# Prediction of Self-Healing of Engineered Cementitious Composite Using Machine Learning Approaches
###### Abstract
However, the intrinsic self-healing ability of ECC is complex and difficult to predict because of different mineral admixture types, interactivity between different composites in the cementitious matrix and its interaction with the exposed environment [15], and unpredictable crack location, orientation, and width [16]. Previous studies have explored the influence of several factors such as limestone powders (LPs) [17, 18], fly ash (FA) [19, 20], hydrated lime [21], the water/binder ratio [22], water permeation [23], and different curing conditions (air, carbon dioxide, wet/dry, and water) [24] on the self-healing behavior of ECC. However, these studies did not predict the self-healing efficiency of ECC by modeling their experimental data. In fact, the relationship between multiple factors is complex and non-linear, so it is difficult to predict the self-healing of ECC mathematically based on the available data. Moreover, mathematical models based on empirical data are generally in regression forms, which cannot be used when the problem (e.g., prediction of the self-healing potential of ECC) contains too many independent variables and requires more assumptions to be made [25]. To account for the drawbacks of using mathematical models, machine learning (ML) techniques have been used for solving many civil engineering problems with multiple variables [26]. They are model-free approaches that do not rely on predefined models [27]. Many research works have been conducted using ML algorithms for the prediction of various properties of concrete [28, 29]. [PERSON] et al. [30] developed a hybrid support vector regression (SVR)-particle swarm optimization (PSO) algorithm model to predict the compressive strength and chloride ion penetrability of concretes containing metakaolin. [PERSON] et al. [31] predicted the bond strength of a glass-fiber-reinforced polymer bar in concrete by an artificial neural network (ANN) with the genetic algorithm (GA). [PERSON] et al. [32] proposed an ML method called extreme learning machine (ELM) to predict the compressive strength of lightweight foamed concrete. In the literature, the performance of various ML algorithms in predicting concrete properties have been evaluated and compared. [PERSON] and [PERSON] [33] reported that SVR is better than other individual methods in predicting the elastic modulus of normal and high-strength concrete. [PERSON] [34] compared the performance of several individual and ensemble methods for predicting the mechanical properties of high-performance concrete. The results revealed that ensemble learning strategies outperform individual learning techniques in predicting the compressive strength of high-performance concrete. [PERSON] et al. [27] employed three different individual approaches for modeling concrete failure surfaces. They found that all three individual approaches are able to fit the experimental data with low error. [PERSON] et al. [35] suggested that their proposed fuzzy inference system and ANN are more reliable than traditional regression models in predicting no-slump concrete. [PERSON] et al. [36] predicted the compressive strengths of an environmentally friendly concrete by using three individual methods, two ensemble methods, and four regression tree models. Their results showed that the individual Gaussian process regression model and its ensemble models outperformed other models. Although different ML algorithms have been utilized to predict several properties of concrete, the application of ML on self-healing prediction is considerably rare. Recently, [PERSON] and [PERSON] [37] reviewed the common methods used for modeling autogenous self-healing of concrete and stated that the methods can be classified into two categories: (1) numerical simulation and (2) ML. However, the only ML model reviewed in their study was the GA-ANN method proposed by [PERSON] et al. [3]. They predicted the self-healing ability of cement-based materials using a dataset collected from the literature.