Modern electronics are built on semiconductors, which allow for improvements in communication, computing, healthcare, and renewable energy systems. Miniaturized and highly efficient electronic components are the result of ongoing research into semiconductor materials, fabrication techniques, and device layouts. Fundamental semiconductor principles, recent developments in semiconductor materials, new device technologies, fabrication difficulties, and upcoming trends are all covered in this essay. Nanoscale devices, power semiconductors, and semiconductor applications in Internet of Things (IoT) and artificial intelligence (AI) systems are given particular attention..

The growing complexity of operations at contemporary cloud data centers demands a shift in the response-oriented management of resources to autonomous and intelligence-driven control of infrastructure. This paper will suggest a Cognitive Infrastructure Modeling (CIM) framework that is used to support a self-optimizing data center with a multi-objective Cognitive Predictive Self-Optimization (CPSO) algorithm that is based on workload forecasting, reinforcement learning, and variance-sensitive scheduling. .

Facial expression of emotion are signals of elevated biological values. They are thought to have evolved in part to serve a critical communicatory function between conspecifics. Cross-cultural differences in perception of facial expressions revealed by recent studies. The facial expression recognition system presented in this research work contributes a resilient face recognition model. It is based on the mapping of behavioral characteristics with the physiological biometric characteristics. The behavioral aspect of this system relates the attitude behind different expressions as property base. Property bases are alienated as exposed and hidden category in genetic algorithmic genes. The design of a novel asymmetric cryptosystem based on biometrics eliminates the use of passwords and smart cards. This research work promises a new direction of research..

The current research examines how atmospheric mass influences radiative cooling and near surface warming in an idealized, moist atmospheric aquaplanet general circulation model (GCM). The research is performed by methodically changing surface pressure by a factor of 0.5 to 10 times the current Earth value to examine the thermodynamic reactions of the atmosphere in the conditions of low levels of solar luminosity that were present on early Earth. Findings reveal that the addition of atmospheric mass has a great deal of warming effect on the near-surface temperature, most of which is caused by diminishing radiative cooling as the mass increases because of the augmented heat capacity of the atmosphere. .

This study evaluated the effect of Gibberellic acid (GA₃) at 0.25 mg/L on the growth and tuber production of potato plants cultivated under aeroponic conditions. Tissue-cultured potato plantlets were acclimatized and grown in a controlled polyhouse environment using a standard hydroponic nutrient system. Growth parameters were assessed at 7, 15, 30, and 45 days and compared with untreated controls using statistical analysis (P < 0.05). No significant differences were observed at 7 days; however, from the 15th day onward, GA₃ treatment significantly enhanced shoot length, stem length, leaf length, and plant height. By the 45th day, a marked increase in leaf thickness and tuber number was recorded, with tuber yield nearly doubling compared to the control. .

The global energy sector is undergoing a significant transformation due to the increasing need to combat climate change, reduce emissions, and ensure energy security. The shift towards renewable energy sources such as solar, wind, and hydroelectric power is essential for sustainable energy systems. However, the intermittency and variability of renewable sources pose challenges in grid stability and efficient energy management. Artificial intelligence (AI) technologies, such as machine learning, optimization algorithms, and predictive maintenance, offer solutions to improve energy forecasting, system optimization, and grid integration. The role of AI in enhancing renewable energy efficiency, reliability, and sustainability is crucial for future energy systems..

Transportation infrastructure is crucial for economic growth and social development, with highways being the core element. Geometric design parameters, such as alignment, curve radius, lane width, sight distance, and intersection configuration, are essential in ensuring traffic performance, safety, and environmental sustainability. Improper design increases the risk of accidents and traffic congestion. Advances in computational tools, AI, and machine learning provide new methods for optimizing these parameters, focusing on safety and operational efficiency. This paper highlights how design optimization can reduce road accidents, improve traffic flow, and contribute to environmental sustainability through enhanced design features and innovative technologies..

Artificial Intelligence (AI)-based Energy Management Systems (EMS) have revolutionized energy efficiency and sustainability in modern electrical systems. By integrating machine learning, deep learning, and reinforcement learning, these systems optimize energy consumption in smart grids, microgrids, and smart buildings. With the increasing reliance on renewable energy sources like solar and wind, AI enhances grid stability and minimizes energy wastage through predictive analytics and adaptive control. Despite challenges in computational cost, data quality, and real-world deployment, AI-based EMS remains a promising solution for improving resource utilization and advancing sustainability in energy systems..

The rapid evolution of Industry 4.0 has transformed traditional manufacturing into highly automated and intelligent systems. Predictive Maintenance (PdM) leverages AI and Machine Learning (ML) to optimize maintenance practices by predicting equipment failures before they occur. This approach, powered by real-time data from Industrial Internet of Things (IIoT) sensors, enhances efficiency and reduces operational costs. AI models such as Artificial Neural Networks (ANNs) and hybrid architectures like Attention-Gated Recurrent Units (At-GRU) have significantly improved fault detection accuracy. Despite challenges such as data quality and system integration, AI-driven PdM continues to drive advancements in smart manufacturing..

The rapid development of Digital Manufacturing Systems (DMS) has been a significant influence on industrial production, driving the evolution of Industry 4.0. These systems incorporate advanced technologies such as Artificial Intelligence (AI), Cyber-Physical Systems (CPS), the Internet of Things (IoT), and Digital Twin technologies to optimize manufacturing processes. The shift from Computer-Integrated Manufacturing (CIM) to Smart Manufacturing is aimed at enhancing flexibility, efficiency, and customization in production systems. These innovations are essential for adapting to the dynamic demands of global markets, despite challenges like workforce resistance and cybersecurity risks..

