Advancing Earthquake Engineering through Artificial Intelligence and Machine Learning

Machine learning (ML) has become a transformative tool in earthquake engineering, offering powerful capabilities to model complex nonlinear patterns in seismic data and improve hazard assessment, earthquake forecasting and structural health monitoring. Despite its rapid adoption, the existing literature lacks a comprehensive synthesis that integrates recent developments across key research areas. This review addresses this gap through a dual-method approach, combining a scientometric analysis of global publication trends, leading authors, contributing countries, funding sources, and journals with a systematic evaluation of 89 representative studies focused on ML-based ground-motion models (GMMs), earthquake prediction models (EQPMs) and structural health monitoring (SHM). The scientometric analysis reveals rapid growth in ML applications since 2015, with China and the United States as dominant contributors and a wide range of interdisciplinary journals serving as major publication sources. The systematic review demonstrates that advanced ML techniques (deep neural networks, ensemble learners, SVMs, etc.) dominate recent studies. In GMMs, ML models routinely improve ground-motion intensity predictions over traditional regression. In EQPMs, ML algorithms identify subtle precursory patterns in seismic catalogs. In SHM, vision-based CNNs (e.g. U-Nets) and hybrid CNN–RNN models achieve high-accuracy damage detection and localization. Key advantages of ML include higher predictive accuracy (e.g. matching near-zero residuals of state-of-the-art GMPEs) and flexibility (automatic feature learning from complex seismic data). ML systems also enable efficient, near-real-time inference (e.g. orders-of-magnitude faster aftershock forecasting). However, challenges remain: seismic datasets are often limited, noisy, and imbalanced, leading to overfitting and limited generalizability. Moreover, complex ML models lack transparency, their interpretability requires developing explainability techniques and rigorous validation to ensure trust. Looking ahead, the review emphasizes the development of explainable, uncertainty-aware ML models, integration of physics-based constraints (e.g. physics-informed neural nets), and establishment of standardized benchmark datasets and evaluation protocols. These steps, along with expanded data resources (real-time sensor networks, satellite measurements, and open-source initiatives), will support robust, transparent, and reproducible ML tools for seismic hazard assessment and resilient infrastructure design.

The complete published paper is available at: 

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Paper can be cited as: 

Noor, U.A.* Machine Learning Innovations in Revolutionizing Earthquake Engineering: A Review. Arch Computat Methods Eng (2025). https://doi.org/10.1007/s11831-025-10320-w

Accurate prediction of structural seismic response is essential for earthquake-resistant design, but conventional finite-element (FE) time-history analyses are computationally intensive, limiting large-scale or real-time use. In recent years, machine learning (ML) has emerged as a promising alternative, capable of learning complex nonlinear earthquake-structure relationships and providing rapid inferences. This review addresses a critical gap by combining a scientometric analysis with a systematic evaluation of the ML-SSRP (Machine Learning for Structural Seismic Response Prediction) literature. The scientometric study (Scopus, 2019–2025) maps research trends, collaboration patterns, and thematic developments, revealing rapid growth in the field, dominated by contributions from China and the USA, and a strong emphasis on data-driven prediction of reinforced-concrete systems. In parallel, 65 representative studies are reviewed to synthesize methodological developments across diverse ML architectures (e.g., ANNs, CNNs, LSTMs, transformers, ensembles) and structural typologies. Input features, model structures, seismic contexts, and performance metrics are systematically analyzed. The findings demonstrate that ML-based models consistently deliver high predictive accuracy with significantly reduced computational time compared to traditional simulations. However, challenges such as dependence on idealized training datasets, limited generalizability, and lack of interpretability persist. Promising directions include hybrid and physics-informed frameworks that aim to enhance robustness and adaptability. By integrating bibliometric mapping with in-depth technical analysis, this review provides a comprehensive overview of the current landscape, identifies key strengths and limitations of existing approaches, and outlines strategic priorities to advance ML-driven seismic response prediction in structural and earthquake engineering.

The complete published paper is available at: 

👉 Read/Download Full Paper (SpringerLink)

Paper can be cited as: 

Noor, U.A.* Advances in Machine Learning for Structural Seismic Response Prediction: A Comprehensive Review. Arch Computat Methods Eng (2025). https://doi.org/10.1007/s11831-025-10395-5