Pavement Distress Recognition via Wavelet-Based Clustering of Smartphone Accelerometer Data

Kamranfar, Parastoo; Lattanzi, David; Shehu, Amarda; Stoffels, Shelley M

doi:10.1061/(asce)cp.1943-5487.0001022

Cited by 11 publications

(5 citation statements)

References 40 publications

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“…[ 28 ], suggested using density-based spatial clustering of applications with noise (DBSCAN), an unsupervised clustering technique, in conjunction with acoustic emission monitoring to monitor crack damage in mild steel. In [ 29 ], an unsupervised learning approach for fracture detection using wavelet clustering of accelerometer data collected from smartphones was proposed. Although low-cost smartphone sensor data may not capture the finer details of pavement fractures, they offer a viable solution for detecting road pavement breaks and distinguishing between different types of pavement faults.…”

Section: Related Workmentioning

confidence: 99%

Crack Detection and Analysis of Concrete Structures Based on Neural Network and Clustering

Choi,

Park,

et al. 2024

Sensors

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Concrete is extensively used in the construction of infrastructure such as houses and bridges. However, the appearance of cracks in concrete structures over time can diminish their sealing and load-bearing capability, potentially leading to structural failures and disasters. The timely detection of cracks allows for repairs without the need to replace the entire structure, resulting in cost savings. Currently, manual inspection remains the predominant method for identifying concrete cracks. However, in today’s increasingly complex construction environments, subjective errors may arise due to human vision and perception. The purpose of this work is to investigate and design an autonomous convolutional neural network-based concrete detection system that can identify cracks automatically and use that information to calculate the crack proportion. The experiment’s findings show that the trained model can classify concrete cracks with an accuracy of 99.9%. Moreover, the clustering technique applied to crack images enables the clear identification of the percentage of cracks, which facilitates the development of concrete damage level detection over time.

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Section: Related Workmentioning

confidence: 99%

Crack Detection and Analysis of Concrete Structures Based on Neural Network and Clustering

Choi,

Park,

et al. 2024

Sensors

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“…This versatility makes WEC a valuable tool across diverse disciplines. Researchers applied WEC for tasks like classifying fish feeding sounds to identify species [26], detecting pavement damage through smartphone sensors [27], and even analyzing human emotions using neural networks [28]. WEC is also applicable to study communityacquired pneumonia [29,30,31] and identify pavement quality [32].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Set Theory Enhanced Wavelet Entropy Clustering for Pattern Recognition

Saxena,

Mahapatra,

Rizvi

2024

Preprint

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“…Pavement quality evaluation is typically done using a combination of visual inspections, physical measurements, and non-destructive testing techniques (Okine and Adarkwa, 2013;Pierce et al, 2013;Buttlar and Islam, 2014;Seraj et al, 2016;Kamranfar et al, 2022). A standard practice for evaluating pavements at scale is by capturing the International Roughness Index (IRI) with a pavement roughness profiler or an inertial profiler.…”

Section: Introductionmentioning

confidence: 99%

Applications of using connected vehicle data for pavement quality analysis

Mahlberg,

Li,

Zachrisson

et al. 2024

Front. Future Transp.

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Current quantitative methods to evaluate pavement conditions in the United States are most commonly focused on construction acceptance using the International Roughness Index (IRI). However, from an asset management perspective, qualitative visual inspection techniques are the most prevalent. Modern vehicles with factory-equipped sensors drive these roadways daily and can passively assess the condition of infrastructure at an accuracy level somewhere between qualitative assessment and rigorous construction acceptance techniques. This paper compares crowdsourced ride quality data with an industry standard inertial profiler on a 7-mile bi-directional construction zone. A linear correlation was performed on 14 miles of I-65 that resulted in an R2 of 0.7 and a p-value of <0.001, but with a modest fixed offset bias. The scalability of these techniques is illustrated with graphics characterizing IRI values obtained from 730,000 crowdsourced data segments over 5,800 miles of I-80 in April of 2022 and October 2022. This paper looks at the use of standard original equipment manufacturer (OEM) on-board sensor data from production vehicles to assess approximately 100 miles of roadway pavements before, during, and after construction. The completed construction projects observed IRI improvements of 10 in/mi to 100 in/mi. These results suggest that it is now possible to monitor pavement ride quality at a system level, even with a small proportion of connected vehicles (CV) providing roughness data.

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Pavement Distress Recognition via Wavelet-Based Clustering of Smartphone Accelerometer Data

Cited by 11 publications

References 40 publications

Crack Detection and Analysis of Concrete Structures Based on Neural Network and Clustering

Crack Detection and Analysis of Concrete Structures Based on Neural Network and Clustering

Wavelet Set Theory Enhanced Wavelet Entropy Clustering for Pattern Recognition

Applications of using connected vehicle data for pavement quality analysis

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