Artificial Intelligence in Civil Engineering: Current Applications, Engineering Capabilities, and Limitations

Generative Artificial Intelligence for Smart Civil Engineering: A Comprehensive Review of Applications, Challenges, and Future Trends

Generative Artificial Intelligence (GenAI) is emerging as a transformative technology in civil engineering, offering advanced computational capabilities that enhance engineering design, project planning, infrastructure management, and decision-making. Unlike traditional artificial intelligence, GenAI can generate text, technical documentation, designs, and analytical outputs, thereby improving productivity and reducing the time required for complex engineering tasks. This review provides a comprehensive overview of recent advancements in GenAI and its applications across key civil engineering domains, including structural engineering, construction management, Building Information Modeling (BIM), transportation engineering, geotechnical engineering, and infrastructure monitoring. The study synthesizes findings from recent literature to examine the benefits, challenges, research gaps, and future trends associated with GenAI adoption. The review highlights the significant role of GenAI in design optimization, project scheduling, predictive maintenance, risk assessment, technical documentation, and smart infrastructure development. It also discusses critical challenges such as data quality, model reliability, explainability, cybersecurity, ethical concerns, and professional accountability. Overall, the review demonstrates that GenAI has substantial potential to support intelligent, sustainable, and data-driven civil engineering practices while emphasizing the need for human expertise and responsible AI implementation.

Large artificial intelligence models for coal mining: Development, applications and challenges

Data-Centric Engineering: Integrating Simulation, Machine Learning, and Statistics

Data-centric engineering (DCE) represents a paradigm shift in modern engineering by merging traditional physics-based simulation approaches with machine learning (ML) and statistical methods. As industries face increasing demands for system complexity, efficiency, and reliability, DCE offers a robust framework to model, predict, and optimize engineering processes and products. This article delves deep into the confluence of simulation, ML, and statistics, showcasing how they synergize to improve engineering workflows. By leveraging high-fidelity simulations, advanced ML algorithms, and statistical inference, DCE enables real-time decision-making, anomaly detection, design optimization, and predictive maintenance. In this paper, we explore the historical evolution of DCE, current applications, and its transformative potential across aerospace, automotive, civil infrastructure, and manufacturing domains. The integration of multi-source data, model uncertainty quantification, and digital twins forms the core of our discussion. We present detailed methodologies for hybrid modeling approaches and data fusion techniques and provide experimental results that validate the efficiency of DCE in improving performance metrics. Case studies demonstrate significant improvements in product life cycles, resource allocation, and safety standards. The findings emphasize that DCE is not just a technological advancement but a foundational strategy for next-generation engineering solutions.

Modeling in the Era of AI-Driven Industrial Automation and Optimization

The growing influence of artificial intelligence (AI) is reshaping process systems engineering (PSE) and industrial modeling and optimization. While data-driven methods excel in predictive maintenance, anomaly detection, and pattern recognition, they still face challenges in safety-critical, data-scarce, and extrapolation-prone environments. This paper argues that first-principles models (FPMs) (rooted in fundamental physics, chemistry, and engineering) remain essential for mission- and business-critical workflows in industrial automation, both in process design and operations. We highlight the enduring strengths of first-principles and examine hybrid paradigms that combine mechanistic rigor with data-driven machine learning (ML) and large language models (LLMs) to enhance adaptability and efficiency. Case studies across process design, advanced process control (APC), real-time optimization (RTO), production planning, production scheduling, and supply chain management illustrate the value of retaining first principles as a backbone for modeling and optimization. We conclude that the future lies in AI-enabled systems grounded in first principles and mathematical optimization, where hybrid intelligence integrates mechanistic rigor with data-driven insights to deliver smarter design, safer operations, and more sustainable processes.

Machine Learning for Concrete Performance Prediction and Intelligent Optimization: A Comprehensive Review

