Research Article, Type: Subscription; Pages: 01-13;
Title: Generative design using AI for resilient modular construction in seismic zones
Authors: Komal Nevase, Muhammad Zainul Abidin & Azrina Husin
Abstract: Generative design using AI for resilient modular construction in seismic zones employs algorithms that iteratively explore thousands of structural configurations, optimizing for factors like material efficiency, damping ratios, and drift limits to produce prefabricated modules that assemble rapidly on-site. These AI tools integrate seismic hazard data, soil-structure interaction models, and performance-based criteria to generate lightweight frames with energy-dissipating joints, outperforming traditional designs by 20-40% in ductility and cost. In practice, platforms like those from Autodesk or custom neural networks train on historical quake data to simulate nonlinear behaviors, enabling modules for high-rises that self-align during assembly while minimizing residual deformations. This approach accelerates deployment in vulnerable regions like Bihar, reducing construction timelines by half and enhancing post-event reusability. Ultimately, it bridges civil engineering’s resilience standards with computer science’s optimization power for scalable, adaptive urban rebuilding………….[For more click here]
Keywords: Generative Design, Modular Construction, Seismic Resilience, Artificial Intelligence, Structural Optimization
Research Article, Type: Subscription; Pages: 14-29;
Title: AR/VR simulations integrated with BIM for multidisciplinary training in advanced manufacturing
Authors: Ruth Ronen, Narayanan Ganapathy, Vanita Kaneson, Samantha Sim & M. Z. Cohen
Abstract: AR/VR simulations integrated with BIM for multidisciplinary training in advanced manufacturing create immersive 3D environments where civil, mechanical, and manufacturing engineers collaborate on virtual prototypes, visualizing complex assemblies like prefabricated modules or robotic arms directly from BIM models. Trainees interact with digital twins via headsets, practicing tasks such as clash detection, assembly sequencing, or hazard avoidance in simulated factories, boosting retention by 75% over traditional methods through hands-on repetition without physical risks. This setup supports real-time multiplayer sessions, enabling cross-disciplinary teams to annotate models, test tolerances, and refine designs—such as seismic joints in modular units—before production. Platforms like Autodesk Forge automate BIM-to-VR conversion, while AR overlays guide on-site workers with contextual instructions, cutting training time by 40% and errors in high-precision manufacturing. Overall, it fosters safer, faster skill transfer for Industry 4.0, aligning with sustainable construction workflows in seismic regions…..…….[For more click here]
Keywords: BIM (Building Information Modeling), AR/VR Simulations, Multidisciplinary Training, Advanced Manufacturing, Mixed Reality (MR)
Research Article, Type: Subscription; Pages: 30-46;
Title: Machine learning for climate-adaptive urban planning, fusing environmental and transportation engineering
Authors: Fangzhou Wang, Weiping Pei, Senthamizh Selvi R & Amani Alhaisoni
Abstract: Machine learning drives climate-adaptive urban planning by fusing environmental data, such as temperature and flood predictions, with transportation metrics like traffic density to optimize resilient infrastructure designs. Hybrid models integrate real-time IoT sensors from roads and weather stations, using techniques like LSTMs for forecasting congestion amid rising sea levels or heatwaves. This approach enables dynamic signal adjustments and green corridor planning, reducing emissions by up to 25% while enhancing stormwater management in dense cities. Digital twins powered by AI simulate fused scenarios, allowing planners to test adaptive measures like permeable pavements against climate extremes, aligning with disaster recovery robotics. Ultimately, such interdisciplinary fusion supports sustainable urban growth, cutting operational costs and building equity for vulnerable areas like Delhi’s flood-prone zones………….[For more click here]
Keywords: Machine Learning, Climate-Adaptive Urban Planning, Environmental Engineering, Transportation Engineering, Multi-objective Optimization
Research Article, Type: Subscription; Pages: 47-59;
Title: Nanotechnology-enhanced smart materials for energy-efficient buildings and microgrid systems
Authors: Katalin Zara, Wan Huang, Xiaojin Chen, Jia Qu, Xinting Wang & Chia-Hung Lin
Abstract: Nanotechnology-enhanced smart materials, such as nano-enhanced phase change materials (NEPCMs) with CuO nanoparticles, boost thermal conductivity by up to 60%, enabling buildings to store and release heat efficiently for 25-30% HVAC energy savings. Graphene-infused concrete strengthens structures while reducing carbon emissions, integrating seamlessly with self-healing sensors for resilient, energy-adaptive facades in climate-vulnerable areas. In microgrid systems, FeS2 nanoparticles in carbon nanofibers enhance lithium battery performance, providing stable storage for IoT-driven smart grids and renewables. MXene-based supercapacitors offer high capacitance for edge AI applications, optimizing power distribution in urban microgrids like Delhi’s high-density networks. These innovations fuse advanced materials with your interests in sustainable infrastructure, cutting global building energy use (36% of total) through superior thermal energy storage and lifecycle efficiency….…….[For more click here]
Keywords: Nanotechnology, Smart Materials, Energy-Efficient Buildings, Microgrid Systems, Phase Change Materials (PCMs)
Research Article, Type: Subscription; Pages: 60-74;
Title: Human-robot collaboration in Industry 4.0 for automated sustainable construction processes
Authors: Robert A. Paul, Abdul Ahad, Yvonne Karen Parry & Matthew Ankers
