{"id":35965,"date":"2025-01-01T04:43:48","date_gmt":"2025-01-01T04:43:48","guid":{"rendered":"https:\/\/www.goodacademic.com\/blog\/questions\/multimodal-explainable-ai-framework-for-real-time-structural-health-monitoring-and-predictive-maintenance-in-critical-infrastructure-abstract-this-research-introduces-an-ai-driven-framework-for-ea\/"},"modified":"2025-01-01T04:43:48","modified_gmt":"2025-01-01T04:43:48","slug":"multimodal-explainable-ai-framework-for-real-time-structural-health-monitoring-and-predictive-maintenance-in-critical-infrastructure-abstract-this-research-introduces-an-ai-driven-framework-for-ea","status":"publish","type":"questions","link":"https:\/\/www.goodacademic.com\/blog\/questions\/multimodal-explainable-ai-framework-for-real-time-structural-health-monitoring-and-predictive-maintenance-in-critical-infrastructure-abstract-this-research-introduces-an-ai-driven-framework-for-ea\/","title":{"rendered":"&#8220;Multimodal Explainable AI Framework for Real-Time Structural Health Monitoring and Predictive Maintenance in Critical Infrastructure&#8221;  Abstract: This research introduces an AI-driven framework for early detection and prediction of structural failures"},"content":{"rendered":"<p>Abstract: This research introduces an AI-driven framework for early detection and prediction of structural failures in critical infrastructure such as buildings and bridges. Leveraging multimodal data fusion from vibration sensors, thermal imaging, and high-resolution visual inputs, the system offers real-time anomaly detection with actionable insights. Explainable AI (XAI) methodologies ensure transparent decision-making through visual heatmaps and interpretable outputs. Edge-based deployment enables seamless monitoring in low-connectivity environments. The study emphasizes predictive maintenance, providing actionable recommendations for repairs, enhancing safety, and minimizing catastrophic risks while ensuring cost-effectiveness. Key Contributions: Multimodal Data Fusion: Integration of visual, thermal, and vibration data for comprehensive structural analysis. Enhanced robustness through attention mechanisms for feature weighting. Real-Time Detection and Explainability: Anomaly detection with XAI-based insights highlighting high-risk regions using visual heatmaps. Integration of interpretable models like LIME or SHAP for real-time explanations. Predictive Maintenance: Forecasting degradation timelines to prioritize repairs, reducing long-term costs and risks. Scheduling maintenance actions based on severity levels. Edge Computing Deployment: Lightweight AI models optimized for resource-constrained environments. Ensures reliability in remote areas with minimal connectivity. Significance: This framework addresses the limitations of existing systems by combining multimodal data analysis with XAI for greater reliability and trust. The edge-based solution ensures scalability and accessibility in rural or underdeveloped regions, offering a transformative approach to infrastructure safety.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: This research introduces an AI-driven framework for early detection and prediction of structural failures in critical infrastructure such as buildings and bridges. Leveraging multimodal data fusion from vibration sensors, thermal imaging, and high-resolution visual inputs, the system offers real-time anomaly detection with actionable insights. Explainable AI (XAI) methodologies ensure transparent decision-making through visual heatmaps [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","meta":[],"disciplines":[211],"paper_types":[],"tagged":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/questions\/35965"}],"collection":[{"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/questions"}],"about":[{"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/types\/questions"}],"author":[{"embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/comments?post=35965"}],"version-history":[{"count":0,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/questions\/35965\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/media?parent=35965"}],"wp:term":[{"taxonomy":"disciplines","embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/disciplines?post=35965"},{"taxonomy":"paper_types","embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/paper_types?post=35965"},{"taxonomy":"tagged","embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/tagged?post=35965"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}