The dynamic modeling and analysis of mechanical systems has become a critical aspect of modern mechanical engineering. With the increasing complexity of systems, traditional methods are often insufficient to capture nonlinearity and multi-physics interactions. Simulation-based techniques, such as finite element analysis and multibody dynamics, offer a more accurate approach for system behavior prediction. Recent advancements in computational mechanics have allowed for more robust modeling, enabling better optimization, fault diagnosis, and performance evaluation. Approaches like physics-informed neural ordinary differential equations (PINODE) and hybrid models are enhancing model accuracy while maintaining interpretability. Simulation-driven frameworks play a key role in system identification and control applications..

The rapid expansion of wireless communication systems has resulted in an increased need for high-speed, reliable, and low-latency data transmission. However, challenges such as noise and interference significantly degrade signal quality, affecting system performance across applications like IoT, autonomous vehicles, and smart cities. Traditional noise reduction methods, including adaptive signal processing, are insufficient in modern dynamic environments. Machine Learning (ML)-based noise reduction techniques have emerged as promising solutions, offering intelligent, real-time adaptation to enhance system performance. AI-driven approaches have shown improvements in throughput, latency, and reliability, enabling advanced solutions for next-generation wireless networks, including 5G and 6G..

Wireless communication systems have evolved significantly to meet the rising demand for high-speed data, reliable connectivity, energy efficiency, and security. This evolution is driven by advancements in digital modulation techniques, including adaptive modulation, AI-based optimization, and MIMO-OFDM systems. Traditional schemes, such as ASK and QAM, are being replaced by advanced methods that improve spectral efficiency and reduce bit error rates. Innovations like 3D-HQAM and AI-driven hybrid optimization algorithms enhance system performance in noisy, fading environments. Additionally, physical-layer security and semantic communication frameworks are redefining system security and efficiency, ensuring reliable performance in complex wireless environments..

Machine vision systems have revolutionized quality control in manufacturing by offering automated, precise, and real-time defect detection. These systems integrate AI and deep learning for enhanced accuracy and adaptability, significantly improving production efficiency across industries such as automotive, electronics, and aerospace. Recent innovations include automated defect detection through image processing algorithms, AI-based frameworks for anomaly detection, and integration with industrial automation systems like PLCs. While large-scale adoption faces challenges, such as high costs and lack of technical expertise, the evolution of machine vision continues to shape smart manufacturing environments, improving both operational and inspection accuracy..

The integration of big data, machine learning (ML), and artificial intelligence (AI) has revolutionized decision-making through predictive analytics, enabling the analysis of complex datasets. Predictive analytics, involving statistical techniques and data mining, supports decision-making in sectors like healthcare, finance, and manufacturing. With the need for intelligent systems, AI-driven analytics optimize forecasting and operational efficiency. While challenges in scalability, privacy, and model interpretability persist, the combination of ML and big data enhances accuracy in real-time decision-making across industries. These advancements have transformed traditional decision support systems, providing strategic insights and improving outcomes across various domains..

The rapid expansion of social media has generated vast amounts of unstructured textual data, revealing users' opinions, emotions, and behaviors. Sentiment analysis, a critical task in natural language processing (NLP), leverages deep learning models to classify this data into positive, negative, or neutral sentiments. Traditional machine learning approaches struggled with contextual understanding, leading to the adoption of deep learning techniques like CNNs, RNNs, and transformer-based models such as BERT. These models offer enhanced accuracy and contextual understanding. Despite advances, challenges such as multilingual sentiment analysis and sarcasm detection remain, highlighting areas for future exploration in social media sentiment classification..

Secure data transmission is essential in modern communication systems, especially with the rise of cloud computing, IoT, vehicular networks, and distributed infrastructures. Traditional cryptographic methods are inadequate in addressing evolving cyber threats such as quantum decryption and persistent attacks. To enhance security, integrating machine learning (ML) with cryptography offers adaptive systems capable of real-time threat detection and intrusion management. Studies highlight innovations in vehicular, cloud, and IoT environments, such as hybrid encryption models and ML-driven anomaly detection, which improve data security and system efficiency. This approach is increasingly vital in protecting sensitive data across diverse platforms and emerging technologies..

The seismic performance assessment of reinforced concrete (RC) buildings has gained significant importance due to increasing earthquake frequency and aging infrastructure. Traditional analytical methods, like nonlinear time history analysis, are computationally intensive, making them impractical for large-scale applications. Artificial intelligence (AI) models, particularly machine learning (ML) and deep learning, offer scalable, data-driven solutions. These AI techniques enhance seismic vulnerability predictions, surpassing traditional methods like Rapid Visual Screening (RVS). Furthermore, AI has enabled advancements in structural control systems, improving seismic mitigation. AI-based approaches, including hybrid models and reinforcement learning, have demonstrated promising results, improving prediction accuracy and overall performance..

The prediction of structural behavior under varying loading conditions is crucial in structural engineering, especially for critical infrastructures like bridges and high-rise buildings. Traditionally, the Finite Element Method (FEM) has been widely used for simulating structural responses, but its computational expense, particularly under complex conditions, necessitates the integration of Artificial Intelligence (AI). Hybrid FEM-AI approaches, leveraging machine learning techniques like neural networks and deep learning, have emerged to enhance efficiency without sacrificing accuracy. These methodologies enable faster predictions, making them highly relevant for intelligent infrastructure systems and real-time structural health monitoring..

