###### Abstract
The proposed theories for quantitative crack determination make it difficult to choose a single parameter as a feature parameter for one-time measurement because many parameters have excellent abilities to detect surface cracks according to the aforementioned literature. Transmission, reflection, and scattering phenomena at the crack simultaneously occur. Therefore, the analysis based on any single wave type is partial information for sizing crack detection. More characteristic parameters of transmitted and reflected waves in the time domain, frequency domain, and time-frequency domain are employed to obtain more comprehensive information on crack size. Therefore, in this work, the relationships between crack size and several parameters based on transmitted waves and reflected waves that contain crack geometry information are presented in detail. Moreover, in the above studies, the fitted curves between crack size and reflection or transmission coefficients are usually used to evaluate the size of artificial cracks, such as slots and notches. However, only in the ideal experimental environment can artificial cracks with normal shapes be quantitatively determined using fitted curves. The ability of this methodology to be applied for real nondestructive detection is limited by two factors. First, the amount of real data to be acquired at different crack types is a large, time-consuming problem. Second, real cracks are complex, so a fitted curve obtained under experimental conditions does not work for real crack detection. For these two factors, intelligence algorithms have attracted the attention of researchers. Machine learning, including support vector machines (SVMs) and neural networks, has been a useful tool for fast, intelligent evaluation. [PERSON] [11; 12] used a particle swarm optimization neural network algorithm and a quantized particle swarm optimized support vector regression algorithm to intelligently detect and quantify the depth of surface-breaking cracks. [PERSON] [13] introduced an optimized physics-informed neural network trained to solve the problem of identifying a surface breaking crack in a metal plate. [PERSON] [14] proposed a layered multiclass SVM classification system that combined multiple SVM classifiers through a layered architecture to classify welding defects from ultrasonic test signals. From the above studies, the neural network algorithm and SVM algorithm can be used for the laser ultrasonic nondestructive detection of cracks. The application of neural network algorithms must obtain many crack samples of different depths and angles, which makes it difficult to train samples in practical applications. SVM has the advantage of maintaining high accuracy when processing small samples, so this paper selects SVM as the algorithm to extract crack information. SVM is a statistically supervised machine learning technique proposed by [PERSON] and [PERSON] in 1995 [15]. To date, it has been successfully applied to many pattern recognition problems, such as machine fault diagnosis. SVM can identify implicit information in data classifications with small data dimensions [16]. [PERSON] [17] proposed an automatic target segmentation method based on principal pixel analysis and SVM for image segmentation. [PERSON] [18] used the SVM algorithm for automatic fault identification of ultrasonic signals. [PERSON] [19] used different wavelets to extract bearing vibration characteristic signals and imported them into an SVM for fault diagnosis, which improved the success rate of fault type recognition. [PERSON] [20] proposed a system to characterize normal liver, cirrhotic liver, and hepatocellular carcinoma evolved on cirrhotic liver in which the genetic algorithm-SVM method is used to select features obtained via wavelet packet transform using the Haar wavelet filter. To obtain a precise evaluation model, choosing acoustic parameters linking to surface crack size is a key part of the SVM method.