Thus, various methods have been presented to enhance computational efficiency, including the wave FE method, the scaled boundary FE method, and the semi-analytical finite element (SAFE) method [21; 22; 23]. The SAFE method has shown better performance and has been successfully applied to multi-wire steel cables and rails [24; 25]. This method considers only the cross-sectional properties and assumes the exponential attenuation of waves in the propagation direction. [PERSON] et al. [26] utilized a SAFE model to investigate the influence of different support conditions on the dispersion relations of rails. [PERSON] et al. [27] extended the SAFE method to derive dispersion curves for plates with pillared metasurfaces. Generally, waveguides with arbitrary cross sections exhibit severer dispersion properties than those of plates or rods, meaning that multiple wave modes can exist simultaneously under a single excitation frequency. The main wave modes are closely related to the excitation type, but the relationship between the generated wave modes and excitation type has not been revealed. Machine learning has emerged as a reasonable alternative for extracting features from guided wave signals [28]. Numerous advanced neural network architectures have been proposed in the field of bolt looseness detection. [PERSON] et al. [29] presented an improved multiscale sample entropy-based method combined with a back propagation neural net work (BPNN). Damage indices under various bolt looseness conditions were obtained experimentally to construct the training dataset. Considering the difficulties in acquiring desirable training samples in real applications, [PERSON] et al. [30] proposed testing the experimental signals using the BPNN trained with FE simulation results. [PERSON] et al. [31] constructed a memory-augmented neural network to evaluate the bolt tension of spatial bolt-ball joints, incorporating Mel-frequency coefficients. [PERSON] et al. [32] proposed a convolutional neural network (CNN) enhanced by a multi-head attention mechanism for bolt looseness localization, with multivariate recurrence plots derived from guided wave signals serving as inputs. Furthermore, [PERSON] et al. [33] introduced a physics-informed CNN by involving the wave energy transmission mechanism of SH-typed guided waves into the loss function. Other neural networks, such as one-dimensional deep CNN, a long short-term memory (LSTM) network, were also developed for bolt looseness condition assessment [34; 35; 36]. The performance of the above neural networks depends on the quality of the training dataset, necessitating a sufficient number of training samples to accommodate the extensive training parameters of deep neural networks. However, data acquisition can be costly, so data argumentation is essential for expanding the training dataset [37; 38]. From the above, it is apparent that most research has focused on simple joints, while the wave propagation mechanisms in the I-shaped beam and multiple bolt connection region of the beam-column joint are not clarified. In addition, existing neural networks do not fully take into account the information from both the time and frequency domains of input samples. Given the constraints on the limited training samples, enhancing the precision of locating loosed bolts and the estimation of looseness degree is essential. The major contributions of this research are as follows: (1) A SAFE model is constructed for the I-shaped steel beam, and a mode weight coefficient is presented to study the mode components and wave propagation under different excitation loads; (2) A CNN-LSTM-multi-head self-attention (CNN-LSTM-MHSA) model is introduced for bolt looseness condition estimation using SH-typed guided waves, with time masking and frequency masking data augmentation performed to improve detection accuracy. The performance of the proposed method is verified through FE simulations. This paper is organized as follows: In Section 2, the dispersion properties of the I-shaped steel beam are analyzed.