Seismic retrofitting of existing buildings has become crucial due to the vulnerability of infrastructure to earthquakes. Traditional retrofitting methods are costly and complex, prompting research into more efficient approaches. Artificial Intelligence (AI) has emerged as a transformative tool in this field, enhancing structural assessment, optimizing retrofit design, and improving seismic performance prediction. AI-driven methods, including machine learning, optimization algorithms, and data-driven modeling, have reduced reliance on expensive simulations while improving accuracy. However, challenges such as limited datasets, model interpretability, and integration with existing codes remain. AI represents a paradigm shift in seismic strengthening, promoting resilience and sustainability..

Self-healing concrete has emerged as a transformative innovation in sustainable structural engineering, addressing the challenges of durability degradation, cracking, and high maintenance costs in conventional cement-based materials. It utilizes advanced cementitious composites incorporating industrial by-products, chemical additives, and bio-based systems, such as bacteria-induced calcium carbonate precipitation, to autonomously repair cracks. Recent developments in multifunctional and intelligent concrete systems, integrating self-healing with sensing capabilities, have further enhanced structural resilience. Despite the promising benefits in reducing environmental impact and maintenance costs, challenges remain in large-scale implementation, microbial stability, and long-term performance validation..

The detection of cracks in reinforced concrete (RC) structures is crucial for ensuring the safety and longevity of infrastructure. Traditional methods of visual inspection are labor-intensive and prone to human error. Recent advancements in artificial intelligence (AI), machine learning (ML), and deep learning (DL) have revolutionized crack detection by enabling automated and accurate systems. Convolutional neural networks (CNNs) have proven highly effective in detecting fine crack patterns. Hybrid models and segmentation techniques further enhance accuracy, even under challenging conditions. Despite these advancements, challenges like dataset variability and environmental factors remain, urging continued research for more robust systems..

Concrete, a widely used construction material, faces challenges such as low tensile strength, brittleness, and long-term durability issues. These limitations have prompted advancements in cementitious materials, including smart concrete enhanced with nanomaterials. Nanomaterials like nano-silica, graphene oxide, and carbon nanotubes improve mechanical properties and durability, enhance resistance to cracking, and enable multifunctional capabilities. Smart concrete also offers self-sensing abilities for structural health monitoring. Despite challenges in nanoparticle dispersion and cost, nanotechnology has revolutionized concrete systems, ensuring sustainable, high-performance materials for modern infrastructure..

Earthquakes are destructive natural hazards that cause widespread structural damage, loss of life, and disruption to infrastructure. Rapid assessment of structural damage is essential for emergency response and rehabilitation. Traditional methods rely on field inspections, which are time-consuming and subjective. The integration of Remote Sensing (RS) and Geographic Information Systems (GIS) has revolutionized post-earthquake assessments, offering large-scale, real-time data through satellite imagery and UAV systems. Advanced machine learning models enhance damage detection, while multisource data fusion improves assessment reliability. Despite challenges, the future of earthquake damage evaluation lies in scalable, intelligent systems combining AI, RS, and GIS for real-time response..

The rapid growth of urban populations and vehicle ownership has intensified traffic congestion, making efficient traffic management crucial for sustainable urban development. Intelligent Transportation Systems (ITS) leverage technologies such as communication, sensing, and computation to optimize transportation. A core element of ITS is traffic flow prediction, which, through historical and real-time data, improves congestion management and route optimization. While traditional models like ARIMA struggled with nonlinear patterns, machine learning and deep learning, such as CNN, RNN, and LSTM networks, have enhanced accuracy. Recent advancements focus on improving scalability, real-time predictions, and the integration of external factors for greater accuracy..

Highway pavement systems play a crucial role in transportation infrastructure, influencing mobility, economic development, and sustainability. Recent studies emphasize the importance of assessing life cycle costs (LCC) and carbon footprints (CF) to ensure sustainable infrastructure. Findings highlight that carbon emissions from pavement construction and maintenance extend beyond initial phases, impacting environmental sustainability throughout the life cycle. Key challenges include underrepresented emissions during maintenance, material-intensive construction, and data inconsistencies in life cycle assessment (LCA) models. Addressing these issues requires better data integration, sustainable materials, and advanced tools to achieve accurate carbon footprint evaluations and support climate goals..

Road traffic accidents (RTAs) pose a significant global public health challenge, leading to substantial fatalities and injuries. Recent advancements in machine learning (ML) have facilitated better prediction and analysis of accident severity, addressing the limitations of traditional statistical methods. ML models, including Random Forest and deep learning techniques, outperform classical approaches by handling complex, nonlinear datasets. Moreover, GIS-based frameworks have enhanced the spatial analysis of accident hotspots, offering targeted intervention strategies. Despite these advances, challenges like real-time data integration and model interpretability remain, requiring further research to improve predictive accuracy and system adaptability..

This study explores the application of Artificial Neural Networks (ANNs) for predicting pavement performance and supporting maintenance planning. As traditional empirical and statistical models struggle to capture complex, nonlinear relationships in pavement deterioration, ANNs provide a more reliable, data-driven approach. The research highlights the superiority of ANNs over linear regression models in forecasting key performance indicators like the Pavement Condition Index (PCI) and International Roughness Index (IRI). Machine learning techniques such as deep learning and ensemble models further enhance the accuracy and robustness of pavement management systems. This methodology significantly improves predictive maintenance and long-term infrastructure planning..

