## 4 Understanding of TBM Performance
### Current Developments in Performance Prediction
#### 4.2.3 Artificial Intelligence Model  
There are still studies that rely on conventional statistical and linear methods to forecast TBM performance [53; 54]. However, these methods have been criticized for their limitations in resolving complex and non-linear issues. [PERSON] et al. [30] argue that statistical models may not adequately describe non-linear and complex systems, while [PERSON] et al. [55] highlight the deterioration of their performance when outliers and extreme values are present in the data. [PERSON] et al. [46] suggest that multi-parameter models, compared to simple models, make better use of available project data and are easier to implement. Consequently, it can be argued that artificial intelligence computational models are the most suitable choice for predicting TBM performance. Artificial intelligence (AI) models offer substantial benefits in tackling the intricate, multi-parameter challenges characteristic of TBM operations. These advantages can be categorized into two principal aspects, as outlined below:  
* Data processing and analysis. AI models, particularly machine learning algorithms, excel in handling large datasets generated during TBM operations. These datasets encompass numerous parameters such as geological conditions, machine performance metrics, and environmental factors. AI models can efficiently process these data to provide real-time insights, which is crucial for making informed decisions and adjusting operational strategies dynamically. * Predictive modeling and optimization. AI-driven predictive models can forecast the performance of TBMs under varying conditions, enabling operators to optimize machine settings for different segments of a tunnel project. By simulating various scenarios, AI models help in identifying the most efficient operational parameters, which can lead to significant cost savings and improved project timelines. Moreover, optimization algorithms can continuously adjust TBM parameters in real-time, ensuring optimal performance throughout the tunneling process. Typically, due to the numerous factors influencing TBM performance and the complex interrelations among these factors, the introduction of AI models to handle these intricate relationships is imperative. Artificial intelligence techniques, such as artificial neural networks (ANNs), imperialist competitive algorithms (ICAs), support vector machines (SVMs), particle swarm optimization (PSO), and adaptive neuro-fuzzy inference systems (ANFISs) (see Figure 6), have been widely applied to solve various geotechnical engineering problems, including TBM performance prediction [56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69]. Currently, many researchers have achieved promising results by utilizing AI technology to predict multiple parameters related to TBM performance after collecting them. [PERSON] and [PERSON] [70] employ rule-based and parameter-based fuzzy inference systems (FISs) to predict TBM U using data from three different TBM projects. [PERSON] et al. [71] further utilize the SVM technique on the dataset obtained by [PERSON] [49]. [PERSON] et al. [71] propose optimization algorithms to enhance SVMs and utilize these hybrid models to evaluate TBM PR. [PERSON] and [PERSON] [72] suggest an ANN model for predicting tunnel boring rates using data from Athens metro tunnels. [PERSON] [30] and colleagues introduce the ANFIS, which demonstrates significantly higher PR prediction accuracy compared to statistical methods. [PERSON] et al. [53] employ ANNs to forecast TBM PR using data from the Queens Water Tunnel in the United States. In recent times, researchers have been developing sophisticated artificial intelligence models aimed at enhancing the predictive capabilities of TBM performance. To overcome the low learning rate and the issue of getting stuck in local minima, [PERSON] and [PERSON] [73] introduce several optimization methods, including hybrid harmony search, differential evolution (DE), and grey wolf optimizer (GWO), to estimate TBM PR.