Data-driven framework for modeling deterioration of pavements in the state of Iowa

Hosseini, Seyed Amirhossein

doi:10.31274/etd-20200624-235

Cited by 8 publications

(8 citation statements)

References 34 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%

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%

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

Hosseini

Smadi

2021

Infrastructures

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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

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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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“…The large variance in the aggregated data suggests that a data classification/fusion is needed to get clearer patterns in the speed profiles to improve the long-range demand preview for predictive energy management (41)(42)(43)(44)(45). The traffic signals on arterial corridors dictate the traffic flow with the stop-and-go feature.…”

Section: Long Range: Traffic Data Classificationmentioning

confidence: 99%

Long-Term Vehicle Speed Prediction via Historical Traffic Data Analysis for Improved Energy Efficiency of Connected Electric Vehicles

Amini

Feng

Yang

et al. 2020

Transportation Research Record

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Connected and automated vehicles (CAVs) are expected to provide enhanced safety, mobility, and energy efficiency. While abundant evidence has been accumulated showing substantial energy saving potentials of CAVs through eco-driving, traffic condition prediction has remained to be the main challenge in capitalizing the gains. The coupled power and thermal subsystems of CAVs necessitate the use of different speed preview windows for effective and integrated power and thermal management. Real-time vehicle-to-infrastructure (V2I) communications can provide an accurate speed prediction over a short prediction horizon (e.g., 30 s to 60 s), but not for a long range (e.g., over 180 s). Therefore, advanced approaches are required to develop detailed speed prediction for robust optimization-based energy management of CAVs. This paper presents an integrated speed prediction framework based on historical traffic data classification and real-time V2I communications for efficient energy management of electrified CAVs. The proposed framework provides multi-range speed predictions with different fidelity over short and long horizons. The proposed multi-range speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs). The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the proposed data classification strategy over a long prediction horizon. Despite the uncertainty in long-range CAVs’ speed predictions, the vehicle-level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal driving (i.e., human driver) and conventional BTM strategy.

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Data-driven framework for modeling deterioration of pavements in the state of Iowa

Cited by 8 publications

References 34 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

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

Long-Term Vehicle Speed Prediction via Historical Traffic Data Analysis for Improved Energy Efficiency of Connected Electric Vehicles

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