The increasing demand for efficient transportation infrastructure necessitates the comparative analysis of flexible and rigid pavements under heavy traffic conditions. Flexible pavements, constructed with bituminous materials, distribute loads gradually through layered systems, whereas rigid pavements, made of Portland cement concrete, rely on slab action for load distribution. The performance of these pavements is influenced by factors like traffic load, environmental challenges, and material behavior. Studies highlight that while flexible pavements are cost-effective initially, rigid pavements offer better long-term durability, reduced maintenance, and superior structural stability under heavy traffic. These insights are crucial for optimizing pavement design and long-term infrastructure planning..

Urban transportation systems are vital for the socio-economic development of cities, but rapid urbanization has led to various challenges such as congestion and pollution. Geographic Information Systems (GIS) have emerged as a critical tool in addressing these issues by enabling the integration of spatial and non-spatial data for efficient transportation planning. GIS facilitates route design, accessibility evaluation, infrastructure optimization, and policy assessment, supporting sustainable urban mobility. The integration of emerging technologies like AI, IoT, and Digital Twins further enhances its capabilities, making transportation systems more efficient and environmentally sustainable. This framework aids in addressing transportation equity, operational accuracy, and environmental impacts..

Road traffic accidents (RTAs) represent a significant public safety issue globally. Traditional methods for identifying accident-prone areas (black spots) have been limited, relying on historical data and statistical techniques. Recent advancements in Machine Learning (ML) and Artificial Intelligence (AI) are revolutionizing predictive traffic safety systems. AI-driven methods offer predictive analytics, processing large-scale data sets to identify patterns in traffic flow, environmental factors, and driver behavior. Models like LSTM and Random Forest demonstrate high accuracy in forecasting accident hotspots. The integration of IoT and real-time vehicle data further enhances predictive accuracy, enabling timely interventions for accident prevention..

Flexible pavements are vital components of modern transportation infrastructure, offering adaptable and cost-efficient road networks. However, traditional bituminous binders often face challenges like rutting, cracking, and degradation due to fluctuating temperatures and increased traffic loads. To overcome these issues, bitumen modification using additives such as polymers, rubber, and nanomaterials has gained prominence. Waste materials, like plastic and crumb rubber, not only improve pavement performance but also contribute to environmental sustainability. Innovations like self-healing and semi-flexible pavements further enhance durability, while emerging technologies like graphene and nano-silica offer improved high-temperature performance. These advancements pave the way for more resilient and sustainable transportation systems..

Sustainable Road construction is a critical focus in mitigating environmental impacts caused by traditional materials like cement, aggregates, and bitumen. The use of industrial by-products such as fly ash, recycled plastics, and agricultural residues not only reduces carbon footprints but also enhances the mechanical properties of pavements. Research highlights the success of these materials in improving strength, durability, and load-bearing capacity while addressing environmental concerns. Life Cycle Assessment (LCA) and Building Information Modeling (BIM) tools are vital in optimizing sustainable practices in road infrastructure. This approach contributes to environmental sustainability and circular economy principles, ensuring long-term infrastructure resilience..

The selection of pavement systems plays a crucial role in transportation infrastructure, impacting economic efficiency, structural performance, and sustainability. Life Cycle Cost Analysis (LCCA) has emerged as a pivotal tool for optimizing pavement selection, considering not only initial costs but also long-term economic and environmental factors. This study compares flexible and rigid pavements, highlighting their performance under varying traffic and environmental conditions. Key considerations include maintenance costs, material behavior, and climate adaptability. The integration of sustainable materials further enhances pavement performance, providing a comprehensive framework for informed decision-making in pavement design and management..

The development of sustainable rural road infrastructure is crucial for enhancing accessibility and fostering socio-economic growth in developing regions. Challenges arise due to weak soils, such as expansive clays and loose granular deposits, which impede the load-bearing capacity of roads, causing premature failures. Soil stabilization techniques, including mechanical, chemical, and additive methods, improve soil strength, resistance to moisture, and stiffness, ensuring road durability. Alternative materials like industrial by-products—such as fly ash and GGBS—have gained attention for their environmental benefits. This study reviews various stabilization methods and their impact on mechanical properties, offering eco-friendly solutions for sustainable rural infrastructure..

Sustainable transportation infrastructure planning is increasingly critical in urban areas grappling with growing population density, rising vehicle ownership, and environmental challenges. Traditional approaches focusing solely on road expansion are insufficient. Instead, cities are shifting toward integrated transportation systems that emphasize environmental sustainability, economic efficiency, and social equity. Key innovations such as electric vehicles (EVs), micromobility systems, and autonomous vehicles (AVs) are transforming urban mobility. Additionally, the integration of advanced technologies like Geographic Information Systems (GIS), UAVs, and Artificial Neural Networks (ANN) is driving data-driven decision-making. Effective planning is crucial for reducing emissions, improving urban mobility, and fostering long-term resilience..

Urban transportation systems face growing challenges due to rapid urbanization, leading to severe traffic congestion. Traditional traffic signal control systems often fail to adapt to real-time traffic flow, contributing to travel delays, fuel waste, and high emissions. AI-based solutions such as Genetic Algorithms, Particle Swarm Optimization, and Reinforcement Learning have emerged to address these issues by optimizing traffic flow through adaptive signal timing. These intelligent systems can dynamically adjust signals, reducing congestion, improving road safety, and enhancing sustainability by minimizing travel time, fuel consumption, and carbon emissions. However, challenges such as real-time data requirements and integration with existing infrastructure remain..

