Description:001] The present invention generally relates to the field of structural health monitoring (SHM) systems. More particularly, the invention pertains to a real-time, intelligent, and automated method and system for monitoring the structural integrity of bridge infrastructure using vibration signal analysis. The invention incorporates advanced artificial intelligence (AI) algorithms to detect, classify, and predict structural anomalies, thereby enabling early warning and predictive maintenance capabilities. This invention finds application in civil infrastructure management, particularly in enhancing the safety and longevity of bridge structures through continuous monitoring and data-driven diagnostics.
BACKGROUND OF THE INVENTION
[002] Bridges are among the most vital components of civil infrastructure, facilitating transportation and connectivity across geographic obstacles such as rivers, valleys, and highways. Due to their continuous exposure to environmental elements, vehicular load, seismic activity, and material aging, bridges are highly susceptible to various forms of structural degradation over time. The safety, reliability, and serviceability of these structures are of paramount importance, and any unexpected failure can lead to catastrophic consequences, including loss of life and substantial economic disruption.
[003] Traditional methods of structural health monitoring (SHM) typically rely on periodic manual inspections conducted by trained engineers. These inspections are often labor-intensive, subjective in nature, and limited in frequency due to resource constraints. As a result, critical damage can go undetected between inspection intervals, leading to unforeseen failures or the need for costly emergency repairs. Moreover, manual inspections may not be able to capture subtle or evolving anomalies, particularly in large or complex bridge systems.
[004] To overcome the limitations of manual inspection, vibration-based monitoring techniques have emerged as a promising alternative for assessing the structural integrity of bridges. These techniques analyze changes in the vibration characteristics of a structure—such as natural frequencies, mode shapes, and damping ratios—to detect the presence of damage. However, traditional vibration analysis techniques often require manual interpretation by experts and lack the ability to provide real-time diagnostics or long-term trend analysis.
[005] Existing SHM systems that incorporate vibration sensors often operate on static thresholds or basic signal processing techniques, which are insufficient for handling the complexity and variability of real-world structural behavior. These systems may generate false alarms or fail to detect early-stage damage due to their limited analytical capability. Additionally, they lack the adaptability to different bridge types, environmental conditions, and load scenarios, making them less effective for widespread deployment.
[006] With advancements in computational technologies and machine learning, artificial intelligence (AI) has shown significant potential in automating complex pattern recognition tasks. In the context of SHM, AI can be employed to learn from large volumes of vibration data, detect anomalies, and identify damage patterns with greater accuracy and speed than conventional methods. Machine learning models, including deep learning techniques such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, can be trained to recognize both subtle and significant deviations in structural behavior.
[007] Despite the availability of AI technologies, very few SHM systems for bridges have successfully integrated real-time AI-based vibration analysis with practical deployment. Many existing solutions are either purely theoretical or require extensive computational infrastructure that is not feasible for field applications. There is a critical need for a scalable, reliable, and autonomous AI-based SHM system that can continuously monitor bridge structures, analyze vibration signals in real time, and provide actionable insights to bridge maintenance authorities.
[008] Furthermore, current SHM solutions often lack an integrated user interface or data visualization platform, making it difficult for operators and engineers to interpret the diagnostic output effectively. The absence of intelligent alert systems, predictive maintenance features, and long-term historical analysis further limits their utility. Without these capabilities, infrastructure operators are unable to make informed decisions about maintenance prioritization or to optimize resource allocation.
[009] There is also a growing interest in integrating such intelligent SHM systems with cloud and edge computing architectures. By leveraging edge computing, preliminary data processing and anomaly detection can be performed locally, reducing latency and bandwidth requirements. Simultaneously, cloud platforms can provide centralized data storage, advanced analytics, and remote access capabilities, enabling a comprehensive and scalable SHM solution.
[010] Therefore, there is a pressing need for a novel AI-based structural health monitoring system for bridges that utilizes real-time vibration signal analysis. Such a system should incorporate a robust network of sensors, edge and cloud computing infrastructure, machine learning-based anomaly detection, and an intuitive user interface for seamless diagnostics and reporting. The present invention addresses these needs by providing an intelligent and adaptive SHM platform capable of transforming bridge monitoring from a reactive to a predictive process.
[011] The present invention not only addresses the limitations of current SHM systems but also introduces a proactive, intelligent, and fully automated monitoring solution. It enables early detection of potential structural failures, thereby improving safety, reducing lifecycle costs, and extending the operational lifespan of bridge infrastructure. This invention represents a significant advancement in the field of smart civil infrastructure management.
SUMMARY OF THE INVENTION
[012] The present invention provides an advanced structural health monitoring (SHM) system for bridges, utilizing artificial intelligence (AI) algorithms and vibration signal analysis to detect, diagnose, and predict structural anomalies in real time. The system is designed to enhance the safety, reliability, and service life of bridge infrastructure by continuously monitoring vibration signals and identifying potential damage at an early stage.