With the rapid development of artificial intelligence (AI) technologies, machine learning (ML) has been widely applied in concrete material design, performance prediction, and intelligent structural engineering. Compared with traditional empirical approaches, ML can efficiently establish complex nonlinear relationships among concrete mix proportions, environmental factors, and performance indicators, thereby improving prediction efficiency, reducing experimental costs, and enabling multi-objective optimization of mix proportions. This paper systematically reviews the recent research progress of ML technologies in the field of concrete engineering, with particular emphasis on typical algorithms, including supervised learning, unsupervised learning, and reinforcement learning. Their applications in predicting workability, mechanical properties, durability performance, and mix proportion optimization are comprehensively summarized. In addition, recent advances in ML applications for crack detection and digital twin technologies are also discussed. Moreover, deep learning and computer vision (CV) technologies have significantly promoted the development of crack identification and structural health monitoring, whereas the integration of digital twin and Internet of Things (IoT) technologies has further expanded the application of ML in smart infrastructure. Finally, the current challenges associated with data quality, model interpretability, and engineering applications are summarized, and future research directions are discussed. Overall, by linking algorithm choice to specific concrete performance-prediction tasks, this review clarifies the conditions under which ML delivers reliable results and provides a structured reference for both researchers and practitioners. The comparative analysis indicates that ensemble tree-based models—particularly random forest and gradient-boosting variants such as XGBoost—together with well-tuned neural networks consistently achieve the highest predictive accuracy across most concrete properties, with reported test-set coefficients of determination commonly between 0.90 and 0.99, whereas limited data availability, inconsistent validation protocols, and restricted model interpretability remain the principal obstacles to engineering deployment.

INDUSTRIAL AND SYSTEMS OPTIMIZATION IN THE ERA OF INTELLIGENT, RESILIENT, SUSTAINABLE, AND HUMAN-CENTERED INDUSTRY: AN EDITORIAL INSIGHT

ABSTRACT Industrial and systems optimization is experiencing a significant shift from conventional efficiency-oriented approaches toward more intelligent, resilient, sustainable, and human-centered systems. This editorial insight examines the current direction of industrial and systems optimization amid rapid technological development, supply chain uncertainty, sustainability pressures, and the growing need for data-driven decision-making. The discussion highlights several emerging trends, including artificial intelligence, machine learning, digital twins, supply chain resilience, predictive maintenance, sustainable and multi-objective optimization, and Industry 5.0. These developments show that optimization is no longer limited to minimizing cost, reducing process time, or maximizing output. Instead, optimization should be understood as a broader systems approach that integrates data, technology, human factors, operational risk, environmental responsibility, and practical decision-making. This article also emphasizes the importance of clear problem formulation, transparent assumptions, reliable data, model validation, sensitivity analysis, and practical relevance in industrial optimization research. Within the scope of JISO: Journal of Industrial and Systems Optimization, this editorial insight encourages future studies that connect rigorous methods with real industrial problems, especially in manufacturing systems, supply chain and logistics, facility design, production planning, inventory control, occupational health and safety, ergonomics, sustainable manufacturing, maintenance, and decision analytics. The article concludes that the future of industrial and systems optimization lies in its ability to support systems that are not only efficient, but also adaptive, responsible, resilient, and beneficial for both industry and society. ABSTRAK Optimasi sistem dan industri mengalami pergeseran signifikan dari pendekatan berorientasi efisiensi konvensional menuju sistem yang lebih cerdas, tangguh, berkelanjutan, dan berpusat pada manusia. Wawasan editorial ini membahas arah optimalisasi industri dan sistem saat ini sebagai tanggapan terhadap perkembangan teknologi yang cepat, ketidakpastian rantai pasokan, tekanan keberlanjutan, dan meningkatnya kebutuhan akan pengambilan keputusan berbasis data. Diskusi ini menyoroti beberapa tren yang muncul, termasuk kecerdasan buatan, pembelajaran mesin, kembar digital, ketahanan rantai pasokan, pemeliharaan prediktif, pengoptimalan berkelanjutan dan multi-objektif, dan Industri 5.0. Perkembangan ini menunjukkan bahwa optimasi tidak lagi terbatas pada meminimalkan biaya, mengurangi waktu proses, atau memaksimalkan output. Sebaliknya, pengoptimalan harus dipahami sebagai pendekatan sistem yang lebih luas yang mengintegrasikan data, teknologi, faktor manusia, risiko operasional, tanggung jawab lingkungan, dan pengambilan keputusan praktis. Artikel ini juga menekankan pentingnya perumusan masalah yang jelas, asumsi transparan, data yang dapat diandalkan, validasi model, analisis sensitivitas, dan relevansi praktis dalam penelitian optimasi industri. Dalam lingkup JISO: Journal of Industrial and Systems Optimization, wawasan editorial ini mendorong studi masa depan yang menghubungkan metode ketat dengan masalah industri nyata, terutama dalam sistem manufaktur, rantai pasokan dan logistik, desain fasilitas, perencanaan produksi, kontrol inventaris, kesehatan dan keselamatan kerja, ergonomi, manufaktur berkelanjutan, pemeliharaan, dan analitik keputusan. Artikel tersebut menyimpulkan bahwa masa depan optimasi industri dan sistem terletak pada kemampuannya untuk mendukung sistem yang tidak hanya efisien, tetapi juga adaptif, bertanggung jawab, tangguh, dan bermanfaat bagi industri dan masyarakat.