Volume 87 Issue 04

Research Article, Type: Subscription; Pages: 01-14;

Received: 18 December 2025 / Revised: 25 February 2026 / Accepted: 18 March 2026 / Published : 02 April 2026

Title: Predictive Resilience: Integrating Machine Learning and Digital Twins for High-Rise Earthquake Mitigation

Authors: Avinash Kumar, Sanjeet Patel & Abhishek Bharti

Abstract: AI-driven seismic resilience in high-rise structures leverages machine learning models like artificial neural networks (ANN) and support vector machines (SVM) to predict structural responses under dynamic earthquake loads. These models integrate real-time data from accelerometers, strain gauges, and fiber optic sensors to forecast inter-story drifts and lateral displacements with RMSE values as low as 0.089. Genetic algorithms (GA) optimize outrigger system configurations, including position, stiffness, and damping, achieving 46.67% reductions in lateral displacement and 55.56% in drifts compared to conventional designs. Pushover and time-history analyses validate these optimizations, enhancing energy dissipation by 33.33% in earthquake-prone regions like Bihar. Hybrid frameworks combine supervised learning with reinforcement refinement for adaptive retrofitting, outperforming empirical codes by enabling proactive damper adjustments.………….[For more click here]

Keywords: AI-Driven Seismic Resilience, Outrigger System Optimization, Genetic Algorithms (GA)

Research Article, Type: Subscription; Pages: 15-31;

Received: 24 December 2025 / Revised: 12 February 2026 / Accepted: 22 March 2026 / Published : 02 April 2026

Title: Examine the properties of self-Healing Concrete with IoT for Blast Resistance

Authors: Adolf Kingal

Abstract: Self-healing concrete with IoT integration enhances blast resistance through autonomous crack repair mechanisms, primarily using bacteria-based (e.g., Bacillus species), microcapsule, or vascular systems that activate upon damage from shockwaves. Bacteria-induced healing relies on spores and nutrients like calcium lactate, which, triggered by water ingress post-blast, precipitate calcium carbonate to seal microcracks up to 200-300 µm within days to weeks, recovering 70-90% of mechanical strength. Microcapsule-based variants embed polymer shells (e.g., urea-formaldehyde with epoxy or polyurethane healing agents) that rupture under blast-induced strain, releasing agents for rapid closure (60-80% strength recovery in hours), with permeability reductions up to 99%. IoT sensors, such as embedded piezoelectric or fiber Bragg grating types, enable real-time monitoring of strain, pH, and crack propagation during and after blasts, transmitting data via wireless networks for predictive analytics. Blast resistance improves via reduced spalling and enhanced ductility; studies show self-healed samples exhibit 40-60% higher post-blast compressive strength retention compared to plain concrete under TNT equivalents…..…….[For more click here]

Keywords: Self-Healing Concrete, Blast Resistance, Autonomous Crack Repair, IoT Sensor Integration, Predictive Structural Analytics

Research Article, Type: Subscription; Pages: 32-45;

Received: 25 January 2026 / Revised: 23 February 2026 / Accepted: 01 March 2026 / Published : 07 April 2026

Title: Design and Analysis of an Earthquake Wave-Affected Stencil Platform with a Tuned Liquid Damper Using Finite Element Analysis

Authors: Sachin Kumar, Chandan Kumar, Abhishek Kumar, Amit Kumar Sinha, Shubhendu Amit, Anjali Sinha, Rahul Chatterjee

Abstract: In places with a lot of risk, like India’s Himalayan belt and Seismic Zone IV, elevated stencil platforms like the modular SPD21 are very likely to be affected by seismic resonance and torsion caused by traveling waves. These lightweight, open-grid structures are often not well protected by traditional base isolation and ductile detailing. This study evaluates the seismic performance of an SPD21 platform impacted by earthquake waves, integrated with a Tuned Liquid Damper (TLD) as a passive vibration mitigation method. Utilizing Smoothed Particle Hydrodynamics (SPH) and potential flow theory, an extensive 3D Finite Element Analysis (FEA) framework was developed that distinctly integrated Soil Structure Interaction (SSI) for soft alluvial soils with Fluid-Structure Interaction (FSI). The model was tested with IS 1893 (2016) response spectra and time-history waveforms that simulated the long-lasting earthquake in Nepal in 2015. The optimized TLD cuts peak structural displacements by 20% to 50% without the heavy dead-weight penalties that solid mass dampers cause.…..…….[For more click here]

