Hazards identification and risk assessment for UAV–assisted bridge inspections

Aliyari, Mostafa; Ashrafi, Behrooz; Ayele, Yonas Zewdu

doi:10.1080/15732479.2020.1858878

Cited by 24 publications

(17 citation statements)

References 32 publications

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“…Weighted Score = 0.805 × Cracked class score + 0.195 × Noncracked class score (5) where cracked class score is the score that the crack class gets in term of accuracy, precision, recall, and F1 score and non-cracked class score is the score that the non-crack class gets in term of accuracy, precision, recall, and F1 score However, macro average is not weighted and therefore can be calculated as follows:…”

Section: Step 13-the Cnn Models' Performance Metricsmentioning

confidence: 99%

“…UAVs are among the best alternatives for conventional inspections, predominantly due to the higher safety of workers, lower cost, and tangible scientific improvements. Several studies have shown the successful implementation of UAVs in bridge inspection [2,[5][6][7][8]. Moreover, recent advances in the field of artificial intelligence (AI), especially in machine and deep learning, have offered suitable solutions in many different practical fields such as bridge inspection.…”

Section: Introductionmentioning

confidence: 99%

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UAV-Based Bridge Inspection via Transfer Learning

2021

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As bridge inspection becomes more advanced and more ubiquitous, artificial intelligence (AI) techniques, such as machine and deep learning, could offer suitable solutions to the nation’s problems of overdue bridge inspections. AI coupling with various data that can be captured by unmanned aerial vehicles (UAVs) enables fully automated bridge inspections. The key to the success of automated bridge inspection is a model capable of detecting failures from UAV data like images and films. In this context, this paper investigates the performances of state-of-the-art convolutional neural networks (CNNs) through transfer learning for crack detection in UAV-based bridge inspection. The performance of different CNN models is evaluated via UAV-based inspection of Skodsberg Bridge, located in eastern Norway. The low-level features are extracted in the last layers of the CNN models and these layers are trained using 19,023 crack and non-crack images. There is always a trade-off between the number of trainable parameters that CNN models need to learn for each specific task and the number of non-trainable parameters that come from transfer learning. Therefore, selecting the optimized amount of transfer learning is a challenging task and, as there is not enough research in this area, it will be studied in this paper. Moreover, UAV-based bridge inception images require specific attention to establish a suitable dataset as the input of CNN models that are trained on homogenous images. However, in the real implementation of CNN models in UAV-based bridge inspection images, there are always heterogeneities and noises, such as natural and artificial effects like different luminosities, spatial positions, and colors of the elements in an image. In this study, the effects of such heterogeneities on the performance of CNN models via transfer learning are examined. The results demonstrate that with a simplified image cropping technique and with minimum effort to preprocess images, CNN models can identify crack elements from non-crack elements with 81% accuracy. Moreover, the results show that heterogeneities inherent in UAV-based bridge inspection data significantly affect the performance of CNN models with an average 32.6% decrease of accuracy of the CNN models. It is also found that deeper CNN models do not provide higher accuracy compared to the shallower CNN models when the number of images for adoption to a specific task, in this case crack detection, is not large enough; in this study, 19,023 images and shallower models outperform the deeper models.

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Section: Step 13-the Cnn Models' Performance Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

UAV-Based Bridge Inspection via Transfer Learning

2021

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“…To fill the current voids in BMSs, several non-destructive technologies have been implemented across literature to collect and process data [4]. these include infrared thermography, terrestrial laser scanners and unmanned aerial systems (UAS) [5][6][7][8][9][10]. UAS provide a unique solution to a number of problems in data acquisition by allowing the automation of aerial and close-up digital images, thereby isolating inspectors from potential hazards.…”

Section: Introductionmentioning

confidence: 99%

“…Mismanagement of drone data has the potential to further confuse inspection engineers, who are now faced with an abundance of digital information [11]. As such, there exists a need to develop a data information model to store, process and manage the bridge inspection data captured through UAS [2,8,12]. To fill this gap, this paper proposed a novel methodology for a digital information model covering data acquisition through to a 3D GIS visualisation environment, also capable of integrating within a bridge management system (BMS).…”

Section: Introductionmentioning

confidence: 99%

A Digital Information Model Framework for UAS-Enabled Bridge Inspection

et al. 2021

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Unmanned aerial systems (UAS) provide two main functions with regards to bridge inspections: (1) high-quality digital imaging to detect element defects; (2) spatial point cloud data for the reconstruction of 3D asset models. With UAS being a relatively new inspection method, there is little in the way of existing framework for storing, processing and managing the resulting inspection data. This study has proposed a novel methodology for a digital information model covering data acquisition through to a 3D GIS visualisation environment, also capable of integrating within a bridge management system (BMS). Previous efforts focusing on visualisation functionality have focused on BIM and GIS as separate entities, which has a number of problems associated with it. This methodology has a core focus on the integration of BIM and GIS, providing an effective and efficient information model, which provides vital visual context to inspectors and users of the BMS. Three-dimensional GIS visualisation allows the user to navigate through a fully interactive environment, where element level inspection information can be obtained through point-and-click operations on the 3D structural model. Two visualisation environments were created: a web-based GIS application and a desktop solution. Both environments develop a fully interactive, user-friendly model which have fulfilled the aims of coordinating and streamlining the BMS process.

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“…With the birth and rise of a new round of scientific and technological revolution and industrial change, modern technologies such as "5G" technology, "Internet +" technology, new sensors, robots and artificial intelligence have brought new opportunities to the innovation of bridge engineering [ 5 ], and bridge inspection is developing towards automation and intelligence [ 6 ]. Karim et al [ 7 ] and Aliyari et al [ 8 ] proposed to replace labor-intensive bridge detection with UAV mobile camera, and also proposed a structural damage pattern recognition algorithm based on cellular automata. Yang et al [ 9 ] Based on virtual simulation of vehicle-bridge interactions, a bridge inspection method based on vehicle behavior is proposed to reduce labor consumption and improve the efficiency of bridge inspection.…”

Section: Introductionmentioning

confidence: 99%

Study on optimization of inspection mechanism of concrete beam bridge

Liu²,

Li³

et al. 2021

PLoS ONE

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China is shifting from the stage of large-scale bridge construction to the stage of equal emphasis on the construction and maintenance of bridges. The problems of low efficiency and high cost in bridge inspection are becoming more and more prominent, which threaten people’s life safety. In this paper, the deterioration state prediction model of concrete beam bridge under Boosting DT C5.0 was established as the basis, and optimization suggestions were put forward in terms of bridge inspection standards and processes, which aims to perfect the bridge inspection mechanism, realize the fine management of the bridge and prolong the service life of the bridge. Research shows that: first, the bridge inspection standard with a single index should be improved into the bridge inspection standard with multiple indexes, so as to scientifically determine the bridges that need to be inspected and optimize the allocation of maintenance resources. Second, the bridge deterioration state prediction model is used to add a screening mechanism for the bridge in the inspection plan, which can effectively reduce the workload of bridge inspection and enhance the inspection efficiency. Third, the deterioration phenomenon of coexistence between adjacent traffic assets should be fully considered and the linkage inspection mechanism of adjacent traffic assets should be established to improve the effect of bridge inspection.

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Hazards identification and risk assessment for UAV–assisted bridge inspections

Cited by 24 publications

References 32 publications

UAV-Based Bridge Inspection via Transfer Learning

UAV-Based Bridge Inspection via Transfer Learning

A Digital Information Model Framework for UAS-Enabled Bridge Inspection

Study on optimization of inspection mechanism of concrete beam bridge

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