Insights Sustainable Engineering Practices
The Insights Sustainable Engineering Practices (ISEP) is Published by QUEST RESEARCH AND EDUCATION (SMC-PRIVATE) LIMITED (Frequency Semiannual) leading open-access, peer-reviewed journal dedicated to advancing the field of sustainable engineering. ISEP serves as a platform for interdisciplinary research, showcasing innovative engineering practices that address the pressing challenges of environmental sustainability. The journal aims to provide high-quality, actionable insights that support the global transition to sustainable engineering solutions across various sectors. Founded with the mission to promote sustainable development, ISEP publishes research on the application of engineering principles to create environmentally responsible and resource-efficient systems. The journal focuses on the practical aspects of sustainable engineering, encouraging research that offers real-world solutions to the most critical sustainability issues facing industries and communities. Journal Disciplines Sustainable Design & Engineering Sustainable design and development in engineering systems, life cycle analysis, and resource optimization for long-term environmental and industrial performance. Renewable Energy & Green Construction Renewable energy technologies and their integration, green building practices, and eco-efficient construction for resilient and low-impact development. Industrial Sustainability & Environment Waste management and recycling technologies, sustainable manufacturing and industrial processes, and environmental impact assessments with mitigation strategies. Smart Infrastructure, Policy & Society Smart cities and sustainable infrastructure, along with policy, regulation, and social aspects of sustainable engineering and development.
QUEST RESEARCH AND EDUCATION (SMC-PRIVATE) LIMITED
EXPLAINABLE DEEP LEARNING WITH MULTIMODAL SENSOR FUSION FOR BRIDGE STRUCTURAL HEALTH MONITORING
Structural health monitoring (SHM) of bridges is essential for ensuring public safety, reducing maintenance costs, and extending the service life of critical transportation infrastructure. Traditional bridge inspection methods depend heavily on periodic visual assessment, which can be time-consuming, subjective, costly, and unable to detect early-stage structural deterioration. This paper presents an AI-based SHM framework that integrates multimodal sensor fusion and explainable deep learning for reliable bridge damage detection and condition assessment. The proposed approach combines heterogeneous sensor data, including vibration signals, strain measurements, displacement readings, acoustic emission responses, and environmental variables, to generate a comprehensive representation of bridge behavior under operational conditions. Deep learning models are used to identify hidden damage patterns, classify structural states, and predict abnormal responses with improved accuracy compared with single-sensor monitoring methods. To enhance model transparency, explainable artificial intelligence techniques are incorporated to identify the most influential sensor channels, structural features, and decision regions contributing to damage predictions. The results show that multimodal fusion improves classification performance, reduces false alarms, and strengthens robustness under noise and environmental variability. Explainability outputs further support engineering interpretation by linking model decisions to physically meaningful indicators such as strain variation, modal frequency shift, and vibration intensity. Overall, the study demonstrates that AI-driven multimodal SHM can provide an effective, interpretable, and scalable solution for bridge monitoring, supporting early warning, preventive maintenance, and data-informed infrastructure management.
Structural health monitoring; bridge damage detection; multimodal sensor fusion; explainable deep learning; predictive maintenance
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
STRUCTURAL BATTERY COMPOSITES: DESIGN OPTIMIZATION, SAFETY, AND LIFE-CYCLE PERFORMANCE FOR LIGHTWEIGHT ELECTRIC MOBILITY
Structural battery composites are emerging as a transformative technology for lightweight electric mobility by combining mechanical load-bearing capability with electrochemical energy storage in a single multifunctional material system. This study investigates the design optimization, safety performance, and life-cycle implications of structural battery composites for electric vehicles and lightweight mobility platforms. The proposed analysis focuses on balancing energy density, stiffness, strength, thermal stability, cycle retention, and environmental performance across different composite configurations. The results indicate that optimized multifunctional laminate architectures can reduce system-level mass while maintaining sufficient mechanical integrity and stable electrochemical behavior under repeated loading and cycling conditions. Improved fiber–matrix integration, controlled electrolyte distribution, and optimized electrode alignment were found to enhance both structural performance and energy storage efficiency. Safety evaluation further showed that thermal response, damage tolerance, and failure propagation are critical design factors for practical deployment in mobility applications. From a life-cycle perspective, structural battery composites demonstrate potential reductions in vehicle mass, operational energy demand, and carbon emissions; however, recyclability, material recovery, and manufacturing energy remain important barriers. Overall, the findings suggest that structural battery composites can contribute significantly to next-generation electric mobility if design, safety, and sustainability requirements are addressed together rather than independently.