The rapid growth of transportation infrastructure has increased the demand for durable, high-performance pavements. Traditional bituminous pavements face challenges like rutting, fatigue cracking, and moisture susceptibility. Waste plastic integration into bituminous mixes offers a sustainable solution, improving both performance and waste management. Plastics such as PET, PP, and PE enhance mechanical properties, moisture resistance, and rutting resistance. However, compatibility issues and low-temperature flexibility remain concerns. Research has focused on optimal plastic dosages and advanced techniques like machine learning for predicting asphalt mixture performance. These efforts support the adoption of plastic-modified asphalt for a sustainable future in pavement engineering..

This paper explores the integration of nano-silica in green concrete systems to improve sustainability and performance. By replacing ordinary Portland cement with industrial by-products like fly ash and slag, and incorporating nano-silica, concrete's mechanical and microstructural properties are enhanced. Nano-silica accelerates hydration, refines pore structure, and increases compressive strength. It also supports the circular economy by reducing waste, including recycled PET and glass, in concrete production. Despite challenges in cost and dispersion techniques, the combination of nano-silica and industrial waste offers promising solutions for eco-efficient, high-performance concrete..

Building Information Modeling (BIM) is revolutionizing the Architecture, Engineering, and Construction (AEC) industry by integrating various dimensions such as geometry, structure, time (4D), cost (5D), and lifecycle information into a unified digital framework. This approach significantly enhances collaboration, reduces inefficiencies, and optimizes project outcomes. BIM’s role in sustainability, resource optimization, and design efficiency is well-documented, with AI integration and advanced computational techniques enhancing its potential. Moreover, BIM’s integration with innovative materials and sensor-based systems is transforming construction, performance enhancement, and infrastructure management, contributing to more sustainable, cost-effective, and efficient construction practices..

The integration of Building Information Modeling (BIM) and Finite Element Method (FEM) is transforming structural design, offering enhanced precision, sustainability, and efficiency. BIM serves as a comprehensive platform for integrating architectural, structural, and construction data, while FEM simulates structural behavior under diverse conditions. Together, these technologies streamline design workflows, reduce environmental impact, and optimize material usage. Recent studies highlight the significant benefits of this integration, especially in minimizing embodied carbon and enhancing early-stage design optimization. The convergence of BIM and FEM, coupled with emerging AI techniques, signifies a shift towards data-driven, performance-based engineering for sustainable infrastructure development..

With the rapid growth of network traffic driven by various applications, traditional network traffic classification methods like port-based and deep packet inspection (DPI) have become ineffective. As encrypted traffic surges, machine learning (ML) and deep learning (DL) models have emerged as promising solutions. Techniques such as CNN, LSTM, and hybrid models have demonstrated significant improvements in traffic classification accuracy and network management. This study explores the use of ML in traffic classification for enhancing network security, scalability, and adaptability while addressing challenges such as encryption and concept drift in dynamic environments..

The study of stress, fatigue, and failure behavior in mechanical components is crucial in engineering design, especially for lightweight and high-performance structures in industries like aerospace, automotive, and biomedical. Components under cyclic loading and thermal gradients are prone to progressive damage, often leading to fatigue failure. Traditional experimental methods are costly and time-consuming, making numerical simulations, such as finite element analysis (FEA), essential for efficient design optimization. The integration of additive manufacturing (AM) with simulation tools further enhances the design process by considering anisotropic behavior, residual stress, and microstructural effects, improving overall structural integrity and fatigue resistance..

Steel truss bridges, known for their high strength-to-weight ratio, face significant challenges from repeated traffic loading, environmental exposure, and dynamic stress, leading to potential fatigue failure. Fatigue damage in welded joints is especially critical, as they experience stress concentration and microstructural discontinuities. Recent studies, including those by Ye et al. (2026) and Giannella et al. (2025), emphasize the importance of advanced predictive models and fracture mechanics approaches to predict crack propagation. Despite improvements in experimental and computational methods, challenges persist, particularly with dissimilar welded joints and environmental effects. Understanding these factors is crucial for enhancing the durability and safety of steel truss bridges..

This paper explores the transformation in structural engineering through the integration of artificial intelligence (AI) with traditional methods such as Finite Element Analysis (FEA). It examines how AI-driven techniques like machine learning (ML), deep learning, and hybrid optimization are enhancing the accuracy, efficiency, and decision-making processes in structural analysis, health monitoring, and predictive maintenance. With examples from various case studies, the research highlights AI's potential to address challenges in complex, large-scale systems and its application in infrastructure resilience, particularly in seismic-prone regions. The study underscores AI's growing importance in modern structural engineering..

The rapid integration of digital technologies in civil engineering has led to the widespread adoption of digital twins, enhancing structural safety, performance, and lifecycle management. This approach integrates real-time monitoring, simulation tools, and predictive analytics to assess and optimize civil engineering systems. Studies show that machine learning, finite element modeling, and sensor networks contribute significantly to improving structural reliability, particularly in seismic and material performance. Despite advancements, there remains a need for a unified framework to streamline real-time data acquisition, modeling, and decision-making processes. This research focuses on developing a comprehensive digital twin-based evaluation system for structural performance..

