ANN to quantitatively and qualitatively assess the proposed approach. It is found that the proposed approach based on the time-domain signal features is efficiently able in identifying the damage location, and severity when the truss is subjected to impact loading excitation. [ABSTRACT FROM AUTHOR]