Contribution of analysis and detection the risks appearing in roads using GPR method: A case study in Morocco

Alsharahi, Gamil; Bouami, Mouncef Filali; Faize, Ahmed; Louzazni, Mohamed; Khamlichi, Abdellatif; Atounti, Mohamed

doi:10.1016/j.asej.2020.10.014

Cited by 12 publications

(6 citation statements)

References 34 publications

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“…Identification of distress categories has become more challenging as the differentiation between distress categories has become more refined. Analysis of the measured data revealed that unlike common highway distresses such as cracks [12] and Cavities [13], the four categories of distresses found in East China do not exhibit typical hyperbolic features in the image data. As a result, the accurate detection of these four categories of distress is much more difficult in this region.…”

Section: Introductionmentioning

confidence: 82%

Performance Evaluation of Convolutional Neural Network Models for Classification of Highway Hidden Distresses with GPR B-Scan Images

Jin,

Liu,

Cai

et al. 2024

Applied Sciences

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Despite the considerable advancements in automated identification methods of highway hidden distress with ground-penetrating radar (GPR) images, there still exist challenges in realizing automated identification of highway hidden distress owing to the quantity, variability, and reliability of the distress samples and diversity of classification models. Firstly, the dataset collected contains 31,640 samples categorized into four categories: interlayer debonding, interlayer loosening, interlayer water seepage, and structural loosening from 1500 km highway, for obtaining larger enough samples and covering the variable range of distress samples. Secondly, the distresses were labeled by experienced experts, and the labels were verified with drilled cores to ensure their reliability. Lastly, 18 exemplary convolutional neural network (CNN) models from 8 different architectures were evaluated using evaluation metrics such as precision, recall, and f1-score. Further, confusion matrix and Grad-CAM techniques were utilized to analyze these models. The experimental results show that VGG13 performed most prominently and stably, while the lightweight network SqueezeNet1_1 performed particularly well with a batch size of 64. Furthermore, this study indicates that models with fewer layers can achieve comparable or better performance than deeper models.

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Section: Introductionmentioning

confidence: 82%

Performance Evaluation of Convolutional Neural Network Models for Classification of Highway Hidden Distresses with GPR B-Scan Images

Jin,

Liu,

Cai

et al. 2024

Applied Sciences

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“…Lastly, ground-penetrating radar (GPR) was used to assess the asphalt thickness and electromagnetic properties. GPR technology is also sometimes used to detect the presence of cracks and cavities [34].…”

Section: Smart Probe Vehiclesmentioning

confidence: 99%

“…Regarding the structural category, it was also divided into two distinct sub-categories related to the main focus of the systems under analysis. The works were divided into those that focused on the identification of fatigue cracking [42,43,46,48,50,53], most commonly bottom-up cracks, and the estimation of pavement layers' elastic modulus and the prediction of mechanical response [34,45,49,51,52,[54][55][56], which are intrinsically related.…”

Section: General Aspectsmentioning

confidence: 99%

Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review

Amândio¹,

Parente²,

Neves

et al. 2021

Buildings

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Nowadays, pavement management systems (PMS) are mainly based on monitoring processes that have been established for a long time, and strongly depend on acquired experience. However, with the emergence of smart technologies, such as internet of things and artificial intelligence, PMS could be improved by applying these new smart technologies to their decision support systems, not just by updating their data collection methodologies, but also their data analysis tools. The application of these smart technologies to the field of pavement monitoring and condition evaluation will undoubtedly contribute to more efficient, less costly, safer, and environmentally friendly methodologies. Thus, the main drive of the present work is to provide insight for the development of future decision support systems for smart pavement management by conducting a systematic literature review of the developed works that apply smart technologies to this field. The conclusions drawn from the analysis allowed for the identification of a series of future direction recommendations for researchers. In fact, future PMS should tend to be capable of collecting and analyzing data at different levels, both externally at the surface or inside the pavement, as well as to detect and predict all types of functional and structural flaws and defects.

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“…Vast stretches of land also offer opportunities for wind energy, while hydrothermal, geothermal, and biomass resources are being harnessed in various regions [8]. One of the key features of renewable energies in Africa is their ability to provide electricity to remote areas where traditional grids are unavailable [9,10]. Additionally, increasing investments in the renewable energy sector in Africa are driving technological innovation and creating local employment opportunities [11].…”

Section: Introductionmentioning

confidence: 99%

Renewable Energy Research in Africa: A Bibliometric Review (1979-2022)

EL Moussaoui,

Lamkaddem,

El Alami

et al. 2024

IJSDP

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This paper presents a bibliometric analysis of scientific research on renewable energy in Africa, a rapidly growing field driven by the need for sustainable and accessible energy solutions. It highlights Africa's role as a hub for renewable energy innovation, with local researchers collaborating with global institutions to address the continent's unique energy challenges. This synergy is enhancing energy access in African communities and contributing to the global advancement of renewable technologies. The study meticulously examines 3,109 scientific publications from the Scopus database, spanning from 1979 to 2022. It analyzes the evolution, geographical distribution, and impact of these publications, with a focus on international collaborations and scientific output. Findings indicate that South Africa leads in productivity with 962 publications and hosts the top affiliating institutions in this domain. The study also reveals an exponential increase in renewable energy research, particularly from South Africa, the United States, and the United Kingdom, emphasizing the need for more collaborative efforts and knowledge exchange globally. This analysis provides critical insights into the current landscape of renewable energy research and pinpoints areas ripe for further exploration and development in this vital sector.

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Contribution of analysis and detection the risks appearing in roads using GPR method: A case study in Morocco

Cited by 12 publications

References 34 publications

Performance Evaluation of Convolutional Neural Network Models for Classification of Highway Hidden Distresses with GPR B-Scan Images

Performance Evaluation of Convolutional Neural Network Models for Classification of Highway Hidden Distresses with GPR B-Scan Images

Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review

Renewable Energy Research in Africa: A Bibliometric Review (1979-2022)

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