Keywords: Tuned Liquid Damper, Finite Element Analysis, Seismic Vibration Control, Stencil Platform, Sloshing Dynamics

Doi: 10.1045/2026.Bauingenieur/108704-VDI_871

Research Article, Type: Subscription; Pages: 46-58;

Received: 04 January 2026 / Revised: 22 February 2026 / Accepted: 14 March 2026 / Published : 09 April 2026

Title: Digital twins integrated with generative AI for urban smart city infrastructure simulation

Authors: Shravanee Budgude, Ram Sateesh Pasupuleti, Chiara Biscarini, Gaia Proietti, Zhenhua Huang & Laurence C. Espino

Abstract: Digital twins integrated with generative AI revolutionize urban smart city infrastructure simulation by creating dynamic virtual replicas that fuse real-time IoT data from traffic, energy grids, and environmental sensors with AI-generated scenarios for predictive optimization. Generative models like GANs produce synthetic urban layouts and “what-if” tests for climate resilience, enabling rapid iteration of zoning, green corridors, and mobility networks to cut emissions by 20-30%. These systems support participatory planning through immersive VR visualizations, where AI automates 3D city modeling from GIS inputs, aligning with your digital twin research for smart infrastructure. In high-density areas like Delhi, they simulate flood risks and energy demands, optimizing microgrids and drainage via parametric designs tested against live data. This fusion accelerates sustainable development, bridging civil engineering with AI for carbon-neutral cities and disaster-ready frameworks..………….[For more click here]

Keywords: Generative AI (GANs), Smart Infrastructure Optimization, Urban Digital Twins, Predictive Simulation, Climate Resilience

Research Article, Type: Subscription; Pages: 59-72;

Received: 24 January 2026 / Revised: 12 March 2026 / Accepted: 31 March 2026 / Published : 15 April 2026

Title: AI-Driven Digital Twins for the Seismic Resilience and Structural Health Monitoring of Urban Infrastructure

Authors: Varne Rahan, Supriya Sharma, Jin Zijha & Nira Ziyan

Abstract: The emergence of Digital Twin (DT) technology has created a paradigm shift in Structural Health Monitoring (SHM) by moving from reactive diagnostics to real-time predictive maintenance for civil infrastructure. A comprehensive digital twin framework combines high-fidelity, physics-based 3D modeling with continuous data streams from embedded sensor networks. This dynamic cyber-physical loop allows engineers to precisely map real-world structural behaviors, such as strain, vibration, and crack development, directly onto virtual models. By integrating advanced analytics and machine learning algorithms, the system can autonomously identify anomalies and forecast progressive structural deterioration under varying environmental conditions. Recent implementations demonstrate that DT-driven platforms greatly enhance diagnostic precision for complex infrastructures, including urban bridges and earthen dikes. Consequently, integrating these intelligent replicas into full lifecycle management extends service life, optimizes maintenance cycles, and ensures resilient urban development...………….[For more click here]

Keywords: Digital Twins, Structural Health Monitoring (SHM), Machine Learning Algorithms, Predictive Maintenance, Cyber-Physical Systems

Research Article, Type: Subscription; Pages: 73-86;

Received: 25 January 2026 / Resubmitted: 22 March 2026 / Revised: 08 April 2026 / Accepted: 16 April 2026 / Published : 21 April 2026

Title: Analyzing the performance of a biofilter using treated coconut coir and activated charcoal for rainwater harvesting

Authors: Alka Kumari, Akshay Kumar, Shubhendu Amit, Anjali Sinha, Shashi Ranjan, Ranjan Kumar, Shashi Kumar & V.M. Nguyen

