Effective Crack Damage Detection Using Multilayer Sparse Feature Representation and Incremental Extreme Learning Machine

Abstract

Detecting cracks within reinforced concrete is still a challenging problem, owing to the complex disturbances from the background noise. In this work, we advocate a new concrete crack damage detection model, based upon multilayer sparse feature representation and an incremental extreme learning machine (ELM), which has both favorable feature learning and classification capabilities. Specifically, by cropping and using a sliding window operation and image rotation, a large number of crack and non-crack patches are obtained from the collected concrete images. With the existing image patches, the defect region features can be quickly calculated by the multilayer sparse ELM autoencoder networks. Then, the online incremental ELM classified network is used to recognize the crack defect features. Unlike the commonly-used deep learning-based methods, the presented ELM-based crack detection model can be trained efficiently without tediously fine-tuning the entire-network parameters. Moreover, according to the ELM theory, the proposed crack detector works universally for defect feature extraction and detection. In the experiments, when compared with other recently developed crack detectors, the proposed concrete crack detection model can offer outstanding training efficiency and favorable crack detecting accuracy.

Benefits of ensemble models in road pavement cracking classification

“…A different approach of crack detection is presented by B. Wang et al. (2019), where the extreme learning machine (ELM) model is applied. The advantage of this approach is that the parameters of the hidden layers are initialized and randomly selected.…”

Structural deformation prediction model based on extreme learning machine algorithm and particle swarm optimization

“…Therefore, the ELM algorithm has received extensive attention in various fields such as biomedicine, machine vision, image and video processing, and engineering structural health monitoring. Wang et al 32 applied an online incremental ANN-classified network for defect feature extraction and detection and showed that the proposed concrete crack detection model provided outstanding training efficiency and favorable crack detection accuracy. Dai et al 33 combined an online sequential ELM algorithm with a genetic algorithm to build an efficient online prediction model for crack behavior with an early warning function for dam safety monitoring.…”

A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages

“…Nevertheless, within different frequency ranges, the sensitivity level of acoustic signals to blockages is affected by factors such as the inner diameter, length, and embedding conditions of pipes, which is also correlated with the size and severity of blockages. Hence, a detailed analysis is necessary regarding the amount of feature information contained in different frequencies of sound signals under the blockage condition [28]. The information gain in filter feature selection is introduced to select effective Intrinsic Mode Functions (IMF) components, extract features with large contributions and remove redundant feature information, and construct the best combination of differential features to represent blockage [29].…”