Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

Abstract

The pavement is a complex structure that is influenced by various environmental and loading conditions. The regular assessment of pavement performance is essential for road network maintenance. International roughness index (IRI) and pavement condition index (PCI) are well-known indices used for smoothness and surface condition assessment, respectively. Machine learning techniques have recently made significant advancements in pavement engineering. This paper presents a novel roughness-distress study using random forest (RF). After determining the PCI and IRI values for the sample units, the PCI prediction process is advanced using RF and random forest trained with a genetic algorithm (RF-GA). The models are validated using correlation coefficient (CC), scatter index (SI), and Willmott’s index of agreement (WI) criteria. For the RF method, the values of the three parameters mentioned were −0.177, 0.296, and 0.281, respectively, whereas in the RF-GA method, −0.031, 0.238, and 0.297 values were obtained for these parameters. This paper aims to fulfill the literature’s identified gaps and help pavement engineers overcome the challenges with the conventional pavement maintenance systems.

Application of Analytical Hierarchy Process for Structural Health Monitoring and Prioritizing Concrete Bridges in Iran

“…Another gap that has been followed in this study is the lack of a comprehensive, practical, scientific method for bridge networks in Iran. Although there are attempts in the field of developing health monitoring methods for various infrastructures in Iran, such as [28][29][30][31][32][33][34][35][36][37]; however, the absence of a full detailed, efficient, specialized method for bridges can be felt. Consequently, the authors also tried to fill the later gap, and therefore, the proposed method is based on the condition of bridges in Iran.…”

Integrating Pavement Sensing Data for Pavement Condition Evaluation

“…Over recent decades, there have been many studies attempting to extend the power of IRI by investigating interdependencies between roughness and structural indices or other pavement surface distresses, such as cracking or rutting [ 15 ]. Most focus on the relationship between IRI and Pavement Condition Index (PCI), which is an indicator of surface condition, based on linear regression modelling and machine learning techniques [ 9 , 16 , 17 ]. The rationale behind this approach lies upon the fact that the relationship between roughness and pavement distresses is bilateral [ 17 , 18 ].…”

“…Most focus on the relationship between IRI and Pavement Condition Index (PCI), which is an indicator of surface condition, based on linear regression modelling and machine learning techniques [ 9 , 16 , 17 ]. The rationale behind this approach lies upon the fact that the relationship between roughness and pavement distresses is bilateral [ 17 , 18 ]. Rough surfaces might tend to increase the pavement vertical stresses, impose surface deformations and exacerbate pavement fatigue [ 17 ].…”

“…The rationale behind this approach lies upon the fact that the relationship between roughness and pavement distresses is bilateral [ 17 , 18 ]. Rough surfaces might tend to increase the pavement vertical stresses, impose surface deformations and exacerbate pavement fatigue [ 17 ]. Vice versa, a distressed pavement will progressively deteriorate pavement roughness [ 19 ].…”

Application of Analytical Hierarchy Process for Structural Health Monitoring and Prioritizing of Concrete Bridges in Iran

“…A large portion of the national resources of each country is invested in this area. As one of the most important parts of transportation systems, bridges have a critical role in urban development [1][2][3][4][5][6]. The bridge conditions in the transportation networks are so important that the costs incurred by out of service bridges are exorbitant.…”