# Aircraft Structural Stress Prediction Based on Multilayer Perceptron Neural Network
## 1 Introduction  
Airplanes are subjected to complex mechanical loads during flight, influenced by factors such as flight speed, altitude, ambient temperature, and atmospheric pressure. Consequently, stress analysis of aircraft structures is a crucial step in ensuring the overall structural health of the aircraft. The traditional approach to stress analysis involves using finite element method (FEM) software (patran2018) to simulate the aircraft structure under various operating conditions to obtain the stress field response for corresponding flight scenarios, followed by an evaluation of the aircraft's structural strength [1; 2; 3]. While FEM-based methods are effective in capturing changes in the aircraft's stress field, they are computationally intensive and time-consuming, especially for different aircraft structural models and systems that vary in complexity [4]. The process can become highly resource-demanding. Real-time structural life management requires continuous access to and monitoring of structural stress data, making traditional methods costly and time-prohibitive. Therefore, there is a pressing need to establish a method capable of rapidly predicting the aircraft's stress conditions to provide effective data support for aircraft structural health monitoring and service life analysis. With the rapid advancement of artificial intelligence, big data, and information technology, data-driven approaches to structural strength assessment, supported by powerful computational capabilities and vast datasets, have proven highly effective in monitoring the health of structures [5]. For instance, structural health monitoring techniques are employed to continuously track the condition of aircraft structures [6]. This method involves real-time monitoring through advanced sensor networks, which collect structural health data in realtime. Signal processing techniques are then used to analyze these signals, enabling the detection of structural conditions, identification of damage or performance degradation, and assessment of the remaining lifespan of the aircraft structure [7]. [PERSON] et al. conducted an analysis of the current state of the art in structural health inspection for aerospace vehicles, identifying two critical aspects of aircraft compliance with the maturity of materials in the structural field. They proposed a multi-sensor data fusion concept and demonstrated a conceptual design study of a fusion-based structural health monitoring (SHM) system. The results indicate that the multi-sensor data fusion concept can significantly benefit condition-based maintenance (CBM) applications in the aircraft industry, particularly in advancing the field of composite aircraft structures [8]. [PERSON] et al. developed a nonlinear unsteady aerodynamic model and proposed a theoretical method based on static aeroelasticity, utilizing flight data mining and a fuzzy logic modeling technique. This method assists in the certification of competency holders in aircraft maintenance facilities by enabling them to monitor static aeroelastic behavioral changes and address flight safety warning issues in aging transport aircraft. Additionally, it serves as a valuable tool in structural maintenance programs, enhancing aviation safety as well as operational efficiency [9]. Digital twin technology creates a virtual model of a physical entity by integrating finite element analysis, sensor technology, and visualization techniques. This virtual model enables the simulation, analysis, and prediction of structural behavior, as well as the online and offline monitoring of the structure's operation. As a result, it facilitates real-time monitoring and management of the aircraft's structural health, significantly enhancing maintenance and operational decision-making [10]. [PERSON] et al. [11] constructed the measurement-computation combined digital twin (MCC-DT) framework by integrating measurement and computational data. The construction process consists of four key steps.