Steel plate girder bridges are integral to modern transportation networks due to their high strength-to-weight ratio and versatility for medium to long-span applications. However, these bridges are under increasing stress due to rising traffic loads, environmental degradation, and aging infrastructure. To address these challenges, finite element modelling (FEM) has proven to be a critical tool in simulating the complex structural behavior of steel plate girder bridges under dynamic loads and varying environmental conditions. Advanced FEM techniques have enhanced the design, performance optimization, and durability predictions of these structures, ensuring their long-term serviceability and safety under demanding conditions..

The rehabilitation of reinforced concrete (RC) structures using fiber-reinforced polymer (FRP) composites has become a crucial advancement in modern structural engineering, particularly in extending service life and load-carrying capacity. FRP systems, such as externally bonded reinforcement (EBR) and near-surface mounted (NSM) techniques, have demonstrated significant improvements in flexural, shear, and axial performance. These systems also enhance seismic retrofitting by improving column ductility and compressive strength. However, challenges remain, such as debonding failure, durability issues, and performance under extreme temperatures, emphasizing the need for continued research on improving material behavior, long-term performance, and predictive models..

Bridge piers in tidal and coastal environments face complex hydraulic forces, leading to local scour, which erodes sediment around foundations. Scour intensity is influenced by nonlinear interactions among waves, currents, tides, and wind, as well as pier geometry, debris accumulation, and environmental factors like salinity and ice cover. Traditional empirical models often fail to predict these dynamics accurately. Recent advancements include structural countermeasures—riprap, collars, and sacrificial vanes—and predictive modeling using machine learning algorithms such as XGB with SHAP interpretation. Integrating multi-physics interactions remains a challenge, requiring further research for adaptive, reliable scour mitigation..

This paper explores the evolution of digital communication systems and the role of MATLAB-based modeling in simulating advanced communication networks. The increasing demand for high-speed, reliable, and efficient transmission networks necessitates the use of simulation tools like MATLAB to analyze performance under various channel conditions. The integration of next-generation technologies such as 5G, 6G, and digital twins is also examined. The study emphasizes the convergence of artificial intelligence with communication design, improving system accuracy, security, and reliability. Furthermore, advancements in secure edge intelligence, FPGA-based error correction, and energy-efficient communication are explored..

Orthogonal Frequency Division Multiplexing (OFDM) has emerged as a leading modulation technique in modern digital communication systems, owing to its ability to combat frequency-selective fading and support high data rates in bandwidth-constrained environments. However, noisy channel conditions such as Doppler shifts, frequency offsets, and inter-carrier interference (ICI) significantly degrade the performance of OFDM systems. Advanced techniques like deep learning for joint channel estimation and reconfigurable intelligent surfaces (RIS) have been proposed to enhance system robustness under harsh conditions. These innovations are critical for ensuring reliable communication in terrestrial, satellite, and underwater acoustic networks..

The integration of machine learning (ML) and artificial intelligence (AI) in electrical power system fault detection has revolutionized the field by enhancing diagnostic accuracy and operational resilience. Traditional protection systems, primarily based on rule-based relays, struggle with nonlinear behaviors and evolving grid structures. ML techniques such as decision trees, support vector machines, and deep learning have proven effective in improving fault detection and classification by analyzing complex power system data. However, challenges like class imbalance, model interpretability, and environmental uncertainties remain. Despite these obstacles, ML-based models represent a robust solution for intelligent, adaptive, and scalable fault detection in future smart grids..

Electrical load forecasting is essential for efficient power system planning, smart grid operation, and reliable energy management. With increasing renewable energy integration, electric vehicles, industrial automation, and variable demand patterns, accurate forecasting has become more important. Traditional statistical models often fail to capture nonlinear and dynamic electricity consumption trends. Therefore, artificial intelligence and machine learning techniques such as ANN, LSTM, GRU, CNN, and hybrid attention-based models offer improved forecasting accuracy. By integrating weather, calendar, industrial, and socio-economic data, modern forecasting systems enhance prediction reliability. However, challenges such as data quality, interpretability, and real-time implementation still require further research..

Modern power systems are becoming increasingly complex due to renewable energy integration, smart grids, inverter-based resources, and variable load demand. Artificial intelligence-based predictive models offer effective solutions for stability analysis by improving forecasting, monitoring, and real-time control. Machine learning, deep learning, reinforcement learning, and digital twin technologies help identify transient stability, voltage instability, frequency deviation, and dynamic grid interactions. These models enhance reliability, adaptability, and operational efficiency in modern energy networks. However, challenges such as data quality, cybersecurity, interpretability, and scalability still require further research. Overall, AI-based predictive models support resilient, intelligent, and sustainable power system operation. .

Urban transportation demand forecasting has become essential for smart city planning due to rapid urbanization, congestion, and increasing mobility needs. Traditional statistical models often fail to capture complex, nonlinear, and real-time travel patterns. Therefore, artificial intelligence techniques such as machine learning, deep learning, graph neural networks, IoT-based models, and federated learning offer more accurate and adaptive forecasting solutions. These approaches improve passenger and freight demand prediction, support traffic management, reduce travel uncertainty, and enhance sustainable mobility planning. However, issues related to data privacy, interpretability, scalability, and real-time deployment require further research. .

Economic Load Dispatch (ELD) plays a vital role in modern power system operation by determining optimal generation schedules at minimum cost while satisfying system constraints. With increasing electricity demand, renewable energy integration, and complex grid conditions, traditional optimization methods often become insufficient. Recent advancements in artificial intelligence, machine learning, and metaheuristic algorithms have improved ELD performance by enhancing convergence, accuracy, and adaptability. Techniques such as PSO, GA, deep learning, and hybrid optimization support efficient load forecasting and dispatch decisions. Modern ELD also considers emissions, reliability, and sustainability, making it essential for smart grid operation and future energy management..