[013] The invention comprises a distributed network of vibration sensors strategically mounted on critical sections of a bridge to collect real-time dynamic response data. These sensors capture a wide range of vibration characteristics, including acceleration, velocity, and displacement. The collected data is pre-processed at the edge level using local computational units, which perform initial filtering, normalization, and feature extraction to reduce latency and bandwidth consumption.
[014] The processed vibration data is transmitted to a centralized AI engine, either hosted on the cloud or integrated into an on-site computing hub. The AI engine is configured with machine learning models, including deep learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are trained to detect changes in the structural behavior of the bridge. These models analyze temporal and frequency-domain features to classify potential damage types and assess severity levels.
[015] In addition to real-time detection, the system incorporates a predictive analytics module capable of forecasting future structural degradation based on historical trends and continuous learning. This module employs time-series analysis and long short-term memory (LSTM) models to generate early warnings for maintenance planning and risk mitigation.
[016] A key aspect of the invention is its adaptive learning capability. The system continuously improves its diagnostic accuracy by learning from new data over time, thereby refining its understanding of normal and abnormal structural states under varying environmental and operational conditions. This ensures the system remains robust and effective across different bridge designs and usage patterns.
[017] The SHM platform is further integrated with a user-friendly dashboard that provides maintenance engineers and infrastructure authorities with real-time insights, alerts, and historical analytics. The dashboard enables visualization of structural health indices, vibration signal trends, and detected anomalies, facilitating quick decision-making and prioritization of maintenance tasks.
[018] Moreover, the system is designed to be modular, scalable, and compatible with existing bridge infrastructure. It supports remote configuration, over-the-air updates, and seamless integration with other smart infrastructure systems through standardized communication protocols.
BRIEF DESCRIPTION OF THE DRAWINGS
[019] The accompanying figures included herein, and which form parts of the present invention, illustrate embodiments of the present invention, and work together with the present invention to illustrate the principles of the invention Figures:
[020] Figure 1, is a block diagram illustrating the overall architecture of the AI-based structural health monitoring system for bridges. It depicts the integration of vibration sensors, edge processing units, AI-based analysis engine, cloud infrastructure, and user interface components.
[021] Figure 2, is a flowchart showing the step-by-step process of data acquisition, preprocessing, AI-based analysis, anomaly detection, and reporting within the structural health monitoring platform. It outlines the operational workflow from vibration signal capture to maintenance alert generation.
DETAILED DESCRIPTION OF THE INVENTION
[022] The present invention provides an AI-based structural health monitoring (SHM) platform designed specifically for bridge infrastructure. The system leverages real-time vibration signal analysis, artificial intelligence, edge and cloud computing, and intelligent visualization tools to ensure reliable, scalable, and automated detection of structural anomalies.
[023] Referring to FIG. 1, the system architecture comprises multiple components including vibration sensor nodes (101), edge computing units (102), a central AI analysis engine (103), a cloud storage and analytics platform (104), and a user interface dashboard (105). These components work in unison to monitor and analyze the structural condition of bridges in real-time.
[024] The vibration sensor nodes (101) are strategically installed on critical locations of the bridge structure, such as joints, supports, beams, and decks. These sensors continuously collect data on structural vibrations including acceleration, velocity, and displacement in multiple axes. The sensors may include MEMS-based accelerometers, piezoelectric sensors, or other high-sensitivity devices suitable for long-term deployment in harsh outdoor environments.
[025] The collected vibration data is transmitted to local edge computing units (102) that perform preprocessing tasks. These tasks include signal denoising, normalization, fast Fourier transform (FFT), short-time Fourier transform (STFT), and extraction of relevant features such as natural frequencies, damping ratios, and mode shapes. The edge processors reduce data volume and enhance signal quality before forwarding it for AI analysis.
[026] The AI analysis engine (103) receives the preprocessed data and applies advanced machine learning models to identify structural anomalies. These models include convolutional neural networks (CNNs) for feature extraction from time-series and frequency-domain data, recurrent neural networks (RNNs) or long short-term memory (LSTM) networks for temporal pattern analysis, and anomaly detection models such as autoencoders or support vector machines (SVMs).
[027] The AI engine is trained using historical and simulated datasets representing both healthy and damaged bridge states. During live operation, the AI models compare incoming data with learned patterns to detect deviations indicative of potential structural issues such as cracks, loosened joints, corrosion, or fatigue. The detection results include severity scoring and damage localization.
[028] Referring now to FIG. 2, the system's operational workflow begins with continuous vibration signal acquisition (201), followed by signal preprocessing (202) at the edge unit. The preprocessed data is then sent to the AI engine for pattern recognition and anomaly detection (203). Upon detecting a potential issue, the system generates alerts and diagnostic reports (204) and transmits them to the dashboard. Simultaneously, all data and results are stored in the cloud database (205) for long-term analysis and future model training.
[029] The cloud platform (104) provides centralized storage for historical data, model management, and additional analytics such as trend detection and degradation forecasting. It also supports over-the-air updates of AI models and firmware, ensuring that the system continuously evolves and adapts to changing structural conditions.