Structural battery composites; Lightweight electric mobility; Multifunctional materials; Design optimization; Life-cycle performance
MULTIFUNCTIONAL STRUCTURAL BATTERY COMPOSITES FOR ELECTRIC MOBILITY: DESIGN, SAFETY, AND SUSTAINABLE LIFE-CYCLE PERFORMANCE
Structural battery composites are emerging as a promising multifunctional technology for lightweight electric mobility because they combine mechanical load-bearing capability with electrochemical energy storage in a single material system. This paper examines the design optimization, safety behavior, and life-cycle performance of structural battery composites for electric vehicle applications. The study highlights how carbon-fiber-reinforced electrodes, solid or semi-solid electrolytes, and multifunctional laminate architectures can reduce redundant vehicle mass while improving energy efficiency. The results indicate that optimized structural battery configurations can provide meaningful reductions in vehicle weight, improved stiffness-to-weight performance, and enhanced system-level efficiency compared with conventional battery pack integration. However, trade-offs remain between mechanical strength, ionic conductivity, energy density, impact resistance, and long-term durability. Safety analysis shows that damage tolerance, thermal stability, and electrolyte containment are critical factors for reliable adoption in mobility systems. Life-cycle assessment further suggests that structural batteries can reduce operational emissions through lightweighting benefits, although material sourcing, recycling complexity, and end-of-life separation must be carefully addressed. Overall, the findings demonstrate that structural battery composites can support the transition toward more efficient and sustainable electric mobility when material design, safety validation, and circular life-cycle strategies are integrated from the early development stage.
Structural battery composites; lightweight electric vehicles; multifunctional materials; design optimization; life-cycle performance
AI-ENABLED DIGITAL TWINS FOR PREDICTIVE MAINTENANCE IN INDUSTRY 5.0 MANUFACTURING
Industry 5.0 manufacturing systems require maintenance strategies that are not only efficient and intelligent but also human-centric, resilient, and sustainable. This paper examines the role of AI-enabled digital twin predictive maintenance in improving asset reliability, reducing operational downtime, and supporting autonomous decision-making in advanced manufacturing environments. By integrating real-time sensor monitoring, machine learning-based fault prediction, remaining useful life estimation, and virtual asset simulation, the proposed digital twin framework enables continuous assessment of equipment health and proactive maintenance planning. The results indicate that AI-enabled digital twins can significantly improve fault detection accuracy, maintenance response time, energy efficiency, and cost optimization compared with conventional preventive and reactive maintenance approaches. The framework also supports Industry 5.0 priorities by strengthening collaboration between human operators and intelligent systems, enhancing explainability in maintenance decisions, and minimizing unnecessary machine stoppages. Overall, the study demonstrates that predictive maintenance powered by AI and digital twin technology can serve as a critical foundation for smart, adaptive, and sustainable manufacturing systems.
AI-enabled digital twin; predictive maintenance; Industry 5.0; smart manufacturing; remaining useful life prediction
AGENTIC DIGITAL TWINS INTEGRATING LARGE LANGUAGE MODELS FOR AUTONOMOUS SMART GRID MANAGEMENT
Smart grid energy management is increasingly shifting from conventional rule-based automation toward intelligent, adaptive, and autonomous decision-making systems. This paper examines the role of agentic digital twins in smart grid energy management by integrating large language models, forecasting techniques, and autonomous control mechanisms. The proposed perspective highlights how digital twins can represent real-time grid behavior, simulate future operating conditions, and support proactive decisions across demand response, renewable energy integration, load balancing, fault detection, and operational resilience. By embedding LLM-based reasoning into digital twin environments, smart grids can move beyond static monitoring toward context-aware coordination between human operators, distributed energy resources, and automated control agents. Forecasting models improve the prediction of electricity demand, renewable generation variability, and grid congestion, while autonomous control modules enable timely corrective actions under uncertain operating conditions. The results indicate that agentic digital twins can improve forecasting accuracy, reduce peak load stress, enhance renewable utilization, lower operational latency, and strengthen grid resilience. However, effective implementation requires careful attention to data quality, cybersecurity, explainability, regulatory compliance, and human oversight. The study concludes that agentic digital twins offer a promising framework for next-generation smart grids, where predictive intelligence and autonomous control work together to support reliable, sustainable, and efficient energy systems.
