Research article, Type: Subscription, Pages: 01-17;

Received: 10 November 2025 / Revised: 16 December 2025 / Accepted:  12 January 2026 / Published on: 04 Febuary 2026

Title: Enhancing Infrastructure Resilience Through Digital Twin Life-Cycle Monitoring

Authors: Jan Holnicki-Szulc, Mohammed Nijr Dughaylib Alotaibi, Mana Aziz Awadh Alharbi, Naif Hiji Alrasheedi & Abdulrahim Owaidh Saud Aloufi

Abstract: Digital twin technology creates a virtual model of physical infrastructure (like bridges, roads, or water systems) that can be continuously updated with real-time data to simulate performance and predict maintenance needs. By combining sensors, IoT data, and advanced analytics, digital twins enable engineers to foresee structural issues before they become critical, reducing downtime and life-cycle costs. Research can explore the challenges of integrating diverse sensor data, optimizing predictive models, and ensuring reliability over long service lives. This topic is timely due to increasing digitalization in infrastructure management and the need for proactive rather than reactive maintenance strategies. Researchers can also evaluate how digital twins perform under different environmental conditions or across infrastructure types. Digital twin studies can contribute to safer, more resilient and cost-efficient infrastructure systems. …… [For more click here]

Keywords: Digital Twin Technology, Infrastructure Resilience, Structural Health Monitoring (SHM), Life-Cycle Management, Predictive Maintenance

 

Research article, Type: Subscription, Pages: 18-35;

Received: 04 Octuber 2025 / Revised: 28 December 2025 / Accepted:  15 January 2026 / Published on: 07 Febuary 2026

Title: Microstructural Design and Mechanical Behavior of Lightweight Bio-Composites in Structural Applications

Authors: Avinash Kumar, katrin Wieneke, S. Shao & D. Carsten

Abstract: With rising concerns about climate change, developing and optimizing sustainable materials (such as geopolymer concrete, recycled aggregates, bio-based composites, or bamboo composites) is a major research priority. These materials aim to reduce the carbon footprint of construction compared to traditional Portland cement and steel, which are energy-intensive. Researchers can investigate mechanical properties, durability, cost-effectiveness, and life-cycle environmental impacts of these alternatives. Topics can include the use of industrial by-products like fly ash or slag for greener concrete or evaluating self-healing materials that extend infrastructure life. Understanding barriers to practical adoption (such as regulatory, performance, or supply challenges) is also critical. Sustainable materials research directly supports global climate action and green construction goals. …… [For more click here]

Keywords: Lightweight Bio-Composites, Microstructural Tailoring, Mechanical Performance, Natural Fiber Reinforcement, Load-Bearing Applications

 

Research article, Type: Subscription, Pages: 36-60;

Received: 22 November 2025 / Revised: 31 December 2025 / Accepted:  19 January 2026 / Published on: 10 Febuary 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: 61-84;

Received: 14 November 2025 / Revised: 17 December 2025 / Accepted:  04 January 2026 / Published on: 14 Febuary 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: 85-96;

Received: 03 November 2025 / Revised: 22 December 2025 / Accepted:  29 January 2026 / Published on: 17 Febuary 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 & Abrha Gebregerg

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

 

Research article, Type: Subscription, Pages: 97-111;

Received: 13 November 2025 / Revised: 06 December 2025 / Accepted:  02 Febuary 2026 / Published on: 21 Febuary 2026

Title: Robotics and machine learning for adaptive disaster recovery in geotechnical and structural systems

Authors: Akshay Sharma, Sunil Kumar, Rubina khyat & Nannette C. Auerhahan

Abstract: Robotics and machine learning enable adaptive disaster recovery in geotechnical and structural systems by deploying autonomous robots equipped with sensors to navigate unstable terrains, assess soil liquefaction, and map debris in real-time post-earthquake or flood scenarios. Machine learning algorithms, such as convolutional neural networks, process live data from LiDAR and cameras to classify damage types—like foundation settlements or beam failures—prioritizing recovery efforts and reducing human exposure to hazards. Swarm robotics coordination, enhanced by reinforcement learning, allows multiple units to collaborate on tasks like soil stabilization or temporary shoring, adapting to dynamic conditions such as aftershocks. In structural applications, these systems integrate with BIM models for predictive simulations, optimizing geotechnical reinforcements like deep foundations while cutting recovery timelines by 30-50%. This interdisciplinary approach aligns with resilient infrastructure goals, merging civil engineering’s stability analyses with computer engineering’s adaptive AI for faster, safer post-disaster rebuilding….…… [For more click here]

