Comparison of Pattern Recognition Techniques for Classification of the Acoustics of Loose Gravel

Saeed, Nausheen; Alam, Moudud; Nyberg, Roger G.; Dougherty, Mark; Jomaa, Diala; Rebreyend, Pascal

doi:10.1109/iscmi51676.2020.9311569

Cited by 4 publications

(5 citation statements)

References 20 publications

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“…The indicator of AADT is critical for decision-making in managing gravel roads ( 38 ). In Sweden, these roads have an AADT below 200 ( 54 ), and in Tanzania, the road manual recommends gravel roads for road design when the AADT is less than 300 ( 38 ). Clemmons and Saager ( 92 ) found that paving a road is justified at 145 vehicles per day, considering road user costs (RUCs), the environmental impact of dust, and road maintenance costs.…”

Section: Gravel Roads and Their Current Maintenance Practicesmentioning

confidence: 99%

“…They are cycles of even elevations and depressions aligned across the width of the gravel road surface, perpendicular to the traffic flow, causing a rough and uncomfortable ride for motorists ( 82 ). The integrity of the gravel influences the presence of loose gravel, and when heavy traffic loads crush the gravel and push it to the roadside, it results in ruts that retain water ( 54 ). Gravel loss contributes to rutting, the undesirable environmental impact of sedimentation and dust, and the reduced structural strength of the gravel road ( 38 , 67 ).…”

Section: Gravel Roads and Their Current Maintenance Practicesmentioning

confidence: 99%

“…However, this is not a comprehensive list of research centers investigating the maintenance of gravel roads and developing management systems. Other ongoing research and development on objective and automated assessment methods are also found ( 54 ). For instance, specialized vehicles and devices are used to assess the condition of the gravel road surface for road condition classification ( 89 ).…”

Section: Gravel Roads and Their Current Maintenance Practicesmentioning

confidence: 99%

“…From Table 5, evidence of ongoing research about the objective assessment of the condition of gravels is found. Saeed et al ( 54 ) applied acoustic data of gravel hitting the bottom of a car to determine the condition of the loose gravel; it is an objective and cost-effective method of assessing loose gravel. In addition, research on digital image processing for classification, pattern recognition, and feature extraction is growing, using processing algorithms for data collection, providing decision-makers with performance tools for data extraction and classification and facilitating strategic maintenance plans ( 69 ).…”

Section: The Future Of Gravel Road Maintenancementioning

confidence: 99%

See 3 more Smart Citations

Literature Review on Gravel Road Maintenance: Current State and Directions for Future Research

Mbiyana

Kans

Campos

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Gravel roads form a significant share of the global road network, usually in sparsely populated rural areas. They are important, especially in agriculture, tourism, and forestry, connecting rural to urban areas. This systematic literature study comprises 105 reviewed publications on gravel road maintenance. Review articles on maintenance management practices, especially concerning objective condition assessment and data-driven methods (DDMs), are lacking. Therefore, this review provides a concise overview of current gravel road maintenance practices and ongoing research on objective condition assessment and DDMs for gravel road maintenance. It offers researchers in gravel road maintenance and other related fields a clear indication of where to focus their research efforts, as it suggests the direction for future research. Visual assessment methods are predominant for monitoring the condition of gravel roads, while objective methods and DDMs are not common. Research on gravel roads and their maintenance has increased in the last two decades, especially in North America and Northern Europe. Condition assessment is shifting from subjective to objective methods, utilizing knowledge from technological advancements in image processing, vibration and acoustics analysis, and so forth. There are some excellent research initiatives for objectively assessing the condition of gravel roads and DDMs, but the practical implementation is limited. Implementing objective assessment methods and DDMs generally improves the management of gravel roads with regard to decision-making, maintenance costs, safety, and the stability and comfort of the ride. Objective condition assessment and DDMs have the potential to enhance maintenance practices in the maintenance of gravel roads.

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Section: Gravel Roads and Their Current Maintenance Practicesmentioning

confidence: 99%

Section: Gravel Roads and Their Current Maintenance Practicesmentioning

confidence: 99%

Section: Gravel Roads and Their Current Maintenance Practicesmentioning

confidence: 99%

Section: The Future Of Gravel Road Maintenancementioning

confidence: 99%

See 2 more Smart Citations

Literature Review on Gravel Road Maintenance: Current State and Directions for Future Research

Mbiyana

Kans

Campos

et al. 2022

Transportation Research Record: Journal of the Transportation R

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“…Information obtained from the semantic audio analysis can be useful for analyzing, classifying, and predicting an event [ 16 ]. Automatic sound analysis systems might not replace experts, but they can support them by processing large data sets and yielding results that facilitate the decision-making process [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of the Acoustics of Loose Gravel

Saeed

Nyberg

Alam

et al. 2021

Sensors

Self Cite

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Road condition evaluation is a critical part of gravel road maintenance. One of the assessed parameters is the amount of loose gravel, as this determines the driving quality and safety. Loose gravel can cause tires to slip and the driver to lose control. An expert assesses the road conditions subjectively by looking at images and notes. This method is labor-intensive and subject to error in judgment; therefore, its reliability is questionable. Road management agencies look for automated and objective measurement systems. In this study, acoustic data on gravel hitting the bottom of a car was used. The connection between the acoustics and the condition of loose gravel on gravel roads was assessed. Traditional supervised learning algorithms and convolution neural network (CNN) were applied, and their performances are compared for the classification of loose gravel acoustics. The advantage of using a pre-trained CNN is that it selects relevant features for training. In addition, pre-trained networks offer the advantage of not requiring days of training or colossal training data. In supervised learning, the accuracy of the ensemble bagged tree algorithm for gravel and non-gravel sound classification was found to be 97.5%, whereas, in the case of deep learning, pre-trained network GoogLeNet accuracy was 97.91% for classifying spectrogram images of the gravel sounds.

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