Agentic digital twins; Smart grid; Large language models; Energy forecasting; Autonomous control
SCALABLE SDN-BASED ARCHITECTURE FOR LARGE-SCALE ENTERPRISE NETWORK MANAGEMENT
Software-Defined Networking (SDN) has emerged as a transformative paradigm for enterprise network management, offering centralized control and programmability. However, existing SDN architectures face significant scalability challenges when deployed in large-scale enterprise environments with thousands of network devices and dynamic traffic patterns. This study proposes a novel hierarchical SDN architecture incorporating distributed controller clusters, dynamic load balancing, and optimized flow management mechanisms to address these limitations. We implemented and evaluated the proposed architecture using Mininet emulation with Fat-Tree topologies ranging from 16 to 128 hosts. Performance evaluation demonstrates that the proposed architecture achieves a 42% reduction in flow setup latency (5.2 ms vs. 8.5 ms), 18% improvement in throughput (850K vs. 720K flows/sec), and 65% reduction in packet loss compared to traditional single-controller SDN deployments. Statistical analysis using ANOVA confirmed significant performance improvements (p < 0.001) across all metrics. These findings establish that hierarchical SDN architectures with intelligent load balancing provide a viable solution for scalable enterprise network management, enabling organizations to leverage SDN benefits in production environments without compromising performance or reliability.
Copyright (c) 2023 I K M SAAMEEN YASSAR (Author) https://creativecommons.org/licenses/by/4.0
SMART TRANSPORTATION SYSTEMS FOR URBAN MOBILITY AND CONGESTION REDUCTION
The city jams and commuting in cities worldwide have become a nightmare due to rapid urbanization and the increased number of cars. This paper will analyse how smart transportation systems can be effective in improving the movement and reducing congestion within a city by implementing intelligent technologies. The paper evaluates the impacts of adaptive traffic control, real-time traffic monitoring, smart parking, and smart public transit management on the basis of traffic performance indicators, system efficiency metrics, and environmental results. The findings indicate that intelligent transport systems contribute greatly to efficiency and reduce travel time on the average and congestion indices along the urban routes. The increase in the effectiveness of the public transport and the ability to predict the schedule had made people start using it more and a definite shift toward not driving their own vehicles. The findings also indicate that the decrease in the use of fuel and greenhouse gases is a consequence of a smoother flow of traffic and the choice of routes. The IoT sensors and analytics platforms which allow you to make decisions based on the data mattered a lot as far as proactive congestion control and long-term planning of the transportation in the cities are concerned. The research establishes that system integration issues, data protection as well as costs of implementation have challenges, although the gains are enormous. In general, the findings demonstrate that intelligent transport systems are a potent and durable solution to the challenges of contemporary city mobility. They embrace efficient, environment-friendly, and service-oriented transportation systems.
Copyright (c) 2025 Ahmed Saeed, Laiba Hassan (Author) https://creativecommons.org/licenses/by/4.0
ENGINEERING INNOVATIONS IN WATER PURIFICATION SYSTEMS FOR SAFE DRINKING WATER IN DEVELOPING COUNTRIES
Most of the under-developed countries face a monumental problem of access to safe drinking water as a result of poor infrastructure, inaccessibility of finances and environmental demands. The paper will also cover the future of the engineering studies in the water purifying systems to address these challenges with economical, efficient, and sustainable systems. The findings have shown that superior filter material, membrane-based purification systems and hybrid systems of treating water significantly enhance the quality of the water as they have been shown to effectively eliminate such impurities like biological, chemical and physical impurities. The performance tests had proved that the system was very efficient in cleaning and used less energy and was easily accommodated in an off-grid and decentralized system. The prospects of solar powered and gravity driven were especially good in minimizing the cost of operation, and keeping water in short supply never existed in the remote regions. The socio-economic assessments demonstrated the effectiveness of community-based deployment methods to increase the system acceptance, service, and sustainability. In general, the article shows that the discrepancy between the technological ability and the actual possibility of what can be done in the real world can be closed by the new engineering methods that will be able to provide the scaling solution to the issue of the water shortage and contamination. These results show that it is valuable to combine engineering, policy, and community-based solution in order to provide safe drinking water to everyone and promote the goals of enhancing the population health and sustainable development.
