Ground granulated blast-furnace slag (GGBS) is a widely used precursor in the development of geopolymer concrete (GPC) due to its high reactivity. While nanomaterials have been explored for enhancing GPC properties, the specific influence of nano-Al ₂ O ₃ (NA) on the mechanical and durability performance of GGBS-based GPC remains underexplored. This study investigates the effect of NA at dosages of 1, 2, and 3% as a partial replacement of GGBS, aiming to optimize the strength and durability of GPC. The highest strength was achieved with 2% NA, yielding compressive, split tensile, and flexural strengths of 57.36, 5.81, and 5.59 MPa, respectively, at 90 days of curing. Durability performance was examined by exposing the GPC to harmful chemicals like 6% HCl, 6% H ₂ SO ₄ , 6% NaCl, and 6% Na ₂ SO ₄ . The water absorption is reduced with an increase in the dosage of NA in concrete. This is due to the non-porous nature of the dense and compact NA-incorporated GPC. Microstructure analysis was conducted to evaluate the influence of NA at nano- and microlevels. The formation of the extra N–A–S–H gel after incorporation of NA resulted in an enhancement in the strength of GPC. The key novelty of this study lies in providing comprehensive insights into the role of NA in refining the pore structure, enhancing geopolymerization, and improving the chemical resistance of GGBS-based GPC. The findings highlight the potential of NA in producing high-performance, sustainable concrete for aggressive environments.

Artificial intelligence (AI) has become an essential force in modern civil engineering, reshaping conventional methods through intelligent data processing, predictive modeling, and automated decision-making. This review presents a comprehensive synthesis of AI applications across eight major civil engineering domains: structural analysis and design, concrete technology, geotechnical engineering, hydraulic and water systems, transportation and pavement engineering, construction management, building information modeling (BIM), and green infrastructure with sustainability assessment. The study systematically analyzes findings from 106 peer-reviewed sources, highlighting how AI models, especially machine learning (ML), deep learning (DL), and hybrid combinations such as ANN-PSO and CNN-XGBoost, have significantly improved performance in complex, nonlinear, and data-intensive scenarios. The review reveals that AI-based systems have enhanced the precision of strength predictions, classification of soil types, analysis of fluid dynamics, optimization of transport networks, and the integration of BIM environments with real-time data for advanced infrastructure management. In construction, AI supports safety monitoring, resource planning, and risk forecasting, while in sustainability, it aids in carbon footprint modeling and energy efficiency analysis. Moreover, the incorporation of Internet of Things (IoT) devices and sensor-based systems with AI has enabled real-time feedback and intelligent decision support in several infrastructure systems. Despite its transformative impact, AI integration in civil engineering still faces limitations. These include data scarcity, model transparency challenges, high computational costs, and lack of standardized benchmarks. To address these issues, the review outlines key research recommendations, including the use of physics-informed neural networks (PINNs), the adoption of transfer learning for small datasets, explainable AI (XAI) frameworks, and AI-enabled digital twins for infrastructure lifecycle management. This paper serves as a consolidated reference for researchers and professionals aiming to harness AI in the planning, design, operation, and sustainability of civil infrastructure systems. It not only maps the current state of AI in civil engineering but also offers a roadmap for future advancements toward smarter and more resilient infrastructure.

Machine learning (ML) models in material science and construction engineering have significantly improved predictive accuracy and decision making. However, the practical implementation of these models often requires technical expertise, limiting their accessibility for engineers and practitioners. A user-friendly graphical user interface (GUI) can be an essential tool to bridge this gap. In this study, a sustainable approach to improve the compressive strength (C.S) of plastic-based mortar mixes (PMMs) by replacing cement with industrial waste materials was investigated using ML models such as support vector machine, AdaBoost regressor, and extreme gradient boosting. The significance of key mix parameters was further analyzed using SHapley Additive exPlanations (SHAPs) to interpret the influence of input variables on model predictions. To enhance the usability and real-world application of these ML models, a GUI was developed to provide an accessible platform for predicting the C.S of PMMs based on input material proportions. The ML models demonstrated strong correlations with experimental results, and the insights from SHAP analysis further support data-driven mix design strategies. The developed GUI serves as a practical and scalable decision support system, encouraging the adoption of ML-based approaches in sustainable construction engineering.