Prediction of the crack condition of highway pavements using machine learning models

Inkoom, Sylvester; Sobanjo, John; Barbu, Adrian; Niu, Xufeng

doi:10.1080/15732479.2019.1581230

Cited by 64 publications

(18 citation statements)

References 33 publications

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“…In the event of hazards such as hurricane-induced failure, earthquakes, electrical/mechanical downtimes and natural deterioration, the likelihood of incidence, conflicts, congestion and ultimate closure of roadways cannot be overlooked. Historical evidence has shown the impact of storm surges, tornadoes, hurricanes, wildfires, earthquakes and intentional hazards on the transportation [20][21][22][23][24][25][26][27].…”

Section: Literature Reviewmentioning

confidence: 99%

Competing Risks Models for the Assessment of Intelligent Transportation Systems Devices: A Case Study for Connected and Autonomous Vehicle Applications

2020

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Intelligent transportation system (ITS) has become a crucial section of transportation and traffic management systems in the past decades. As a result, transportation agencies keep improving the quality of transportation infrastructure management information for accessibility and security of transportation networks. The goal of this paper is to evaluate the impact of two competing risks: “natural deterioration” of ITS devices and hurricane-induced failure of the same components. The major devices employed in the architecture of this paper include closed circuit television (CCTV) cameras, automatic vehicle identification (AVI) systems, dynamic message signals (DMS), wireless communication systems and DMS towers. From the findings, it was evident that as ITS infrastructure devices age, the contribution of Hurricane Category 3 as a competing failure risk is higher and significant compared to the natural deterioration of devices. Hurricane Category 3 failure vs. natural deterioration indicated an average hazard ratio of 1.5 for CCTV, AVI and wireless communications systems and an average hazard ratio of 2.3 for DMS, DMS towers and portable DMS. The proportional hazard ratios of the Hurricane Category 1 compared to the devices was estimated as <0.001 and that of Hurricane Category 2 < 0.5, demonstrating the lesser impact of the Hurricane Categories 1 and 2. It is expedient to envisage and forecast the impact of hurricanes on the failure of wireless communication networks, vehicle detection systems and other message signals, in order to prevent vehicle to infrastructure connection disruption, especially for autonomous and connected vehicle systems.

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Section: Literature Reviewmentioning

confidence: 99%

Competing Risks Models for the Assessment of Intelligent Transportation Systems Devices: A Case Study for Connected and Autonomous Vehicle Applications

2020

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“…In recent years, the application of AI methods in engineering sciences is very common. Methods such as artificial neural networks (ANN) [34][35][36][37][38][39], radial basis function (RBF) [40][41][42][43][44], genetic programming (GP) [45][46][47][48][49], genetic algorithm (GA) [50][51][52][53], gene expression programming (GEP) [24,54,55], support vector machine (SVM) [40,54,[56][57][58], Random Forest (RF) [59][60][61][62][63], Fuzzy systems [64][65][66][67], and regression tree (RT) [68][69][70] have received much attention from engineers. In this paper, authors use the RF and Random Forest optimized by Genetic Algorithm (RF-GA) methods to predict PCI based on IRI.…”

Section: Analysis Phase By Using Artificial Intelligencementioning

confidence: 99%

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

Karballaeezadeh¹,

Mohammadzadeh²,

Moazami³

et al. 2020

Preprint

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The construction of different roads, such as freeways, highways, major roads or minor roads must be accompanied by constant monitoring and evaluation of service delivery. Pavements are generally assessed by engineers in terms of the smoothness, surface condition, structural condition and surface safety. Pavement assessment is often conducted using the qualitative indices such as international roughness index (IRI), pavement condition index (PCI), structural condition index (SCI) and skid resistance value (SRV), which are used for smoothness assessment, surface condition assessment, structural condition assessment, and surface safety assessment, respectively. In this paper, Tehran-Qom Freeway in Iran has been selected as the case study and its smoothness and pavement surface conditions are assessed. At 2-km intervals, a 100-meter sample unit is selected in the slow-speed lane (totally, 118 sample units). In these sample units, the PCI is calculated after a visual inspection of the pavement and the recording of distresses. Then, in each sample unit, the average IRI is computed. The purpose of this study is to provide a method for estimating PCI based on IRI. The proposed theory was developed by Random Forest (RF), and Random Forest optimized by Genetic Algorithm (RF-GA) methods and these methods were validated using correlation coefficient (CC), scattered index (SI), and Willmott’s index of agreement (WI) criteria. The proposed method reduces costs, saves time and eliminates the safety risks.

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“…In recent times, pavement management systems employed the use of intelligent algorithms which involves data mining for condition classification for efficient and reliable maintenance decisions [11][12][13]. The use of intelligent algorithms in pavement surface condition classification is apt in road pavement management system, since the process is characterised with inconsistencies caused by subjective judgment and measurement of performance variables [12][13][14]. Hence the need for optimum classification of road pavement surface condition based on damaging effects at different stages of the life cycle for efficient and sustainable management policy.…”

Section: Pavement Management Systemsmentioning

confidence: 99%

Optimised Surface Condition Classification of Flexible Road Pavement Using AutoWEKA Model

Adeke

Zava

Tyogo

2020

Usak University Journal of Engineering Sciences

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The development of pavement management tools using intelligent algorithms requires a robust form of data mining -data classification for efficient and reliable results. The aim of this study is to investigate and optimally classify the surface condition of flexible road pavement along 60 km length of the Zaria -Kaduna Federal Highway in Northern Nigeria for maintenance decision. The study used data mining technique for the classification of pavement surface condition into good, satisfactory, fair, poor, very poor, serious or failed. A field survey was carried out to examine the surface area and length of various surface defects such as cracks, potholes, rutting and edge failure within chainages measuring 200 meters apart, which was used to compute the Pavement Condition Index (PCI) values and section classification in accordance with procedures stated in ASTM D6433. The AutoWEKA model of Waikato Environment for Knowledge Analysis (WEKA) software was used to optimally classify the surface condition of the highway. Results indicated that, 79.67% of the 300 total instances considered by the model were correctly classified while 20.33% of the instances were incorrectly classified. The optimum surface condition classification showed that worse pavement surface conditions of the sampled site were 'Poor', 'Very Poor' and 'Failed' at 77 (32.22%), 51 (21.34%) and 54 (22.59%) instances respectively of the correctly classified 239 instances out of the 300 total instances sampled. Based on its present condition, 76.15% of the road segment was bad. The rehabilitation or reconstruction of the Zaria -Kaduna Federal Highway was therefore recommended for improved condition and optimum performance.

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Prediction of the crack condition of highway pavements using machine learning models

Cited by 64 publications

References 33 publications

Competing Risks Models for the Assessment of Intelligent Transportation Systems Devices: A Case Study for Connected and Autonomous Vehicle Applications

Competing Risks Models for the Assessment of Intelligent Transportation Systems Devices: A Case Study for Connected and Autonomous Vehicle Applications

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

Optimised Surface Condition Classification of Flexible Road Pavement Using AutoWEKA Model

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