Research article, Type: Subscription; Page: 31-51;

Received: 31 March 2026 / Revised: 16 May 2026 / Accepted: 18 June 2026 / Published: 20 July 2026

Title: Autonomous Structural Health Monitoring of Aging Bridges Using UAV Swarms and Deep Learning

Authors: Nilesh Choudhary & Asish Thakur

Abstract: Aging transportation infrastructure requires frequent, high-precision inspections to prevent catastrophic failures. Traditional manual inspection methods are labor-intensive, subjective, and dangerous for personnel. This research presents an automated framework for structural health monitoring (SHM) using a cooperative swarm of Unmanned Aerial Vehicles (UAVs) integrated with deep learning algorithms. The proposed system deploys multiple small UAVs equipped with high-resolution optical and thermal cameras to dynamically map concrete bridge structures. A decentralized flocking algorithm ensures collision-free, optimal coverage of the bridge components. The captured imagery is processed in real-time using a customized Convolutional Neural Network (CNN) optimized for pixel-level segmentation of structural defects, including fatigue cracks, concrete spalling, and rebar corrosion. Field testing on a decommissioned reinforced concrete girder bridge demonstrates that the UAV swarm reduces inspection time by 65% compared to single-drone operations. The deep learning model achieves a 94.2% Intersection over Union (IoU) score in detecting sub-millimeter surface cracks…………….. [For more click here]

Keywords: Structural Health Monitoring, UAV swarms, Deep learning, Crack segmentation, Bridge inspection, Computer vision

Research article, Type: Subscription, Pages: 52-64;

Received: 22 March 2026 / Revised: 25 June 2026 / Accepted:  09 July 2026 / Published : 21 July 2026

Title: Machine Learning-Driven Damage Detection in Aerospace Composites: A Structural Health Monitoring Approach

Authors: Abdulwahab Owaidh Saud Aloufi, Eisi Ghanem Aljohani, Abdulmajeed Aouidh Alaofi & Amani Abdulmunaem Alhaisoni

Abstract: The application of AI and machine learning in assessing the condition of structures is rapidly expanding, moving beyond traditional methods to data-driven approaches that can identify damage earlier and more accurately. This topic involves using sensor data, vibration records, and visual inputs (e.g., from drones or cameras) to train predictive models for detecting faults like cracks, corrosion, or deformation. A key research area is domain adaptation — teaching models trained on one type of structure or environment to work reliably on others with different conditions. Researchers can explore how to make these models more interpretable, trustworthy, and generalizable for field use. Such work enhances infrastructure safety and reduces the need for costly manual inspections. Integrating physical modeling with AI is a cutting-edge challenge in this field……….. [For more click here]

Keywords: Structural Health Monitoring (SHM), Machine Learning Algorithms, Aerospace Composites, Damage Identification, Anomaly Detection

Research article, Type: Subscription, Pages: 65-81;

Received: 14 January 2065 / Revised: 17 March 2026 / Accepted:  04 July 2026 / Published : 22 July 2026

Title: Vulnerability Assessment and Adaptive Design of Aerospace Facilities Subjected to Extreme Thermal Loads

Authors: Auhedur Rahman & Ismoth Zerine

Abstract: Climate change is increasing the frequency and severity of extreme weather events, making it essential for civil engineers to design infrastructure that withstands floods, heatwaves, hurricanes, and rising sea levels. Research can focus on adaptive structural design methods, such as elevated transportation corridors, flood-resilient bridges, or permeable pavement systems that reduce stormwater runoff. Another angle is modeling future climate scenarios and integrating them into design standards and safety margins. Researchers can also investigate materials and structural systems that maintain functionality after extreme loads or require minimal repair. This topic is vital for ensuring infrastructure longevity and protecting communities from climate risks………….. [For more click here]

Keywords: Extreme Thermal Loading, Aerospace Infrastructure, Vulnerability Assessment, Adaptive Structural Design, Thermal Resilience

Research article, Type: Subscription, Pages: 82-96;

Received: 03 April 2026 / Revised: 12 June 2026 / Accepted:  29 June 2026 / Published : 22 July 2026

Title: Real-Time Structural Health and Traffic Flow Optimization: An AI-Driven Digital Twin Approach to Smart Infrastructure

Authors: Arohi Sinha, Rohit Kashyap, Ankit Bhardwaj & Abrham Gebrag

Abstract: AI-driven digital twins for real-time optimization of smart city infrastructure fuse civil engineering’s physical designs with computer engineering’s data analytics to create virtual replicas of urban systems like roads, buildings, and utilities. These models ingest live IoT data on traffic, energy use, and environmental conditions, employing AI algorithms to simulate scenarios, predict failures, and recommend adjustments such as dynamic signal timing or grid load balancing. In practice, they enable civil engineers to test seismic retrofits or flood defenses virtually, while computer engineers optimize the underlying machine learning for low-latency processing, cutting operational costs by 20-30% in cities like Copenhagen. This interdisciplinary synergy supports sustainable growth by integrating BIM models with edge computing, enhancing resilience against disasters in densely populated areas. Ultimately, such systems empower planners to achieve carbon neutral outcomes through continuous feedback loops between physical assets and digital simulations.…………. [For more click here]

Keywords: Digital Twin Technology, Structural Health Monitoring (SHM), Traffic Flow Optimization, Artificial Intelligence (AI), Smart Infrastructure