Abstract: Human-robot collaboration in Industry 4.0 transforms sustainable construction by pairing human decision-making with robotic precision for tasks like 3D-printed concrete assembly and modular prefabrication, reducing material waste by up to 30%. Cobots equipped with AI vision systems handle repetitive heavy lifting and rebar placement, allowing workers to focus on complex quality checks while minimizing injuries in dynamic sites. Real-time biosignal monitoring and VR training enhance safety and adaptability, enabling seamless task-sharing that cuts project timelines and boosts energy-efficient designs like passive solar integration. This synergy supports circular economy principles through automated deconstruction for material reuse, aligning with resilient infrastructure goals in seismic zones. Ultimately, HRC in Construction 5.0 fosters greener processes, with exoskeletons and drones optimizing workflows for carbon-neutral urban builds………...[For more click here]
Keywords: Human-Robot Collaboration (HRC), Industry 4.0, Automated Construction, Sustainable Construction, Cyber-Physical Systems (CPS)
Research Article, Type: Subscription; Pages: 75-88;
Title: AI-optimized seismic retrofitting for reinforced concrete high-rises using machine learning for performance prediction
Authors: Fraces Restucia Melony, Haemi Won, Joon Tag Cho, Haemi Won, Chunrye Kim & Riccardo Ferraresso
Abstract: AI-optimized seismic retrofitting for reinforced concrete high-rises employs machine learning models like neural networks to predict structural performance under various earthquake intensities, rapidly screening thousands of retrofit configurations such as fiber-reinforced polymer (FRP) jacketing or base isolation. These ML algorithms, trained on finite element simulations, approximate complex nonlinear responses with over 95% accuracy, slashing computational time from days to minutes for high-rise frames. By fusing sensor data from IoT-embedded buildings with generative AI, the approach identifies optimal retrofit zones, enhancing drift control and collapse prevention in seismic hotspots like Delhi. Performance predictions integrate probabilistic fragility curves, enabling cost-effective designs that extend residual life by 50+ years while minimizing material use for sustainability. This aligns with your resilient infrastructure focus, bridging ML-driven digital twins for real-time monitoring and adaptive retrofits in urban high-rises.……….[For more click here]
Keywords: AI-Optimized Retrofitting, Seismic Retrofitting, Reinforced Concrete (RC) High-Rises, Machine Learning Performance Prediction, Structural Health Monitoring (SHM)
Research Article, Type: Subscription; Pages: 89-101;
Title: Self-healing concrete with embedded IoT sensors for real-time damage detection in earthquake-prone structures
Authors: Narayanan Ganapathy, Rachyl Lim & Alfrad Brook
Abstract: Self-healing concrete embedded with IoT sensors revolutionizes earthquake-prone structures by autonomously repairing microcracks up to 0.5 mm wide using bacteria or polymers, while sensors detect strain and damage in real-time. Carbon fiber and graphite integration enables self-sensing properties, transmitting data via wireless networks to predict failures before visible deterioration, enhancing safety in high-rises. In seismic zones like Delhi, these systems monitor vibration and stress through accelerometers, triggering healing agents that recover 87% of compressive strength within 28 days. AI algorithms analyze IoT feeds for predictive maintenance, integrating with digital twins to optimize retrofits and reduce downtime by 40% post-quake. This aligns with your resilient infrastructure focus, merging advanced materials and edge AI for sustainable, self-monitoring urban builds.……….[For more click here]
Keywords: Self-Healing Concrete, Embedded IoT Sensors, Real-Time Damage Detection, Seismic Monitoring, Structural Health Monitoring (SHM)
Research Article, Type: Subscription; Pages: 102-115;
Title: Wind-induced vibration control in tall buildings via computer vision and drone-based monitoring
Authors: Shravanee Budgude, Eric G. Lambert, Hanif Qureshi, James Frank & Jessie Slepicka
Abstract: Wind-induced vibration control in tall buildings leverages computer vision and drone-based monitoring to track real-time structural displacements with millimeter accuracy, outperforming traditional accelerometers by capturing dynamic sway patterns across facades. Drones equipped with LiDAR and high-frame-rate cameras orbit structures during storms, feeding computer vision algorithms like optical flow analysis to predict vortex shedding frequencies and adjust active dampers preemptively. Machine learning models trained on drone footage optimize tuned mass dampers or morphing facades, reducing peak accelerations by 40-90% while minimizing occupant discomfort in high-rises. This approach integrates with IoT sensor networks for hybrid monitoring, enabling predictive maintenance that aligns with your self-healing concrete and seismic retrofitting interests. In Delhi’s windy urban corridors, such systems enhance resilience, cutting retrofit costs through data-driven vibration suppression………….[For more click here]
Keywords: Wind-Induced Vibration Control, Computer Vision (CV), Drone-Based Monitoring (UAV), Tall Buildings / Structural Dynamics
Research Article, Type: Subscription; Pages: 116-131;
Title: BIM-enhanced modular construction using robotics and predictive maintenance AI
Author: Rohit Choudhary, Shiv Kumar, Pawan Kumar & Andrea Murrey
Abstract: BIM-enhanced modular construction integrates robotics for precise off-site assembly of prefabricated modules, reducing construction time by up to 40% while minimizing onsite waste through clash-free 3D coordination. Predictive maintenance AI analyzes IoT sensor data from robotic arms and modules, forecasting equipment failures to prevent downtime and extend lifecycle in sustainable projects. This synergy enables generative design optimization within BIM platforms, where robots execute complex geometries for energy-efficient buildings, aligning with your advanced materials research. Real-time digital twins track assembly sequences, ensuring seamless human-robot collaboration and compliance with seismic codes in high-rise modular systems. Overall, the approach cuts costs by 20-30% and emissions through repeatable processes, ideal for resilient urban infrastructure in France….……….[For more click here]
Keywords: BIM-Robotics Integration, Automated Modular Construction, AI-Driven Predictive Maintenance, Digital Twin Technology, Industrial Internet of Things (IIoT)