Jul 2026· International journal of computer information systems and industrial management applications· Vol 18, pp. 215-228· 0 citations
TL;DR
These studies propose an artificial intelligence driven predictive monitoring framework of vibration based structural health assessment under real conditions including where the labelled damage data is unavailable to validate that the proposed framework allows for interpretable, scalable and data-driven predictive monitoring.
Abstract
Structural health information from continuous monitoring of vibration makes an important contribution to the development of a proactive approach to the management of civil infrastructure. These studies propose an artificial intelligence driven predictive monitoring framework of vibration based structural health assessment under real conditions including where the labelled damage data is unavailable. Structural behavior is evaluated according to baseline referenced deviation based on physics informed vibration features. A Structural Deviation Index is introduced to quantify deviation where median values range from the baseline measurements 0.42-0.45 to 3.68-3.92 in later monitoring tests representing progressive structural change. One-Class Support Vector Machine deviation scores reveal a corresponding deviation from close to zero to -0.76 which indicates that the classifier is highly sensitive to deviation in early stages. Band-limited spectral energy and dominant frequency are the most influential indicators with the value of permutation importance up to 0.231 and correlation coefficients up to 0.78, according to explainability analysis. The results validate that the proposed framework allows for interpretable, scalable and data-driven predictive monitoring. The study is an illustration of the possibilities of artificial intelligence to facilitate early warning, decision-making and smart infrastructure management with continuous structural health assessment.
The convergence of civil infrastructure and electrical power systems within smart city frameworks necessitates robust, cross-domain monitoring strategies. While machine learning (ML) has shown promise in isolated Structural Health Monitoring (SHM) and Predictive Maintenance (PdM), comparative evaluations across both domains remain fragmented. This study presents a comprehensive comparative analysis of four prominent ML algorithms Random Forest (RF), Support Vector Machines (SVM), Long Short-Term Memory (LSTM) networks, and XGBoost applied to multimodal sensor data. We utilized a synthesized dataset comprising vibration signatures from civil structures (bridge decks) and thermal-electrical load profiles from substation transformers. Our findings indicate that while LSTM networks excel in capturing temporal dependencies in electrical load forecasting (achieving an F1-score of 0.94), tree-based ensemble methods, specifically XGBoost, demonstrate superior efficacy in classifying structural damage from high-dimensional vibration features (accuracy of 96.2%). Furthermore, RF offered the most computationally efficient inference, making it highly suitable for edge-deployment in resource-constrained IoT nodes. This paper provides a practical decision-making framework for civil and electrical engineers selecting ML architectures for integrated smart infrastructure monitoring.
M. el-sseid, L. B. Ben Dalla, Tasnem ELsseid et al.· Al-Farooq Journal of Science...· 0 citations
With the growing complexity and aging of civil infrastructure, they must be monitored by an intelligent system and predictive maintenance solutions must be provided for the safety and reliability of the structures. The proposed architecture in this paper is founded on the idea of using the concept of AI to enable real-time monitoring and prediction of damage in smart civil infrastructure using the Digital Twin. The proposed framework integrates the three aspects of the IoT-based sensor network, cloud-edge computing and deep learning models and ties them to a dynamic Digital Twin to continuously monitor the circumstances of the structure and identify anomalies. Using a combination of AI algorithms, real-time data from accelerometers, strain gauges and vibration sensors is analysed to identify damage, predict cracks and evaluate structural performance. The Digital Twin connects physical and virtual infrastructure models to enable perpetual visualization and predictive analytics in order to plan maintenance proactively. The framework improves the accuracy of monitoring, lowers inspection costs and increases the resilience of infrastructure to structural failures. The proposed system can be effectively implemented to manage intelligent and sustainable infrastructure for bridges, buildings, tunnels and smart city infrastructure.