AI-based smart manufacturing systems have transformed industrial production by integrating artificial intelligence, robotics, IIoT, cyber-physical systems, digital twins, and big data analytics. This study focuses on the role of AI in improving productivity, operational accuracy, predictive maintenance, quality inspection, anomaly detection, and energy efficiency within Industry 4.0 environments. Smart manufacturing enables machines to function as intelligent, data-driven systems capable of self-monitoring, adaptive decision-making, and real-time optimization. Although challenges such as legacy infrastructure, workforce resistance, and implementation complexity remain, AI-driven manufacturing offers significant potential for sustainable, competitive, and efficient industrial transformation..

Pressure vessels are essential engineering components used for storing and transporting fluids under high pressure and temperature conditions. Their safe design is crucial because failure may cause severe economic, environmental, and human losses. This study focuses on the design optimization of pressure vessels using Finite Element Analysis and computational optimization techniques. FEA helps identify stress distribution, deformation, and critical failure zones, while optimization methods improve material efficiency, reduce weight, and enhance structural performance. The study emphasizes the importance of integrating simulation, material behavior, and optimization algorithms to develop safe, cost-effective, and high-performance pressure vessel systems. .

Computer Numerical Control (CNC) machining plays a vital role in modern manufacturing by improving precision, repeatability, productivity, and surface quality. However, CNC processes face challenges such as high energy consumption, tool wear, vibration, noise emission, and machining inaccuracies. This study focuses on optimizing CNC machining parameters using advanced optimization techniques and artificial intelligence-based approaches. The research considers key performance indicators such as cutting speed, feed rate, depth of cut, surface roughness, energy use, and process stability. The study supports sustainable and intelligent manufacturing by balancing productivity, quality, cost-effectiveness, and environmental performance. .

Concrete structures are prone to cracks due to loading, shrinkage, corrosion, thermal stress, seismic effects, and environmental deterioration. Manual crack inspection is often time-consuming, subjective, and inconsistent. This study focuses on automated concrete crack detection using digital image processing and artificial intelligence techniques. Methods such as image enhancement, edge detection, segmentation, feature extraction, convolutional neural networks, U-Net, YOLO-based models, UAV imaging, and digital image correlation improve crack identification, classification, and damage assessment. These intelligent systems support faster, safer, and more accurate structural health monitoring, helping engineers reduce maintenance costs and improve the durability and safety of civil infrastructure. .

This study examined the structural performance of reinforced concrete (RC) buildings under modern loading, durability, and sustainability challenges. It focused on the use of advanced simulation tools, finite element modelling, machine learning methods, and experimental validation to predict seismic response, stiffness degradation, drift, corrosion effects, and fire-induced damage. The study also highlighted the importance of sustainable materials such as plastic waste, recycled glass, fiber-reinforced composites, stainless steel reinforcement, nanomaterials, and self-healing concrete in improving durability and reducing environmental impact. Overall, integrated digital and material-based approaches were found essential for resilient RC building design. .

This study examines the sustainable use of industrial waste materials in flexible pavement construction to improve performance and reduce environmental impact. Industrial by-products such as ceramic waste powder, crumb rubber, marble dust, fly ash, and construction and demolition waste can enhance pavement strength, durability, rutting resistance, and subgrade stability. Their use also reduces dependence on natural aggregates, lowers landfill disposal, and supports circular economy practices. Although challenges such as material variability, lack of standardization, and limited long-term performance data remain, industrial waste-based pavements offer an effective approach for developing economical, durable, and environmentally sustainable transportation infrastructure. .

This study examines the comparative evaluation of pavement design methods for improving structural performance in modern transportation infrastructure. It highlights the limitations of traditional empirical methods and the advantages of mechanistic–empirical approaches in predicting pavement distress, load response, and long-term durability. The study considers key factors such as traffic loading, subgrade strength, climatic conditions, material properties, sustainability, and life cycle cost. Advanced tools such as FWD, TSDD, LCA, and performance-based evaluation support more reliable pavement assessment. The study emphasizes the need for optimized design strategies to enhance durability, cost efficiency, and sustainable pavement performance..

Transportation energy consumption modeling is essential for developing sustainable and efficient mobility systems. The growing demand for passenger and freight transport has increased fossil fuel use, carbon emissions, and environmental concerns. This study focuses on predicting and optimizing energy consumption in transportation through mathematical, statistical, simulation, and machine learning-based approaches. It highlights the role of digitalization, intelligent transport systems, renewable energy integration, and policy planning in reducing emissions and improving efficiency. The study emphasizes that sustainable transport requires advanced modeling, cleaner energy adoption, and integrated planning to balance mobility needs with environmental protection and long-term economic development. .

Geographic Information Systems (GIS) play a vital role in transportation infrastructure analysis and regional accessibility improvement. GIS supports spatial data integration, network analysis, route optimization, accident hotspot identification, and infrastructure performance evaluation. It helps planners and policymakers understand transport connectivity, accessibility patterns, and regional development needs more effectively. By combining GIS with modern technologies such as remote sensing, GPS, AI, IoT, and big data analytics, transportation planning becomes more accurate, sustainable, and data-driven. Therefore, GIS-based spatial analysis is an essential tool for improving mobility, safety, infrastructure management, and balanced regional growth. .

