Review of artificial intelligence-based bridge damage detection

Abstract

Bridges are often located in harsh environments and are thus extremely susceptible to damage. If the initial damage is not detected in time, it can develop further causing safety hazards. Therefore, accurate detection of bridge damage is an important topic. In recent years, artificial intelligence technology has been developed rapidly, especially machine learning algorithms, which have shown amazing results in various fields while it also received attention in bridge inspection. This paper summarizes the progress of bridge damage detection research related to artificial intelligence techniques between 2015 and 2021. For structural health monitoring, sensing data is the basis for various data processing methods. The strength and weakness of the sensing data itself directly affect the effectiveness of subsequent processing methods. As a result, this paper classifies bridge damage detection studies into six categories from the types of sensing data: visual image, point cloud, infrared thermal imaging, ground-penetrating radar, vibration response, and other types of data. These six types of damage detection methods were reviewed and summarized respectively. Finally, challenges and future trends were discussed.

Review: Research on product surface quality inspection technology based on 3D point cloud

“…At the same time, because the structure of the model is fixed and cannot be updated incrementally, when the data set is updated, the network model needs to be retrained, resulting in an increase in the time and cost of iterative updating of the model. 111 Real-time monitoring speed. The size of the point cloud is determined by the detection accuracy.…”

Unsupervised data normalization for continuous dynamic monitoring by an innovative hybrid feature weighting-selection algorithm and natural nearest neighbor searching

“…(5) The NaNN searching algorithm is an effective and efficient method and it can be considered rather than the parametric NN searching techniques, which require the number of NNs as priori. (6) If the dynamic features have low variability, this characteristic enables the NaNN searching algorithm to find further NaNNs. (7) The proposed method has outperformed the state-of-theart nonparametric anomaly detectors developed by the MSD, SVD, and PCA.…”

“…[1][2][3] Due to significant progress in sensing technologies and computational techniques, artificial intelligence brings a good possibility for civil engineers that enables them to implement data-driven methods based on various machine learning algorithms for continuous dynamic monitoring and long-term SHM. [4][5][6] Machine learning is the main area of artificial intelligence that intends to develop an automated learner (i.e., computational model) via training data and then conduct some tasks in terms of classification, regression, prediction, clustering, anomaly detection, etc. This methodology contains some underlying frameworks based on supervised, 7 semi-supervised, 8 and unsupervised learning 9 within some advanced algorithms under deep learning, 10 transfer learning, 11 active learning, 12 kernel learning, 13 multi-task learning, 14 dictionary learning, 15 meta-learning, 16 etc.…”

Disaster impact analysis in Africa: EDDA - UAV building damage dataset of rural and urban Mozambique

“…11,[14][15][16] The literature on the topic has become very rich in the recent years. Therefore, [17][18][19][20] introduced reviews on the topic of structural damage assessment from different perspectives. One common challenges to all the studies are robustness against different spatial resolutions, dataset specifics depending on the location of the assessment and limited availability of annotated data.…”