Predictive Modeling of Pavement Damage Using Machine Learning and Big Data Processing

Abstract

Potholes, soil settlement, and road subsidence have become major road safety hazards in South Korea. Such problems not only impede driver and pedestrian safety but also cause secondary accidents, economic losses, and damage the nation's image. To this end, we developed local predictive models that can be extrapolated to national estimation models. These models were developed from a specific area (Seoul Metropolitan City) that has the highest occurrences of potholes and road subsidence. This research utilized big data and artificial intelligence techniques to develop these models. The first step involved the dimensional reduction of independent variables using a mechanical-statistical approach. A data standardization process was then used for reducing the uncertainty of these variables. A total of 19 machine learning optimization methods were used to train the standardized variables. The optimized models were finally determined by an error comparison. As a result, the optimized prediction models for potholes, soil settlement, and road subsidence were found to be multiple regression analysis that showed an accuracy of 70% and robust regression analysis that showed an accuracy of 73%.

Application of Soft Computing in Predicting Groundwater Quality Parameters

“…The covariance function can be identified via different kernel functions, which can be parameterized in terms of kernel parameters in vector θ; hence, a covariance function can be expressed as k(x i , x j |θ) (Kim et al, 2019). In the current study, we will perform the prediction by applying these four kernel functions: rational quadratic; exponential; squared exponential; and matern 5/2.…”

“…Ensembles of regression trees (ER): It is a multilearning algorithm method that complements individual MLAs, and bagging and boosting trees are typical (Breiman, 1996;Hastie et al, 2009). The ensembles used to model groundwater quality in this study are described as follows: boosted regression tree: the boosted tree reinforces training as a totality by altering the weights of weak learning (Mohamed et al, 2017;Kim et al, 2019). The model is an ensemble technique that depends on both the strength of the regression tree (models that use a recessive dual split to answer their predictors) and the boosting algorithm (a grouping of various models for adjusting the prediction of performance).…”

Prediction of the Temperature of Liquid Aluminum and the Dissolved Hydrogen Content in Liquid Aluminum with a Machine Learning Approach

“…. The covariance function can be defined by various kernel functions, which can be parameterized in terms of the kernel parameters in vector θ, thus the covariance function can be expressed as k(x i , x j θ) [46,47]. In this work, we performed the prediction by using four different kernel functions such as rational quadratic, exponential, squared exponential, and matern 5/2 kernels.…”

“…The ensembles of regression trees are multi-learning algorithm techniques that complement the individual machine learning algorithms, and the bagged tree and boosted tree are typical [50,51]. The bagged tree makes a decision by constructing the tree by training the variables that are composed by randomly extracting the same size from the independent variables, and the boosted tree reinforces the learning as a whole by adjusting the weight of the weak learning [46,52]. In this work, the minimum leaf size and number of learners were set to 8 and 30 for both methods, and the learning rate of 0.1 was used for the boosted tree model.…”