2026· Journal of Recent Activities in Infrastructure Science· Vol 11, pp. 83-95· 0 citations
TL;DR
This study critically analyzes existing literature to pinpoint current achievements, implementation strategies, and technological progress across various civil engineering fields, and highlights several key research challenges, such as data interoperability, cybersecurity, computational complexity, scalability, standardization, data quality, and model validation.
Abstract
The swift evolution of technologies like artificial intelligence (AI), the Internet of Things (IoT), Building Information Modeling (BIM), cloud computing, and sensor technology has dramatically reshaped how society manages modern civil infrastructure. Among these innovations, the Digital Twin (DT) has emerged as a standout technology: a dynamic virtual representation of physical infrastructure that continuously exchanges real-time data with its physical counterpart. By merging AI with DT, infrastructure systems can engage in smart monitoring, predictive maintenance, fault diagnosis, performance optimization, and even autonomous decision-making throughout their lifecycle. This powerful combination boosts operational efficiency, cuts down maintenance costs, enhances safety, and promotes sustainable infrastructure development. This study reviews the integration of DT technology with AI for smart infrastructure applications. It explores the core concepts, enabling technologies, architecture, and lifecycle of AI-driven DT, while showcasing their applications across buildings, bridges, transportation networks, highways, railways, water distribution systems, and smart cities. Additionally, it discusses the latest advancements in machine learning, deep learning, computer vision, generative AI, large language models (LLMs), and physics-informed AI that elevate the capabilities of DT for infrastructure monitoring and management. It also critically analyzes existing literature to pinpoint current achievements, implementation strategies, and technological progress across various civil engineering fields. The review highlights several key research challenges, such as data interoperability, cybersecurity, computational complexity, scalability, standardization, data quality, and model validation. It also explores exciting future research opportunities in areas like explainable AI, autonomous DT, edge computing, 6G communication, quantum computing, and sustainable infrastructure management.
Findings indicate that AI-enabled digital twins significantly improve equipment reliability, reduce unexpected failures, enhance resource utilization, and enable proactive manufacturing strategies, however, challenges related to interoperability, cybersecurity, computational complexity, data quality, and governance remain critical barriers to widespread industrial adoption.
H. Mahmood· European International Journ...· 0 citations
This review examines the application of AI–IoT integrated technologies across multiple industrial domains to identify their strengths, limitations, and recurring challenges and underscores the importance of scalable, secure, and efficient frameworks to ensure the safe and reliable adoption of AI–IoT in the industrial ecosystem.
Asmarani Ahmad Puzi, Ahmad Anwar Zainuddin, Muhammad Afham Anuar et al.· International Journal of Inn...· 0 citations
The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has given rise to a transformative
paradigm known as the Artificial Intelligence of Things (AIoT). AIoT integrates the sensing and connectivity capabilities of IoT
with the analytical and decision-making power of AI, enabling intelligent systems that can perceive, learn, and act autonomously
in real time. This paper presents a comprehensive overview of AIoT, with a focus on its architecture, applications, and
implications in the education sector. A five-layer AIoT architecture is proposed, comprising the Sensing, Edge, Network, Cloud,
and Application layers, each responsible for specific functions that collectively enable smart, adaptive environments. Key
applications in education, such as automated attendance systems, real-time engagement monitoring, personalized learning
platforms, and smart classroom automation, are discussed to illustrate the practical benefits of AIoT. The paper also examines
the technologies underpinning AIoT systems, including embedded hardware, AI models, communication protocols, and cloud
infrastructure. While AIoT offers significant advantages in terms of efficiency, personalization, and data-driven decisionmaking, challenges related to data privacy, security, algorithmic bias, and implementation costs are also highlighted. The study
concludes by exploring future trends, including edge AI, 5G integration, and immersive learning environments, emphasizing the
potential of AIoT to reshape education and other domains. This work aims to provide BCA students and researchers with a
foundational understanding of AIoT and inspire further exploration into its innovative applications
Unknown authors· International Journal for Re...· 0 citations
Next-generation technologies are transforming the landscape of scientific innovation by enabling intelligent, connected, and data-driven approaches to research and problem-solving. Artificial Intelligence (AI), the Internet of Things (IoT), and intelligent systems are increasingly being integrated into scientific processes to enhance data analysis, experimentation, prediction, automation, and decision-making. AI facilitates advanced computational analysis and pattern recognition, while IoT enables real-time collection and exchange of data through interconnected devices and sensors. Intelligent systems integrate these capabilities to support autonomous operations and adaptive scientific applications. This research examines the role of AI, IoT, and intelligent systems in advancing scientific innovation across diverse domains, including healthcare, environmental science, engineering, agriculture, and industrial research. It further explores their potential benefits, challenges, ethical implications, and future prospects. The study argues that effective integration of these technologies can accelerate scientific discovery, improve research efficiency, and contribute to sustainable and evidence-based technological development.
Mrs. Vinita Edlabadkar, Mrs. Bhushana Thakur, Sonu, CS Khushboo Lalit Shah, Adv. Hardik Goradiya· International Journal of Adv...· 0 citations
Rapid urbanization has increased the need for intelligent and sustainable smart city infrastructure management. Smart cities generate massive amounts of data through IoT devices, sensors, cloud platforms, and communication networks, making traditional centralized AI systems less effective due to scalability, latency, privacy, and reliability challenges. Distributed Artificial Intelligence (DAI) addresses these issues by distributing intelligence across multiple interconnected nodes, enabling decentralized learning and decision-making. This study examines the role of DAI technologies such as multi-agent systems, edge computing, federated learning, IoT networks, and cloud-edge collaboration in managing urban services. A review of recent applications demonstrates DAI’s effectiveness in traffic management, energy distribution, water systems, predictive maintenance, public safety, and environmental monitoring. The proposed framework enhances real-time processing, resource optimization, fault tolerance, and data privacy. Results indicate that DAI outperforms centralized approaches in response time, scalability, accuracy, and reliability. The study concludes that DAI is a key enabler of future smart cities, with emerging technologies such as Explainable AI (XAI), blockchain, digital twins, and autonomous urban management expected to further improve smart city operations and citizen services.
R. Sharma· International Journal of App...· 0 citations
As the world has moved into the fourth industrial revolution, the logistics and warehouses have become more and more part of a digital ecosystem, where intelligent technologies are helping companies to reach unprecedented levels of operational efficiency, flexibility and sustainability. Digital Twin (DT) technology has become a game-changer as it enables virtual representations of physical logistics assets, warehouse structures and processes to be created in real-time. Digital Twins, when supported by Artificial Intelligence (AI), the Internet of Things (IoT), cloud computing, and big data analytics, enable predictive monitoring, autonomous decision making, dynamic resource allocation and ongoing process optimisation. This paper provides a thorough overview of Digital Twin-driven intelligent logistics and warehouse automation, discusses recent developments in technology, implementation models, and their industrial applications. The study covers the challenges of embedding IoT connected sensors, autonomous mobile robots, cyber-physical systems, edge-cloud computing, and machine learning algorithms to improve the productivity, inventory control, route optimization, and predictive maintenance of warehouses. Additionally, the paper examines weaknesses of traditional WMS and uncovers existing problems and gaps in the field of scalability, interoperability, cyber security and real-time synchronization. The survey presented in this work shows the importance of Digital Twin as a crucial enabling technology for new generation smart warehouses and for the intelligent supply chain in Industry 5.0, as well as the opportunities for future research in autonomous logistics operations in Industry 5.0.
Iyengar P. K.· International Journal of Eme...· 0 citations
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