Navami P M, Reshma M. Raju· International Conference on...· 0 citations
This systematic review synthesizes recent advances in AI applications for SHM across civil infrastructure including bridges, buildings, tunnels, and dams and identifies interdisciplinary opportunities including federated learning for decentralized monitoring, explainable AI for stakeholder trust, and autonomous inspection systems.
M. Khan, Muhammad Shoaib Ashraf, Muhammad Jahanzeb et al.· International journal of com...· 0 citations
Structural health monitoring systems based on machine learning routinely achieve high classification accuracy but rarely explain the basis of their decisions, limiting their adoption by practicing engineers who must justify safety-critical actions. This paper presents a framework that combines vibration-based and image-based damage assessment with explainable artificial intelligence and a data-driven digital twin. An XGBoost classifier is trained on physics-informed modal features extracted from 15 months of monitoring data from the KW51 railway bridge in Leuven, Belgium, to distinguish healthy, damaged, and repaired structural states, while Shapley Additive Explanations are used to attribute each prediction to specific frequency, damping, and environmental features. In parallel, an EfficientNet-B0 convolutional neural network is fine-tuned on the Concrete Defect Bridge Image dataset to classify six concrete surface defect types, and Gradient-weighted Class Activation Mapping is used to visually localize the image regions driving each defect prediction. The outputs of the two branches are combined into a Structural Health Index designed to support a continuously updated data-driven digital twin. On a temporally held-out test set, the vibration classifier achieves a macro F1 score of 0.682 and an area under the receiver operating characteristic curve of 0.880, while the image classifier achieves a macro F1 score of 0.886 and an area under the curve of 0.977. The study also identifies three findings with direct relevance for deployment: environmental variables retain a measurable share of feature importance after detrending, the vibration classifier confuses the damaged and repaired classes in a safety-relevant direction, and a fixed-weight fusion of the two branches on unpaired data does not improve upon the vibration branch alone. Together, these findings highlight the importance of paired multi-modal datasets for reliable fusion and provide practical guidance for the development of robust, explainable, and validated digital twin frameworks for structural health monitoring.
Unknown authors· Frontiers in Built Environme...· 0 citations
Results show that multi-year monitoring data can be reduced into compact fatigue-relevant features while preserving traceability to raw measurements, and a supervisory agentic layer coordinates data-quality checks, multi-sensor consistency review, and confidence-tagged substitution, creating an auditable workflow for engineering decision support.
Guga Gugaratshan, A. Halfpenny, F. Kihm et al.· e-Journal of Nondestructive...· 0 citations
The natural and accidental deterioration of civil structures, combined with the increasing complexity of engineering projects, demands continuous monitoring strategies to ensure structural safety and durability. In this context, Structural Health Monitoring (SHM) techniques based on variations in modal properties have been widely investigated due to their capability for global assessment. However, practical limitations—such as information loss during modal identification and the low sensitivity of certain modal parameters to damage—have motivated the development of alternative approaches. Among these approaches, methodologies based on statistical features extracted directly from acceleration time series have gained prominence, particularly when combined with Machine Learning (ML) classifiers. Although several studies have successfully employed statistical indicators for damage detection, the relative relevance of these indicators and the potential benefits of dimensionality reduction can still be explored through a systematic evaluation, particularly under Sim-to-Real conditions. This study investigates the performance of ten statistical indicators as input features for ML-based damage detection, applied to an experimentally tested frame structure, using numerically obtained data for training. A structured variable analysis was conducted to assess the individual and combined impact of the indicators, supported by visualization techniques and classifier performance metrics. The results showed some possible redundancies between certain indicators and demonstrated that a reduced set of attributes can achieve comparable or superior classification performance, while simultaneously improving training stability and computational efficiency. These findings highlight the importance of systematic feature selection and the effectiveness of reduced-dimensional statistical representations for SHM applications in Sim-to-Real scenarios.
Victor Carvalho, M. Marcy, G. Doz· Journal of Civil Structural...· 0 citations
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