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.

Natural fiber-reinforced polyolefin composites have emerged as promising sustainable materials; however, their mechanical performance is strongly governed by the chemical composition and moisture characteristics of the reinforcing fibers. This research establishes an interpretable Response Surface Methodology (RSM)-based framework to forecast and enhance the tensile strength (TS) and tensile modulus (TM) of natural fiber/HDPE composites as functions of cellulose content, hemicellulose content, moisture content, and filler loading. A complete dataset from the literature was put together, cleaned up, normalized, and coded for regression analysis. Second-order polynomial models were formulated within a Central Composite Design (CCD)-based analytical framework and validated using analysis of variance (ANOVA), residual diagnostics, and cross-validation. The developed models were highly significant ( p < 0.0001), exhibited excellent goodness of fit (R ² > 0.98), and achieved average external validation errors below 5%. The results indicated that TS and TM increased with cellulose content and filler loading up to an optimal region of approximately 65–70 wt.% cellulose and 25–30 wt.% filler, after which performance declined because of fiber agglomeration and reduced polymer wetting. In contrast, increasing hemicellulose and moisture content decreased mechanical performance because of their hygroscopic and amorphous characteristics. Multi-response optimization identified an optimal composition of approximately 66 wt.% cellulose, 19 wt.% hemicellulose, 8 wt.% moisture, and 27 wt.% filler, corresponding to maximum predicted values of 45.2 MPa for TS and 2.95 GPa for TM. The proposed composition-driven RSM framework provides a practical quantitative basis for designing high-performance sustainable polyolefin composites.

Sustainable and resilient road infrastructure requires the early identification of pavement deterioration mechanisms that emerge under complex urban traffic conditions, particularly at signalized intersections where repeated stop–go operations, queue persistence, and lane-wise freight concentration generate highly nonuniform structural loading. However, most existing intelligent transportation studies emphasize crash prediction, traffic-state estimation, or mobility optimization, while the infrastructure-performance consequences of freight-dominant interrupted flow remain insufficiently addressed. To support proactive pavement management and resilient urban road operation, this study proposes a traffic simulation-driven deep learning framework for predicting lane-level pavement deterioration under freight loading and stop–go urban traffic conditions. A high-resolution PTV Vissim 2024 microscopic simulation environment was developed for a four-leg signalized urban intersection, and a structured multi-scenario design was used to generate progressively increasing operational stress regimes, ranging from baseline flow to freight-dominant oversaturated operation. The resulting lane-wise dataset integrates direct traffic variables with pavement-oriented descriptors, including the Lane Freight Loading Index (LFLI), Stop–Go Severity Index (SGSI), ESAL proxy, queue persistence, and Loading Asymmetry Index (LAI). To learn the complex relationship between traffic operation and infrastructure degradation, a new Freight-Aware Lane Interaction Transformer Network (FLIT-Net) is introduced. The proposed model combines feature embedding, lane-interaction self-attention, freight-aware gating, residual refinement, and multi-task regression to jointly predict rutting risk, fatigue-cracking risk, and the Pavement Deterioration Index (PDI). Experimental results show that FLIT-Net outperforms MLP, CNN, LSTM, Bi-LSTM, and generic Transformer baselines, achieving RMSE/MAE/$R^2$ values of 0.041/0.032/0.9687 for rutting risk, 0.044/0.034/0.9635 for fatigue-cracking risk, and 0.031/0.024/0.9824 for PDI. Sensitivity and scenario-wise analyses further confirm that deterioration increases monotonically with freight intensity, stop–go severity, and queue persistence, highlighting the importance of lane-resolved deterioration intelligence for sustainable maintenance prioritization. The proposed framework bridges traffic microsimulation, pavement-oriented feature engineering, and freight-aware deep learning, providing a decision-support basis for improving the performance, safety, and resilience of urban pavement infrastructure.

UAV-based remote sensing systems are increasingly deployed in smart surveillance, disaster response, environmental monitoring, and critical infrastructure inspection. In these applications, aerial sensing platforms must transmit telemetry, control commands, and observation data securely and reliably under strict latency, energy, and computational constraints. However, existing security approaches often fail to jointly provide lightweight payload confidentiality, quantum-resilient key establishment, and adaptive communication protection suitable for dynamic and resource-constrained aerial sensing environments. To address this challenge, this paper proposes an energy-aware post-quantum hybrid cryptographic framework for secure and low-latency UAV remote sensing communications in UAV–IoT mission networks. The proposed framework integrates Ascon-based authenticated encryption for low-overhead protection of remote sensing payloads and mission telemetry, ML-KEM-based post-quantum session-key establishment for long-term resilience against quantum-era threats, and an AI-driven adaptive rekeying mechanism that dynamically adjusts key-refresh decisions according to threat level, residual energy, mobility state, channel stability, anomaly density, traffic sensitivity, link type, and mission progression. Accordingly, rekeying is treated not as a static maintenance process but as an intelligent and context-aware cryptographic control function that adapts communication security to evolving mission and sensing conditions. The framework is evaluated across twenty progressively demanding scenarios involving different UAV counts, sensor densities, payload sizes, communication modes, and adversarial settings relevant to real-time remote sensing operations. Experimental results demonstrate a secure delivery rate of 99.2%, attack detection and mitigation effectiveness of 98.9%, end-to-end encryption latency of 8.7 ms, throughput of 5.03 Mbps, energy overhead of 11.6 mJ/session, rekeying overhead of 2.9 mJ/event, session resilience of 96.4%, and integrity verification success of 99.1%. These findings show that the proposed framework provides a practical and scalable contribution to post-quantum secure UAV remote sensing by unifying lightweight authenticated encryption, ML-KEM-based quantum-resilient key establishment, and AI-driven adaptive rekeying within a resilient aerial–terrestrial communication architecture.