Abstract: Global water scarcity and the overexploitation of groundwater have created a critical need for sustainable management practices, such as Rainwater Harvesting (RWH). This study focuses on developing an RWH system for the self-sustained irrigation of the 2500 m² TAAG Park, which currently relies entirely on expensive deep groundwater extraction. To address the high levels of Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), and turbidity naturally found in surface runoff, a custom dual-media filtration unit was physically developed. The filtration system uses a 0.5 m layer of coconut shell activated charcoal that has been chemically enhanced with calcium chloride and a 0.3 m layer of coconut coir that has been treated with a sodium hydroxide solution. Tests done during 30 monsoon events showed that the treated effluent met strict non-potable reuse standards by reducing TSS by 85%, COD by 72%, and turbidity by 83% (from 52 NTU to 9 NTU)….………….[For more click here]

Keywords: Rainwater Harvesting (RWH), Dual-Media Filtration, Activated Charcoal, Coconut Coir, Sustainable Irrigation, Groundwater Conservation

Doi (Jounal): 10.1045/2026.Bauingenieur/108704-VDI_874

Research Article, Type: Subscription; Pages: 87-104;

Received: 12 January 2026 / Revised: 14 March 2026 / Accepted: 01 April 2026 / Published : 21 April 2026

Title: Synergistic Effects of Deep Cryogenic Treatment and Severe Plastic Deformation on Al6061-TiB2 Composites

Authors: Piyali Dutta, Girish Chhimwal, Akshara Govind, Jie Leng, Kang Cheng, Khansa Zaman & Felicia Kim

Abstract: The integration of deep cryogenic treatment (DCT) and severe plastic deformation (SPD) offers a highly effective pathway to significantly enhance the mechanical profile of Al6061-TiB2 metal matrix composites. DCT facilitates the precipitation of fine secondary phases and promotes residual stress relief within the Al6061 matrix, stabilizing its microstructural integrity at sub-zero temperatures. Subsequent application of SPD techniques, such as severe rolling, induces extreme grain refinement and increases dislocation density around the uniformly dispersed TiB2 reinforcing particles. The synergistic interaction between the cryogenically stabilized matrix and the strain-induced grain boundaries restricts dislocation mobility much more effectively than either process applied in isolation. Consequently, these sequentially treated composites exhibit superior ultimate tensile strength, enhanced wear resistance, and improved microhardness, making them highly suitable for advanced load-bearing structural applications…..………….[For more click here]

Keywords: Al6061-TiB2 Composites, Deep Cryogenic Treatment (DCT), Severe Plastic Deformation (SPD), Grain Refinement, Mechanical Properties

Research Article, Type: Subscription; Pages: 105-117;

Received: 20 January 2026 / Revised: 04 March 2026 / Accepted: 11 March 2026 / Published : 24 April 2026

Title: Digital twins integrated with generative AI for urban smart city infrastructure simulation

Authors: Shravanee Budgude, Norman L. Beatty & J. Pagidipati

Keywords: Digital Twins, Generative AI, Smart Cities, Infrastructure Design

Research Article, Type: Subscription; Pages: 118-131;

Received: 01 February 2026 / Revised: 15 March 2026 / Accepted: 30 March 2026 / Published : 24 April 2026

Title: Optimization of Leaf-Vein Inspired Biomimetic Micro-Channel Fins for Latent Heat Thermal Energy Storage Systems

Authors: Jau Chengjie, F. Weipeng, YE Liyu, WUN Chunhui & Pravin Potdukhe

Abstract: This research investigates the thermal performance of triplex-tube heat exchangers equipped with novel leaf-vein inspired biomimetic fin architectures for advanced thermal management. Utilizing phase change materials (PCMs) within these micro-channel structures provides high-density latent heat thermal energy storage, which is critical for regulating temperatures in highly sensitive engineering environments. The study will employ computational fluid dynamics (CFD) to analyze how varying the geometric parameters of the biomimetic fins enhances overall thermal conductivity and accelerates both melting and solidification rates. Experimental validation will be conducted using scaled prototypes to measure heat transfer efficiency during rapid charge and discharge cycles under varying operational loads. By mimicking the natural fluid-transport efficiency of biological vascular systems, this approach seeks to systematically solve the persistent challenge of low thermal conductivity inherent in conventional PCMs. Ultimately, integrating these optimized heat exchangers promises significantly improved energy conservation, structural longevity, and operational stability in complex thermal systems. .………….[For more click here]

