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

This 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

This article delves deep into the confluence of simulation, ML, and statistics, showcasing how they synergize to improve engineering workflows and emphasizes 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

It is argued that first-principles models (FPMs) remain essential for mission- and business-critical workflows in industrial automation, both in process design and operations and 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

Comparison 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, 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.