Abstract
Different types of distresses affect cement concrete pavement at different degrees. The determination of dominant distresses of the pavement preventive maintenance (PM) and its judgement standard can provide corresponding basis for PM decision. In this paper, 22 military airports in Northeast China, such as Heilongjiang Province, Jilin Province, and Liaoning Province, were selected to collect the data of pavement distresses. Based on the structural equation model (SEM), the structural relationship between the influencing factors of each distress and the pavement damage was established, and the goodness-of-fit of the model was tested. In addition, through path analysis, the influence degree of five kinds of latent variables such as joint distress, surface distress, vertical distress, repair distress, and fracture distress on pavement damage was obtained. Four distresses, such as corner peeling, surface peeling, surface crack, and interplate slip, were identified as the dominant distresses of PM of cement concrete pavement. On this basis, a binary classification model of confusion matrix was constructed. The basic evaluation index, receiver operating characteristic (ROC) curve, and Kolmogorov–Smirnov (KS) curve were used to comprehensively determine the judgement standard of the dominant distresses of pavement PM from multiple evaluation angles (corner peeling rate ≤ 35%, surface peeling rate ≤ 30%, surface crack rate ≤ 8%, and interplate slip rate ≤ 0.5%). The judgement standard can be combined with the corresponding prediction model to determine the optimal timing of PM of cement concrete pavement and provide pavement maintenance managers with the support of decision-making.
Highlights
Eight research hypotheses were proposed for the conceptual model: (i) Hypothesis 1 (H1): joint distresses have a positive impact on pavement damage (ii) Hypothesis 2 (H2): surface distresses have a positive impact on pavement damage (iii) Hypothesis 3 (H3): vertical distresses have a positive impact on pavement damage (iv) Hypothesis 4 (H4): repair distresses have a positive impact on pavement damage (v) Hypothesis 5 (H5): fracture distresses have a positive impact on pavement damage
Results and Discussion. e influence path and strength of each factor on pavement damage are analyzed as follows. e path coefficients of joint distress, surface distress, vertical distress, repair distress, and fracture distress are 0.244, 0.337, 0.261, 0.093, and 0.065, respectively, which means that for every standard deviation (S.D.) of joint distress, surface distress, vertical distress, repair distress, or fracture distress, the pavement damage will increase by 0.244, 0.337, 0.261, 0.093, or 0.065 S.D. e positive coefficient indicates that the degree of pavement damage increases with the increase of joint distresses, surface distresses, vertical distresses, repair distresses, or fracture distresses, which supports our research hypothesis 1–5
According to the survey data of the influential factors of pavement damage collected in this paper, the structural equation model (SEM) was constructed and the path coefficient was estimated. e applicability and rationality of the model were verified by six statistical tests
Summary
Erefore, it is necessary to study the dominant distress and its judgement standard of the PM of the cement concrete pavement of the military airport according to different regional conditions. To study the relationship of cement concrete pavement distress of military airfields in the seasonal frozen region and determine the dominant pavement distress and its judgement standard of PM, this paper investigated military airfields in the seasonal frozen region of Northeast China, such as Heilongjiang Province, Jilin Province, and Liaoning Province. E survey data were sourced from 22 sample airports in the northeast seasonal frozen area, including the types and causes of pavement distresses in military airports over the years. The model fitting index test and path analysis were carried out to determine the dominant distress in PM of cement concrete pavement. Where Λx is the factor load matrix of the exogenous variable x, ξ is used as a latent exogenous variable, δ is the error vector of the exogenous variable, Λy is the factor load matrix of the endogenous variable y, η is an latent endogenous variable, ε is the error vector of the endogenous variable, B is the coefficient matrix of latent endogenous variables, Γ is the coefficient matrix of latent exogenous variables, and ζ is the error vector
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
