## 2 Contributions  
This Special Issue, comprising a total of sixteen research articles, is dedicated to recent developments in artificial-intelligence-based methods for structural health monitoring. The first two studies explored the bridges' cable monitoring system. [PERSON] et al. [1] proposed a novel and intelligent approach for reliably evaluating the system of cable-supported bridges under stochastic traffic load by utilizing deep belief networks (DBNs). They introduced the theoretical basis for utilizing DBNs to approximate the structural load, and presented a computational framework to illustrate the procedures followed to evaluate the bridge system reliability via DBNs. [PERSON] et al. [2] proposed a novel method for identifying time-varying cable tension based on the variational mode decomposition (VMD) method. This recent method decomposes signals and adaptively estimates instantaneous frequencies combined with the Hilbert-Huang transform method, where the time-varying modal frequencies were identified from stay cable acceleration data, and then the time-varying cable tension was identified by the relationship between cable tension and identified fundamental frequency. The next two research articles [3; 4] of the Special Issue proposed a non-probabilistic surrogate model based on wavelet weighted least squares support vector machine (WWLS SVM) to address the problem of uncertainty in vibration-based damage detection. The input data for WWLSSVM consist of selected wavelet packet decomposition (WPD) features of the structural response signals, and the output is the [PERSON]'s modulus of structural elements. Figure 1: The main stages of typical integrating structural control and health monitoring systems. For damage segmentation purposes, [PERSON] et al. [5] proposed ways to improve the damage segmentation framework using two methods to train VGG-Unet models. They collected and manually labeled 200 corrosion images of steel and 500 crack images of rubber bearing to build the training dataset. The first method involves squashing segmentation to input squashed images from high resolution directly into the VGG-Unet model, while the second method, cropping segmentation, uses cropped images with a size of 224 \(\times\) 224 as the input images. Meanwhile, in the study by [PERSON] et al. [6], the structure damage segmentation was used alongside an open-source software platform \"InstaDa\" for the fast pixel-wise annotation of damage by utilizing binary masks to aid user input. They described details of InstaDam's software architecture and presented some of its key features. They also proposed several benefits of InstaDam by comparing it to the Image Labeler app in Matlab, and various comparisons were made between the InstaDam results. Moreover, experiments were conducted where two employed student annotators were given the task of annotating damage levels in a small dataset of images using Matlab. [PERSON] et al. [7] is investigated the effect of nano-MgO on the durability of normal concrete under freeze-thaw conditions. They also created a total of 98 cubic 10 \(\times\) 10 \(\times\) 10 concrete samples for the compressive strength test, while 78 cylindrical concrete 10 \(\times\) 20 samples were considered for the tensile strength and permeability tests to build the training dataset for gene expression programming (GEP) algorithm. They then applied GEP and three formulations were derived to predict the mechanical properties of concrete containing nano-MgO by randomly using 80% of the dataset for the training process and 20% for formulation testing. Machine learning (ML)-aided structural health monitoring (SHM) can rapidly evaluate the safety and integrity of the infrastructure. The next two research articles [8; 9] in this Special Issue introduced the framework of applying the ML algorithm for damage identification purposes. [PERSON] et al. [8] used low dimensionality, namely cumulative absolute velocity (CAV)-based features, to enable the use of ML for rapid damage assessment.