Prioritisation of pavement maintenance sections deploying functional characteristics of pavements

Janani, L.; Dixit, Raunak Kumar; Sunitha, V.; Mathew, Samson

doi:10.1080/10298436.2019.1567923

Cited by 19 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given the advances of deep learning, there has been significant research using these techniques for Pavement Engineering applications [21][22][23][24]. These applications can be assigned to the following areas: Pavement condition and performance predictions [25][26][27][28], Pavement management systems [29][30][31], pavement performance forecasting [32][33][34], structural evaluations [35][36][37], modelling pavement materials [38][39][40] and pavement image analysis and classification [22,[41][42][43][44]. Pavement Image analysis and classification is the most researched area, where the focus has been split between image classifications, where images are classified based on the distress occurring in the image; and object detection, where distresses are located within bounding boxes or masks within the image.…”

Section: The Use Of Deep Learning In Pavement Engineeringmentioning

confidence: 99%

Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

et al. 2020

View full text Add to dashboard Cite

Governments are faced with countless challenges to maintain conditions of road networks. This is due to financial and physical resource deficiencies of road authorities. Therefore, low-cost automated systems are sought after to alleviate these issues and deliver adequate road conditions for citizens. There have been several attempts at creating such systems and integrating them within Pavement management systems. This paper utilizes replicable deep learning techniques to carry out hotspot analyses on urban road networks highlighting important pavement distress types and associated severities. Following this, analyses were performed illustrating how the hotspot analysis can be carried out to continuously monitor the structural health of the pavement network. The methodology is applied to a road network in Sicily, Italy where there are numerous roads in need of rehabilitation and repair. Damage detection models were created which accurately highlight the location and a severity assessment. Harmonized distress categories, based on industry standards, are utilized to create practical workflows. This creates a pipeline for future applications of automated pavement distress classification and a platform for an integrated approach towards optimizing urban pavement management systems.

show abstract

Section: The Use Of Deep Learning In Pavement Engineeringmentioning

confidence: 99%

Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

et al. 2020

View full text Add to dashboard Cite

show abstract

“…This increased cost can also have impact on the financial outlay and result in adverse effect on economy of country. It is advised that the planning and maintenance program should be examined the road condition on regular interval and timely maintenance of road should be done in order to avoid irreversible damages [16]. The pavement condition should be determined through structural adequacy, roughness, distress, and the extent of past maintenance activities, etc.…”

Section: Introductionmentioning

confidence: 99%

IoT and Cloud Based Automated Pothole Detection Model Using Extreme Gradient Boosting with Texture Descriptors

Ghafoor

2023

SCPE

View full text Add to dashboard Cite

One of crucial activity related to road monitoring and maintenance is the occurrence of potholes. These potholes are also be major reason of road accidents, damaging of vehicles, discomfort of passenger journey and extensive in terms of time and cost. But, identification of potholes can significantly alleviate the aforementioned issues. Other side, the Internet of Things (IoT) plays a crucial role in different applications, and provides viable and state of art solutions for variety of problems. Hence, the aim of this work is to develop a real time automated pothole detection model to detect the potholes in asphalt roads based on IoT devices.The proposed model comprises of three main components such as collection of pothole data and labeling, image pre-processing and texture feature extraction, and extreme gradient boosting (XGBoost) algorithm. The potholes data on asphalt road is collected by three IoT sensors such as accelerometer, ultrasonic sensor, and GPS and further, the collected data is transmitted on cloud via Wi-Fi module. The texture features are extracted using Gaussian steerable and median filters. The extreme gradient boosting (XGBoost) classifier is adopted for prediction task. The simulation results showed that proposed XGBoost model obtains higher accuracy, recall, precision and F1-score rates as 94.56, 97.41, 96.40, and 96.90 respectively using 10-cross fold validation method.

show abstract

Boosting Machine Learning Algorithm to Classify Road Conditions for Maintenance Strategy of Flexible Pavements

Kaur

Kumar

2023

Springer Tracts in Human-Centered Computing

View full text Add to dashboard Cite

Prioritisation of pavement maintenance sections deploying functional characteristics of pavements

Cited by 19 publications

References 22 publications

Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

IoT and Cloud Based Automated Pothole Detection Model Using Extreme Gradient Boosting with Texture Descriptors

Boosting Machine Learning Algorithm to Classify Road Conditions for Maintenance Strategy of Flexible Pavements

Contact Info

Product

Resources

About