This study focuses on the performance evaluation of reinforced concrete structures using nano-modified concrete materials. Nanomaterials such as nano-silica, graphene oxide, carbon nanotubes, nano-alumina, and nano-titanium dioxide improve concrete strength, durability, pore refinement, hydration, and resistance against chloride penetration and corrosion. The study highlights the role of nano-modified materials in enhancing mechanical properties, bond strength, retrofitting performance, and long-term structural reliability. It also emphasizes sustainability through reduced cement consumption, waste utilization, and improved service life. Despite challenges such as cost, dispersion, and standardization, nano-modified concrete offers strong potential for durable and sustainable infrastructure development. .

Highway capacity analysis and Level of Service (LOS) evaluation are essential for understanding roadway performance under increasing traffic demand. Rapid urbanization, vehicle growth, and mixed traffic conditions have created congestion, delay, and safety issues on highways and urban corridors. This study focuses on the use of traffic simulation models for evaluating highway capacity and LOS more realistically than conventional analytical methods. Simulation tools help analyse vehicle movement, delay, density, speed, and queue formation under different traffic scenarios. The study highlights the importance of simulation-based approaches for improving traffic management, reducing congestion, and supporting sustainable transportation planning. .

This study examines the application of fiber reinforced polymer (FRP) and smart materials for retrofitting reinforced concrete structures. Existing RC structures often deteriorate due to corrosion, aging, seismic forces, fatigue loading, poor construction practices, and outdated design standards. FRP composites offer high strength, lightweight properties, corrosion resistance, and ease of installation, making them effective for flexural, shear, and seismic strengthening. The integration of smart materials, structural health monitoring systems, piezoelectric sensors, and computational techniques further improves damage detection, performance evaluation, and service life. Thus, FRP and smart materials provide sustainable solutions for safe infrastructure rehabilitation. .

This study focuses on the application of optimization algorithms for achieving cost-effective structural design in modern civil engineering. Structural optimization helps reduce material consumption, construction cost, structural weight, and lifecycle expenses while maintaining safety, serviceability, and code compliance. Advanced techniques such as Genetic Algorithm, Particle Swarm Optimization, Differential Evolution, Artificial Neural Network, Simulated Annealing, and hybrid metaheuristic methods provide efficient solutions for complex structural problems. These algorithms are useful in reinforced concrete structures, steel systems, seismic design, retrofitting, and sustainable construction. Overall, optimization-based design improves economic efficiency, structural performance, reliability, and sustainability in future infrastructure development. .

The deterioration of reinforced concrete structures has increased the need for effective retrofitting and strengthening methods. Conventional techniques such as steel jacketing, epoxy injection, external post-tensioning, and FRP wrapping improve load capacity, ductility, and seismic resistance, but they are mainly reactive and require post-damage intervention. Recent advancements in fiber-reinforced polymer composites, nanomaterials, smart sensors, and self-healing mechanisms have introduced more intelligent and sustainable solutions. Self-healing FRP systems can repair microcracks, reduce maintenance needs, and improve long-term durability. Therefore, comparing conventional retrofitting with self-healing FRP systems is essential for developing resilient and smart infrastructure. .

Transportation network optimization is essential for improving mobility, reducing congestion, minimizing travel cost, and supporting sustainable urban development. This study focuses on the application of Genetic Algorithms for solving complex transportation problems such as routing, scheduling, traffic distribution, and network design. Genetic Algorithms are effective because they can handle nonlinear, dynamic, and multi-objective conditions through selection, crossover, and mutation processes. The study highlights recent GA-based approaches, including hybrid models, real-time routing, and environmentally sustainable optimization. Overall, Genetic Algorithms provide an adaptive and efficient framework for enhancing transportation network performance in modern intelligent transportation systems..

Stone Matrix Asphalt (SMA) is an advanced pavement material designed to improve the performance of flexible pavements under heavy traffic loading. Its gap-graded aggregate structure, high binder content, mineral filler, and stabilizing additives provide excellent resistance to rutting, fatigue cracking, moisture damage, and deformation. Recent developments in SMA include the use of recycled materials, polymers, fibers, nano-silica, crumb rubber, and industrial waste to enhance durability and sustainability. SMA also contributes to noise reduction, thermal control, and long-term pavement serviceability. Therefore, SMA offers a reliable, sustainable, and high-performance solution for modern heavy traffic pavement construction..

Urban transportation systems play a vital role in supporting mobility, accessibility, and economic development, but they also contribute significantly to environmental degradation. Rapid urbanization, increasing private vehicle use, traffic congestion, and fuel-based transport have intensified air and noise pollution in cities. Emissions such as PM₂.₅, NOx, CO, and CO₂ reduce air quality, while vehicle engines, tire friction, and congestion increase harmful noise exposure. Sustainable solutions such as public transport, electric mobility, micro-mobility, non-motorized transport, and intelligent transportation systems can help reduce these impacts. Therefore, environmental assessment is essential for developing healthier and sustainable urban transport policies..

The increasing integration of renewable energy sources into modern power systems has introduced significant challenges in maintaining power quality, frequency stability, and system resilience due to the intermittent and variable nature of solar, wind, and hydropower generation. Machine learning and deep learning techniques, combined with energy storage systems and optimization-based control strategies, have emerged as effective tools for real-time monitoring, predictive analytics, and uncertainty mitigation. Advanced scenario-based modeling and AI-enabled controllers enhance operational efficiency, reliability, and sustainability. This review highlights the latest methodologies, challenges, and future directions for achieving intelligent, resilient, and low-carbon power systems..