[030] The user interface dashboard (105) provides real-time visualization of key metrics including structural health index (SHI), vibration signal plots, anomaly alerts, and historical trends. It allows bridge operators and civil engineers to make informed decisions regarding inspection schedules, maintenance planning, and load management. The dashboard is accessible through web and mobile interfaces and includes customizable alerts, threshold settings, and downloadable reports.
[031] The system is designed to be modular and scalable. Multiple bridges within a region can be connected to a centralized monitoring center, enabling regional infrastructure management. The modular nature allows for easy integration with additional sensors, power supplies (solar, battery), communication protocols (LoRa, LTE, Wi-Fi), and third-party asset management systems.
[032] Importantly, the AI models are continuously refined using both supervised and unsupervised learning mechanisms. In supervised mode, feedback from human inspectors is used to validate model predictions and improve classification accuracy. In unsupervised mode, the system clusters new vibration patterns and flags unknown conditions for manual review, thus enabling adaptive learning without the need for labeled data in every scenario.
[033] The invention thus offers a complete, autonomous, and intelligent structural health monitoring solution that significantly improves upon the limitations of traditional SHM techniques. It ensures early detection of structural issues, supports predictive maintenance strategies, and contributes to the safety, reliability, and cost-efficiency of bridge infrastructure over its lifecycle.
[034] In conclusion, the present invention introduces an AI-based structural health monitoring (SHM) platform that integrates advanced vibration signal analysis, machine learning algorithms, and intelligent data visualization to offer a robust and real-time solution for monitoring the integrity of bridge infrastructure. By enabling early detection of structural anomalies, the invention shifts maintenance strategies from reactive to predictive, significantly reducing risks associated with undetected structural failures, extending the lifespan of bridges, and optimizing maintenance resource allocation.
[035] The integration of edge computing and cloud-based analytics ensures scalability and efficient deployment across diverse geographic locations and bridge types. The system’s adaptability to various environmental conditions, dynamic loads, and structural configurations makes it suitable for both new and existing bridges. Furthermore, the modularity of the platform allows for easy integration with existing infrastructure and future smart city systems, making it a forward-compatible solution.
[036] Looking ahead, future enhancements of the system could involve the incorporation of additional sensing modalities such as strain gauges, temperature sensors, acoustic emission sensors, and LiDAR systems to provide a more holistic assessment of bridge health. Integration with unmanned aerial vehicles (UAVs) and robotic inspection units could further automate data collection and visual inspection processes, enhancing system coverage and reducing manual intervention.
[037] On the software front, advancements in federated learning and edge AI could enable decentralized model training, thereby enhancing privacy and reducing reliance on centralized data transmission. Additionally, the integration of blockchain technology for secure and immutable storage of inspection and health records could further bolster the system’s reliability and trustworthiness for regulatory and legal purposes.
[038] Overall, the proposed AI-based structural health monitoring platform addresses the critical need for intelligent, automated, and scalable infrastructure management tools. It lays the groundwork for smarter, safer, and more sustainable transportation networks and presents significant potential for future innovation and adoption across civil infrastructure domains worldwide.
, Claims:1. An AI-based structural health monitoring system for bridges, comprising:
a plurality of vibration sensor nodes configured to collect real-time vibration data from structural elements of a bridge;
one or more edge computing units electrically coupled to the vibration sensor nodes and configured to preprocess the vibration data; and
an AI analysis engine in communication with the edge computing units, the AI analysis engine comprising machine learning models configured to detect and classify structural anomalies based on the preprocessed vibration data.
2. The system of claim 1, wherein the vibration sensor nodes comprise at least one of a MEMS accelerometer, a piezoelectric sensor, or a geophone.
3. The system of claim 1, wherein the preprocessing performed by the edge computing units comprises signal denoising, normalization, and feature extraction including at least one of Fast Fourier Transform (FFT) or Short-Time Fourier Transform (STFT).
4. The system of claim 1, wherein the AI analysis engine comprises at least one of a convolutional neural network (CNN), a long short-term memory (LSTM) network, or an autoencoder model.
5. The system of claim 1, further comprising a cloud-based platform configured to store historical vibration data, retrain the machine learning models, and provide predictive maintenance analytics.
6. The system of claim 5, wherein the cloud-based platform provides a user interface dashboard displaying structural health indices, vibration signal trends, anomaly alerts, and maintenance recommendations.
7. The system of claim 1, wherein the machine learning models are trained using supervised learning on labeled datasets of healthy and damaged bridge states.
8. The system of claim 1, wherein the machine learning models further employ unsupervised anomaly detection to identify novel or evolving damage patterns without requiring labeled data.
9. A method for monitoring the structural health of a bridge, comprising:
(a) collecting vibration signals from the bridge via a network of vibration sensor nodes;
(b) preprocessing the collected vibration signals at an edge computing unit to extract relevant features;
(c) transmitting the preprocessed features to an AI analysis engine;
(d) analyzing the preprocessed features with machine learning models to detect and classify structural anomalies; and
(e) generating and outputting an alert when the detected anomaly exceeds a predefined severity threshold.
10. The method of claim 9, further comprising using historical vibration data and time-series forecasting models to predict future structural degradation and provide proactive maintenance recommendations.