Use of Deep Learning to Study Modeling Deterioration of Pavements a Case Study in Iowa

Hosseini, Seyed Amirhossein; Alhasan, Ahmad; Smadi, Omar

doi:10.3390/infrastructures5110095

Cited by 27 publications

(16 citation statements)

References 27 publications

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“…In this research, the result of the pavement prediction model (LSTM model), already developed in a previous study is used [35,36]. LSTM is used for time-dependent prediction of the pavement condition index.…”

Section: Project Levelmentioning

confidence: 99%

“…Information regarding the highway system, including construction history, section identification, maintenance history, pavement age, traffic loading, and pavement distresses are available in the Iowa DOT PMIS database and was used to develop the prediction model in the previous study [35]. The condition data of pavement sections from 1998 through 2018 was used for model development purposes.…”

Section: Datamentioning

confidence: 99%

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How Prediction Accuracy Can Affect the Decision-Making Process in Pavement Management System

Hosseini

Smadi

2021

Infrastructures

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One of the most important components of pavement management systems is predicting the deterioration of the network through performance models. The accuracy of the prediction model is important for prioritizing maintenance action. This paper describes how the accuracy of prediction models can have an effect on the decision-making process in terms of the cost of maintenance and rehabilitation activities. The process is simulating the propagation of the error between the actual and predicted values of pavement performance indicators. Different rate of error (10%, 30%, 50%, 70%, and 90%) was added into the result of prediction models. The results showed a strong correlation between the prediction models’ accuracy and the cost of maintenance and rehabilitation activities. The cost of treatment (in millions of dollars) over 20 years for five different scenarios increased from ($54.07–$92.95), ($53.89–$155.48), and ($74.41–$107.77) for asphalt, composite, and concrete pavement types, respectively. Increasing the rate of error also contributed to the prediction model, resulting in a higher benefit reduction rate.

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Section: Project Levelmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

How Prediction Accuracy Can Affect the Decision-Making Process in Pavement Management System

Hosseini

Smadi

2021

Infrastructures

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“…4-Learning-based methods have contributed significantly to roadway crack detection in recent years (39,40). Recently, the image data size has increased considerably, and concurrently the computation power of computers has soared up.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Adaptive Pixels Segmentation Algorithm for Pavement Crack Detection

Safaei¹,

Smadi²,

Safaei³

et al. 2021

Preprint

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Cracks considerably reduce the life span of pavement surfaces. Currently, there is a need for the development of robust automated distress evaluation systems that comprise a low-cost crack detection method for performing fast and cost-effective roadway health monitoring practices. Most of the current methods are costly and have labor-intensive learning processes, so they are not suitable for small local-level projects with limited resources or are only usable for specific pavement types. This paper proposes a new method that uses an improved version of the weighted neighborhood pixels segmentation algorithm to detect cracks in 2-D pavement images. This method uses the Gaussian cumulative density function as the adaptive threshold to overcome the drawback of fixed thresholds in noisy environments. The proposed algorithm was tested on 300 images containing a wide range of noise representative of different noise conditions. This method proved to be time and cost-efficient as it took less than 3.15 seconds per 320 × 480 pixels image for a Xeon (R) 3.70 GHz CPU processor to determine the detection results. This makes the model a perfect choice for county-level pavement maintenance projects requiring cost-effective pavement crack detection systems. The validation results were promising for the detection of low to severe-level cracks (Accuracy = 97.3%, Precision = 79.21%, Recall= 89.18% and F1 score = 83.9%).

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“…Learning-based methods have contributed significantly to pavement deterioration modeling and crack detection practices ( 37–39 ). Recently, the image data size has increased considerably, and concurrently the computation power of computers has soared.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient Road Crack Detection Based on an Adaptive Pixel-Level Segmentation Algorithm

Safaei

Smadi

Safaei

et al. 2021

Transportation Research Record: Journal of the Transportation R

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Cracks considerably reduce the life span of pavement surfaces. Currently, there is a need for the development of robust automated distress evaluation systems that comprise a low-cost crack detection method for performing fast and cost-effective roadway health monitoring practices. Most of the current methods are costly and have labor-intensive learning processes, so they are not suitable for small local-level projects with limited resources or are only usable for specific pavement types. This paper proposes a new method that uses an adapted version of the weighted neighborhood pixels segmentation algorithm to detect cracks in 2-D pavement images. The method uses the Gaussian cumulative density function (CDF) as the adaptive threshold to overcome the drawback of fixed thresholds in noisy environments. The proposed algorithm was tested on 300 images containing a wide range of noise representative of various pavement noise conditions. The method proved to be time and cost-efficient as it took less than 3.15 s per 320 × 480 pixels image for a Xeon (R) 3.70 GHz CPU processor to generate the detection results. This makes the proposed method a perfect choice for county-level pavement maintenance projects requiring cost-effective pavement crack detection systems. The validation results were promising for the detection of medium to severe-level cracks (precision = 79.21%, recall = 89.18%, and F1 score = 83.90%).

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Use of Deep Learning to Study Modeling Deterioration of Pavements a Case Study in Iowa

Cited by 27 publications

References 27 publications

How Prediction Accuracy Can Affect the Decision-Making Process in Pavement Management System

How Prediction Accuracy Can Affect the Decision-Making Process in Pavement Management System

A Novel Adaptive Pixels Segmentation Algorithm for Pavement Crack Detection

Efficient Road Crack Detection Based on an Adaptive Pixel-Level Segmentation Algorithm

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