Deep learning models for bridge deck evaluation using impact echo

Dorafshan, Sattar; Azari, Hoda

doi:10.1016/j.conbuildmat.2020.120109

Cited by 63 publications

(64 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…IE device used for data collection contained a mechanical spherical impactor with a diameter of 3 to 12 mm to generate wave, and a displacement transducer located roughly 5 cm near the impact dot [11]. The produced wave propagates through the surface of structure and reflected at interface to find discontinuities area like defect.…”

Section: Ie Methodsmentioning

confidence: 99%

“…In this research, the dataset contained IE signals collected from eight slabs with artificial defect created at Advanced Sensing Technology FAST NDE laboratory) (FHWA) [11]. The length, width, and thickness of the slabs were 3.0, 1.0, and 0.2 m, respectively.…”

Section: Description Of Datamentioning

confidence: 99%

“…The height of the defect was 0.3 m while its width was 0.2 m. Figure 1 shows the defect type and the plane of the slabs. More information regarding to the type of defects, concrete age, and material can be found in [11].…”

Section: Description Of Datamentioning

confidence: 99%

“…Yeh et al (2018) employed a phase spectrum approach to distinguish cracks and the location of reinforcing bars [10]. The result of some studies revealed that the shape of IE signals, in terms of frequency responses, does not always follow the theoretical description to show defect area [11]. Moreover, the boundary condition of structure could influence the frequency and shape of signals as the waves reflected from lateral boundaries can significantly affect the time-domain signals and spectra [12].…”

Section: Introductionmentioning

confidence: 99%

“…In the majority of machine learning algorithms, user input is required to select the features considered for classification. Recently, Dorafshan et al (2020) developed a deep learning model for training convolutional neural networks to detect artificial delamination in laboratory-made reinforced concrete specimens where the features were extracted autonomously and through training [11,20]. The training datasets of the convolutional neural network (CNN) in raw IE data collected from the subsurface of structure with no pre-processing.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Bridge Inspection and Defect Recognition with Using Impact Echo Data, Probability, and Naive Bayes Classifiers

Jafari

Dorafshan

2021

Infrastructures

Self Cite

View full text Add to dashboard Cite

Interpretation of IE data have been carried out by analyzing IE signals in frequency domain to determine the maximum frequency. However, the current peak frequency method can be inaccurate. The purpose of this research is to introduce features in IE signals that can be used for effective classification and interpretation for bridge deck evaluation through statistical analysis and Naive Bayes classifiers. The dataset contained IE data collected from eight slabs created at Advanced Sensing Technology FAST NDE laboratory (FHWA). A set of statistical features in time domain, normalized peak values, and length of preprocessed signals were used to classify the IE data, statistically. Then, Naive Bayes classifiers was employed to recognize defect area. Finally, the result of statistical classification was compared with frequency approach. The result shows that 19 and 21% of the IE signals collected from the defect area have multiple peaks, respectively. However, 85% of the IE signals collected from the sound set had only one peak. A probability classifier was used to find the relationship between the result of the frequency method and statistical analysis. The result shows that 10% of the IE signals were usable for estimating the thickness in the sound group.

show abstract

Section: Ie Methodsmentioning

confidence: 99%

Section: Description Of Datamentioning

confidence: 99%

Section: Description Of Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Bridge Inspection and Defect Recognition with Using Impact Echo Data, Probability, and Naive Bayes Classifiers

Jafari

Dorafshan

2021

Infrastructures

Self Cite

View full text Add to dashboard Cite

show abstract

Machine Learning Applications in Nondestructive Testing of Concrete Structures

Algernon,

Münch,

Muller

et al. 2023

ce papers

View full text Add to dashboard Cite

Machine Learning bears great potential for data‐driven solutions in the field of nondestructive testing (NDT) of concrete structures. The analysis of the data collected with NDT methods, such as ultrasonics, impact‐echo and ground penetrating radar, can be complex and requires experience. The expected benefit of Machine Learning applications in this context goes beyond the increase of efficiency obtained by automating the analysis process. While traditional analysis approaches are usually solely based on key features according to the basic principles, Machine Learning algorithms can consider the full data content and reveal hidden correlations. For an organized approach to Machine Learning on NDT data, an analysis tool has been developed. The tool provides a graphical user interface to manage and label training/test data and interactively define the Deep Neural Network architecture. In particular, Convolutional Neural Networks, as proven successful in various image recognition tasks, are implemented. The Machine Learning concepts are demonstrated in show cases, comprising ultrasonic as well as impact‐echo applications. In particular, the relevance of a targeted preprocessing is addressed, comparing the effectiveness of time‐, frequency‐ and joint‐time‐frequency‐representations.

show abstract

Deep learning models for analysis of non‐destructive evaluation data to evaluate reinforced concrete bridge decks: A survey

Lavadiya

Dorafshan

2022

Engineering Reports

View full text Add to dashboard Cite

Application of deep learning (DL) for automatic condition assessment of bridge decks has been on the raise in the last few years. From the published literature, it is evident that lot of research efforts has been done in identifying the surface defects such as cracks, potholes, spalling and so forth using supervised learning methods such as deep learning. However, the health of a reinforced concrete bridge deck is jeopardized substantially due to presence of subsurface defects. Subsurface defects in bridge decks are genearlly detected using non‐destructive evaluation (NDE) methods. Interpertation of NDE data for autonomous deck evaluation requires development of DL models; however, The task of defect detection DL has not received the proper attention for subsurface defect detection in the past. The goal of this paper is to provide a review of existing DL models for analysis of NDE data of bridge decks. The authors reviewed prominent NDE techniques for subsurface defect detection of bridge decks and explored the various DL models proposed to identify these defects. First a brief overview of the working principle of NDE techniques and DL architectures is provided, and then the information about proposed DL models and their efficacy is highlighted. Based on the existing knowledge gaps, various challenges and future prospects associated with application of DL in bridge subsurface inspection are discussed.

show abstract

Deep learning models for bridge deck evaluation using impact echo

Cited by 63 publications

References 42 publications

Bridge Inspection and Defect Recognition with Using Impact Echo Data, Probability, and Naive Bayes Classifiers

Bridge Inspection and Defect Recognition with Using Impact Echo Data, Probability, and Naive Bayes Classifiers

Machine Learning Applications in Nondestructive Testing of Concrete Structures

Deep learning models for analysis of non‐destructive evaluation data to evaluate reinforced concrete bridge decks: A survey

Contact Info

Product

Resources

About