feat: major add content to introductions and literature review

latex/bibliography.bib

@@ -14,6 +14,24 @@
   file = {C:\Users\damar\Zotero\storage\5WG6DL7B\Abdeljaber et al. - 2017 - Real-time vibration-based structural damage detect.pdf}
 }
 
+@article{gui2017,
+  title = {Data-Driven Support Vector Machine with Optimization Techniques for Structural Health Monitoring and Damage Detection},
+  author = {Gui, Guoqing and Pan, Hong and Lin, Zhibin and Li, Yonghua and Yuan, Zhijun},
+  date = {2017-02-01},
+  journaltitle = {KSCE Journal of Civil Engineering},
+  shortjournal = {KSCE Journal of Civil Engineering},
+  volume = {21},
+  number = {2},
+  pages = {523--534},
+  issn = {1226-7988},
+  doi = {10.1007/s12205-017-1518-5},
+  url = {https://www.sciencedirect.com/science/article/pii/S1226798824047913},
+  urldate = {2025-09-29},
+  abstract = {Rapid detecting damages/defeats in the large-scale civil engineering structures, assessing their conditions and timely decision making are crucial to ensure their health and ultimately enhance the level of public safety. Advanced sensor network techniques recently allow collecting large amounts of data for structural health monitoring and damage detection, while how to effectively interpret these complex sensor data to technical information posts many challenges. This paper presents three optimization-algorithm based support vector machines for damage detection. The optimization algorithms, including grid-search, partial swarm optimization and genetic algorithm, are used to optimize the penalty parameters and Gaussian kernel function parameters. Two types of feature extraction methods in terms of time-series data are selected to capture effective damage characteristics. A benchmark experimental data with the 17 different scenarios in the literature were used for verifying the proposed data-driven methods. Numerical results revealed that all three optimized machine learning methods exhibited significantly improvement in sensitivity, accuracy and effectiveness over conventional methods. The genetic algorithm based SVM had a better prediction than other methods. Two different feature methods used in this study also demonstrated the appropriate features are crucial to improve the sensitivity in detecting damage and assessing structural health conditions. The findings of this study are expected to help engineers to process big data and effectively detect the damage/defects, and thus enable them to make timely decision for supporting civil infrastructure management practices.},
+  keywords = {data-driven modeling,optimization,structural health monitoring and damage detection,support vector machine learning},
+  file = {C\:\\Users\\damar\\Zotero\\storage\\V8PP7XRS\\Gui et al. - 2017 - Data-driven support vector machine with optimizati.pdf;C\:\\Users\\damar\\Zotero\\storage\\KMM2Q6NT\\S1226798824047913.html}
+}
+
 @book{geron2019,
   title = {Hands-on Machine Learning with {{Scikit-Learn}}, {{Keras}}, and {{TensorFlow}}: Concepts, Tools, and Techniques to Build Intelligent Systems},
   shorttitle = {Hands-on Machine Learning with {{Scikit-Learn}}, {{Keras}}, and {{TensorFlow}}},
@@ -172,6 +190,25 @@
   file = {C\:\\Users\\damar\\Zotero\\storage\\59EASW6K\\Avci et al. - 2021 - A review of vibration-based damage detection in ci.pdf;C\:\\Users\\damar\\Zotero\\storage\\GQZUKPQN\\10.1016@j.ymssp.2020.107077.pdf.pdf}
 }
 
+@article{katam2025,
+  title = {Machine Learning-Driven Structural Health Monitoring: {{STFT-based}} Feature Extraction for Damage Detection},
+  shorttitle = {Machine Learning-Driven Structural Health Monitoring},
+  author = {Katam, Rakesh and Pasupuleti, Venkata Dilip Kumar and Kalapatapu, Prafulla},
+  date = {2025-08},
+  journaltitle = {Structures},
+  shortjournal = {Structures},
+  volume = {78},
+  pages = {109244},
+  issn = {23520124},
+  doi = {10.1016/j.istruc.2025.109244},
+  url = {https://linkinghub.elsevier.com/retrieve/pii/S2352012425010586},
+  urldate = {2025-06-05},
+  abstract = {Structural health monitoring (SHM) is essential for ensuring the safety and durability of engineering structures by enabling early identification of damage. This research presents a novel approach that integrates experimental testing, signal processing, and machine learning to enhance damage detection in cantilever beams, mainly when working with limited datasets. Vibration-based time-series data (VBTSD) are gathered from damaged and un­ damaged beams, utilizing fast fourier transform (FFT) and short-time fourier transform (STFT) for frequencydomain analysis. While FFT delivers a comprehensive spectral overview, STFT provides a focused timefrequency analysis, effectively capturing transient structural changes that are critical for early damage detec­ tion. To manage the high-dimensional STFT feature space, an autoencoder is utilized to extract compressed yet informative representations while preserving essential frequency-magnitude variations. The resulting encoded features are then used to train a support vector machine (SVM) classifier, achieving an impressive 98 \% accuracy in predicting the presence of structural damage. The proposed framework is designed to function effectively even with limited data availability, ensuring robustness in real-world SHM applications where data collection is often restricted. The high-resolution frequency selectivity offered by STFT surpasses traditional methods such as wavelet transforms and standalone FFT, making it exceptionally suitable for real-time damage detection. This research highlights the combination of vibration-based feature extraction and machine learning, resulting in a scalable, data-efficient, and computationally feasible approach for SHM. The results aid in the progression of automated damage classification, presenting a practical and dependable resource to improve structural resilience and safety within civil engineering applications.},
+  langid = {english},
+  file = {C:\Users\damar\Zotero\storage\U2WHH4SL\Katam et al. - 2025 - Machine learning-driven structural health monitori.pdf}
+}
+
+
 @inproceedings{bulut2005,
   title = {Real-Time Nondestructive Structural Health Monitoring Using Support Vector Machines and Wavelets},
   author = {Bulut, Ahmet and Singh, Ambuj K. and Shin, Peter and Fountain, Tony and Jasso, Hector and Yan, Linjun and Elgamal, Ahmed},