This study presents an AI-based framework that unifies civil and mechanical engineering principles to optimize the structural performance of steel frameworks. Unlike traditional methods that analyze material behavior, load-bearing capacity, and dynamic response separately, the proposed model integrates these factors into a single hybrid feature space combining material properties, geometric descriptors, and load-response characteristics. A deep learning model enhanced with physics-informed reliability constraints is developed to predict both safety states and optimal design configurations. Using AISC steel datasets and experimental records, the framework achieves 99.91% accuracy in distinguishing safe from unsafe designs, with mean absolute errors below 0.05 and percentage errors under 2% for reliability and load-bearing predictions. The system also demonstrates high computational efficiency, achieving inference latency below 3 ms, which supports real-time deployment in design and monitoring environments. the proposed framework provides a scalable, interpretable, and code-compliant approach for optimizing steel structures, advancing data-driven reliability assessment in both civil and mechanical engineering.

The pursuit of energy efficiency and sustainability in the built environment has placed façade systems at the forefront of innovation in architectural design. This study proposes an integrated framework that combines generative design techniques with artificial intelligence (AI) to optimize composite façade configurations for next-generation smart buildings. Using parametric modeling, a wide design space of façade geometries and material compositions was generated, capturing trade-offs between thermal performance, daylight, structural strength, and aesthetic variability. Artificial intelligence algorithms, particularly machine learning models, are trained on simulation-derived performance datasets to rapidly predict key indicators such as energy consumption, thermal transmittance (U-value) and solar heat gain coefficients. The proposed approach achieved a predictive accuracy of 99.85%, enabling efficient exploration of optimal solutions across high-dimensional design alternatives. A multi-objective optimization strategy was further implemented to balance energy efficiency with structural and aesthetic constraints, producing façade configurations that outperform conventional designs. The findings demonstrate that integrating generative design with AI-based prediction not only accelerates the façade design process but also provides actionable pathways toward net-zero energy buildings. This research highlights the transformative potential of AI-driven generative workflows in advancing sustainable architecture and delivering intelligent, adaptive and performance-oriented façades for future urban environments.

Sustainable and resilient communities increasingly rely on interdependent, data-driven building systems where material choices, energy performance, and lifecycle impacts must be optimized jointly. This study presents a digital-twin-ready, system-of-systems (SoS) decision-support framework that integrates BIM-enabled building energy simulation with an AI-enhanced lifecycle assessment (AI-LCA) pipeline to optimize fiber-reinforced concrete (FRC) façade systems for smart buildings. Conventional LCA is often inventory-driven and static, limiting its usefulness for SoS decision making under operational variability. To address this gap, we develop machine learning surrogate models (Random Forests, Gradient Boosting, and Artificial Neural Networks) to perform a dual prediction of façade mechanical performance and lifecycle indicators (CO₂ emissions, embodied energy, and water use), enabling a rapid exploration of design alternatives. We fuse experimental FRC measurements, open environmental inventories, and BIM-linked energy simulations into a unified dataset that captures coupled material–building behavior. The models achieve high predictive performance (up to 99.2% accuracy), and feature attribution identifies the fiber type, volume fraction, and curing regime as key drivers of lifecycle outcomes. Scenario analyses show that optimized configurations reduce embodied carbon while improving energy-efficiency trajectories when propagated through BIM workflows, supporting carbon-aware and resilient façade selection. Overall, the framework enables scalable SoS optimization by providing fast, coupled predictions for façade design decisions in smart built environments.