Keywords: Latent Heat Thermal Energy Storage (LHTES), Phase Change Materials (PCM), Biomimetic Fins, Triplex-Tube Heat Exchanger, Thermal Conductivity Enhancement

Research Article, Type: Subscription; Pages: 132-145;

Received: 13 February 2026 / Revised: 21 March 2026 / Accepted: 12 April 2026 / Published : 27 April 2026

Title: Integration of Generative AI and Dynamic Digital Twins for the Predictive Seismic Retrofitting of Fiber-Reinforced Concrete Infrastructure

Authors: F. Guoqiang, DAI Shaoshi, S. Liping, Y, Bassam, WANG Chao, HU Jian, GU Lang, XU Pei & YANG Yixin

Abstract: This study proposes a novel framework utilizing generative artificial intelligence to automate the design and evaluation of seismic retrofitting strategies for vulnerable urban structures. By leveraging real-time structural health monitoring data fed directly into a dynamic digital twin, the system can continuously assess the specific vulnerabilities of existing buildings subjected to environmental stress. The research will focus extensively on evaluating the structural efficacy of advanced fiber-reinforced concrete composites when applied virtually as strengthening reinforcements. Generative models will simulate thousands of high-magnitude earthquake “what-if” scenarios to test the physical resilience of various parametric retrofitting configurations in a risk-free digital environment. This predictive optimization approach aims to minimize physical material waste while maximizing dynamic energy dissipation during actual seismic events. The resulting methodology will provide civil engineers with highly accurate, data-driven blueprints to significantly enhance disaster readiness and long-term sustainability in earthquake-prone regions. .………….[For more click here]

Keywords: Digital Twins, Generative AI, Seismic Retrofitting, Fiber-Reinforced Concrete, Predictive Optimization, Smart Infrastructure

Research Article, Type: Subscription; Pages: 146-157;

Received: 02 March 2026; Resubmitted: 21 March 2026; Revised: 24 April 2026; Accepted: 27 April 2026; Published : 04 May 2026

Title: Statistical analysis of driver reaction times under mixed traffic conditions with respect to gender and education

Authors: Rahul Kumar, Shalini Kachhi, Nikhil Biswas, Ankita Kumari, Anand Kumar, Chandra Shekhar Verma

Abstract: The complexity of mixed traffic conditions—characterized by heterogeneous vehicle types, lack of strict lane discipline, and high vehicular interaction—imposes significant cognitive loads on drivers. A critical parameter in traffic safety and highway design is the Perception-Reaction Time (PRT). While standard design guidelines often assume a uniform PRT (e.g., 2.5 seconds), real-world driver responses are highly variable. This study investigates the statistical variance in driver reaction times under mixed traffic environments, focusing specifically on the demographic variables of gender and formal education level. Utilizing a high-fidelity driving simulator coupled with naturalistic observational data (N=450), this research isolates the cognitive and psychomotor processing speeds of drivers facing sudden hazard scenarios (e.g., abrupt pedestrian crossings, erratic two-wheeler maneuverer). A Two-Way Analysis of Variance (ANOVA) and multiple linear regression models were deployed to analyse the data. The findings indicate a statistically significant interaction between education level and hazard perception, where higher formal education correlates with a reduced cognitive processing phase, though baseline reflex times remain largely uniform..………….[For more click here]

Keywords: Perception-Reaction Time (PRT), Mixed Traffic Conditions, Cognitive Load, Sociodemographic Factors, Driving Simulation.

Doi (Journal): 10.1045/2026.Bauingenieur/108711-VDI_871