Keywords: Autonomous Disaster Response Robotics, Machine Learning-Based Damage Assessment, Adaptive Infrastructure Recovery, Geotechnical and Structural Resilience

 

Research article, Type: Subscription, Pages: 112-124;

Received: 23 Octuber 2025 / Revised: 15 December 2025 / Accepted:  08 Febuary 2026 / Published on: 24 Febuary 2026

Title: Quantum computing applications in optimizing renewable energy integration for sustainable Authors: Tihamer Bako & Mukesh Rastogi

Abstract: Quantum computing applications in optimizing renewable energy integration for sustainable civil projects leverage quantum annealing and algorithms to solve complex grid optimization problems that classical computers struggle with, such as balancing variable solar and wind inputs. These systems model intricate energy flows, weather patterns, and storage dynamics in real-time, enabling precise partitioning of power grids to minimize losses and enhance stability during peak demands or fluctuations. In civil engineering contexts, they optimize layouts for solar integrated smart buildings or wind-resilient offshore foundations, reducing costs by 20-25% while supporting carbon-neutral urban infrastructure. Collaborations like D-Wave with E.ON demonstrate practical gains, including better renewable forecasting and demand-response strategies that cut reliance on fossil backups. Overall, this technology accelerates sustainable transitions by simulating material behaviors for energy-efficient structures, aligning civil designs with quantum-enhanced energy systems. ……… [For more click here]

Keywords: Quantum Optimization Algorithms, Renewable Energy Integration, Smart Grid Management, Sustainable Power Systems, Energy Scheduling and Dispatch

 

Research article, Type: Subscription, Pages: 125-136;

Received: 23 Octuber 2025 / Revised: 15 December 2025 / Accepted:  12 Febuary 2026 / Published on: 27 Febuary 2026

Title: Biomimetic materials with embedded sensors for self-healing infrastructure in harsh environments Authors: Sunit Sharma, Pawan Jhajhar, Sandip Singla, Kranti Thakur & Katalin Zara

Abstract: Biomimetic materials with embedded sensors for self-healing infrastructure mimic biological systems like human skin or tree bark, using vascular networks or microcapsules filled with healing agents such as polymers or bacteria-induced calcite to autonomously repair cracks in concrete or steel under harsh conditions like extreme temperatures or seismic stress. Embedded piezoelectric or fiber-optic sensors continuously monitor strain, pH changes, or moisture levels, triggering agent release when damage exceeds thresholds, thus extending infrastructure lifespan by 30-50% in corrosive marine or desert environments. Recent advances incorporate Bacillus subtilis bacteria immobilized in natural fibers like sisal, which survive harsh exposures and precipitate minerals for repeated healing cycles up to 0.5 mm cracks. These smart composites integrate with IoT for real-time data feedback, enabling predictive maintenance in bridges or offshore platforms while reducing carbon footprints through minimized repairs. This interdisciplinary fusion of materials science, civil engineering, and sensor tech supports resilient designs for climate-vulnerable regions like Bihar’s flood zones….…… [For more click here]

Keywords: Biomimetic Self-Healing Materials, Embedded Sensor Networks, Autonomous Damage Repair, Infrastructure Resilience, Extreme Environmental Durability

 

Research article, Type: Subscription, Pages: 137-154;

Received: 01 November 2025 / Revised: 28 December 2025 / Accepted:  02 Febuary 2026 / Published on: 27 Febuary 2026

Title: Edge AI and IoT for predictive maintenance in hybrid electric vehicle infrastructure and smart grids Authors: Bantu Joseph, Ajit Kumar & Agnes Szaicx Daw

Abstract: Edge AI and IoT enable predictive maintenance in hybrid electric vehicle (HEV) infrastructure and smart grids by processing sensor data—like battery voltage, motor vibrations, and grid load—at the network edge for instantaneous anomaly detection without cloud latency. In HEVs, embedded IoT devices monitor thermal runaway risks or regenerative braking wear, using lightweight ML models to forecast failures up to 48 hours ahead, slashing downtime by 30-50% and extending component life. For smart grids, edge nodes analyze transformer health and line faults in real-time, integrating with SCADA for dynamic load balancing that prevents outages during peak renewable surges. This setup supports civil infrastructure by optimizing EV charging stations and grid-tied solar arrays, reducing maintenance costs by 20-35% through condition-based scheduling. Overall, it fosters resilient urban energy systems, aligning with sustainable transport goals in high density areas like Paris. . …… [For more click here]

Keywords: Edge Artificial Intelligence (Edge AI), Internet of Things (IoT), Predictive Maintenance, Hybrid Electric Vehicle (HEV) Infrastructure, Smart Grid Integration