Smart and sustainable construction requires methods that jointly manage construction processes, carbon emissions, energy supply, and decision-making. This study develops a renewable energy (RE)-aware carbon digital twin using Siemens Tecnomatix Plant Simulation, version 2022 (Siemens Digital Industries Software, Plano, TX, USA), hereafter referred to as STPS, and artificial intelligence (AI)-based multi-objective optimization. The framework integrates activity records, equipment-demand profiles, renewable-generation data, battery-storage states, emission factors, cost parameters, schedule indicators, and reinforcement learning (RL) state–action–reward records within a discrete-event simulation environment. Construction activities are represented through event-driven source, queue, buffer, processor, resource-pool, transporter, event-controller, table-file, and sink objects, enabling predecessor validation, resource allocation, processing, completion tracking, and key performance indicator (KPI) updates. The energy layer coordinates equipment demand, solar photovoltaic (PV) generation, battery charging and discharging, grid electricity, diesel backup, RE share, battery state of charge (SOC), and emissions. The AI-control layer observes carbon, cost, delay, queue length, utilization, idle time, renewable share, and SOC; filters infeasible actions; evaluates corrective interventions; and ranks policies under carbon, delay, and SOC constraints. Three scenarios are evaluated: diesel–grid baseline operation, rule-based solar-battery operation, and AI-controlled renewable-aware operation. The AI-controlled scenario achieved 16,850 kg carbon dioxide equivalent (kg CO₂e), a cost of 2.28 million United States dollars (M USD), a delay of 4.2 h, an 81.6% RE share, 86.7% resource utilization, and 8.6% idle time. Relative to the diesel–grid baseline, it reduced emissions by 32.2%, cost by 18.0%, delay by 66.7%, and diesel-equivalent energy by 97.7%, while increasing RE share by 69.3 percentage points. Reward-weight sensitivity results show that the balanced policy preserves low-carbon performance across stakeholder priorities. The findings demonstrate that integrating digital twins, RE dispatch, and AI-based decision control can support data-driven, optimized low-carbon construction management.

Mechanics‐conditioned reconstruction of nonlinear stress–strain responses in high‐performance cementitious materials is essential for interpreting experimentally characterized material behavior and supporting the safe design of underground, geotechnical, and structural systems. Owing to the heterogeneous nature and complex damage evolution of these materials, conventional constitutive models often fail to capture their full mechanical behavior under varying loading conditions. This study introduces a Genetic Algorithm Optimized Long Short‐Term Memory (GA‐LSTM) framework that combines evolutionary optimization and deep learning to reconstruct complete stress–strain curves of high‐performance cementitious materials conditioned on known or experimentally derived mechanical descriptors, including elastic modulus, peak stress, peak strain, and toughness. Experimental data from uniaxial compression tests were systematically preprocessed using cleaning, normalization, interpolation, and Savitzky–Golay filtering to preserve physical integrity. The genetic algorithm optimized key hyperparameters, including network depth, hidden units, dropout rate, and sequence length under a multi‐objective, physics‐informed strategy. The proposed model achieved superior curve‐reconstruction accuracy (, RMSE MPa) with minimal deviation in peak stress and post‐peak toughness. Comparative statistical analyses (ANOVA, Tukey's HSD, and Pearson correlation) verified the model's reliability and robustness. The developed GA‐LSTM framework offers a scalable and interpretable tool for mechanics‐conditioned constitutive reconstruction, mechanical‐response characterization, and geomechanical simulation within experimentally characterized high‐performance cementitious material domains.

Predicting the nonlinear stress-strain response of high-performance cementitious materials is essential for the safe and efficient design of underground, geotechnical, and structural systems. Owing to the het erogeneous nature and complex damage evolution of these materials, conventional constitutive models of ten fail to capture their full mechanical behavior under varying loading conditions. This study introduces a Genetic Algorithm Optimized Long Short-Term Memory (GA-LSTM) framework that combines evo lutionary optimization and deep learning to model the complete stress-strain curve of high-performance concrete. Experimental data from uniaxial compression tests were systematically preprocessed using cleaning, normalization, interpolation, and Savitzky-Golay filtering to preserve physical integrity. The genetic algorithm optimized key hyperparameters including network depth, hidden units, dropout rate, and sequence length under a multi-objective, physics-informed strategy. The proposed model achieved superior predictive accuracy (R2 ≈ 0.995, RMSE < 3 MPa) with minimal deviation in peak stress and post-peak toughness. Comparative statistical analyses (ANOVA, Tukey’s HSD, Pearson correlation) ver ified the model’s reliability and robustness. The developed GA-LSTM framework offers a scalable and interpretable tool for mechanical characterization, design optimization, and geomechanical simulation of rock-concrete composite systems in civil, mining, and geotechnical engineering.