Copyright (c) 2025 Kashif Mehmood, Sadia Noor (Author) https://creativecommons.org/licenses/by/4.0
ADVANCED ROBOTICS IN CONSTRUCTION: APPLICATIONS IN PREFABRICATION AND MODULAR BUILDING TECHNIQUES
High level robotics has also become an important element in the prefabrication and modular construction. It offers the solutions to the old issues of productivity, quality, safety, and sustainability. This paper evaluated the feasibility and impacts of robotic technology in critical stages of the modular assembly, including production of components, assembly, transportation and construction on site. The findings demonstrate that prefabricated elements are much more precise in terms of size, less time consuming to construct, and more predictable with the help of robotic automation as compared to using the traditional methods. Quantitative outcomes are that the intensity of labor, material waste and operational errors have decreased whereas the qualitative outcomes are that the safety and reliability of workflow has increased as well. Robotics combined with such digital construction platforms as BIM and real-time monitoring technologies can further enhance the coordination and optimization of the process. Along economic analysis, the long-term benefits outweigh the expenditures on investments made at the initial stage because of improved efficiency and reduced life cycle costs. In general, the analysis demonstrates that the field of advanced robotics is significant to make the modular construction scalable, high quality, and sustainable. This renders it a critical technology to the future of industrialized building systems.
Copyright (c) 2025 Hassan Raza, Mehwish Anwar (Author) https://creativecommons.org/licenses/by/4.0
THE ROLE OF NANOTECHNOLOGY IN ENHANCING THE DURABILITY AND PERFORMANCE OF CONSTRUCTION MATERIALS
Nanotechnology is an emerging technology that has become a new opportunity in the construction industry offering new opportunities to prepare materials in better and more long-lasting ways. This paper looks at the implications of the changes introduced by nanotechnology to conventional construction materials in terms of mechanical strength, durability and the overall performance. The experimental results indicated that incorporation of nanomaterials such as nano-silica, nano-titania, carbon nanotubes and graphene exhibited remarkable enhanced compressive, tensile, and flexural strengths due to the increase in microstructural density and bonds between interfaces. It was also revealed that the porosity, water absorption, and permeability were significantly decreased which increased the resistance of the material to chemical attack, chloride penetration, and environmental damage. Functional properties such as self-cleaning, enhanced thermal conductivity and resistance to microbial growth were also functional in nanomodified materials. It can be seen that the outcomes of nanotechnology do not only enhance the strength and durability of building material but it is also environmentally friendly, as it enables the material to last longer and require less maintenance. It is shown in the research that construction materials based on nanotechnology have a great potential in the development of resilient high performance and sustainable infrastructure.
Copyright (c) 2025 Zain Ul Abidin, Iqra Mahmood (Author) https://creativecommons.org/licenses/by/4.0
AI-BASED STRUCTURAL HEALTH MONITORING SYSTEMS: IMPROVING SAFETY AND REDUCING MAINTENANCE COSTS IN HIGH-RISE BUILDINGS
The rapid growth of high-rise buildings in urban areas has rendered the need to identify reliable, effective and inexpensive methods of managing structural safety a greater priority. The article under consideration considers how AIs-based Structural Health Monitoring (SHM) can be used to provide better safety and reduce maintenance costs in high-rise buildings. Meanwhile, the structural problems were discovered with the help of advanced machine learning and deep learning algorithms, how bad the damage was was determined, and how the structure is thought to work in the future was guessed. This was performed based on information of distributed sensors which recorded the vibration, strain, displacement and the environmental factors. It can be seen that AI-based SHM systems are significantly superior to old-fashioned manual inspection and threshold-based monitoring in detecting the presence of the early signs of deterioration and unusual behaviour of buildings. The predictive analytics allowed performing repair work in time, thus reducing unnecessary inspections, maintenance bills, and the risk of a structural collapse. It was also demonstrated by the system that it could easily respond to various load conditions and environmental conditions and this indicated that it fitted in complex high rise buildings. In general, the analysis demonstrates that AI-based SHM is a powerful, adaptable, and intelligent method of making modern high-rise buildings safer, allowing people to make decisions grounded on information, and reducing the costs of maintaining a building throughout its service.
Copyright (c) 2025 Usman Tariq, Areeba Khan (Author) https://creativecommons.org/licenses/by/4.0
SUSTAINABLE URBAN DRAINAGE SYSTEMS: ADDRESSING FLOOD RISKS AND WATER QUALITY MANAGEMENT IN FLOOD-PRONE AREAS
This study investigates the effectiveness of Sustainable Urban Drainage Systems (SuDS) in addressing flood risks and water quality management in flood-prone urban areas. Focusing on five cities that have implemented various SuDS techniques, including permeable pavements, green roofs, detention basins, and swales, the research evaluates the impact of these systems on flood risk reduction and water quality improvement. Results show that SuDS techniques significantly reduced flood events, with cities such as City B experiencing a 70% reduction in flood events and City A mitigating 60% of runoff volumes. All field projects showed declining nutrient and heavy metal along with sediment concentrations particularly in City E which recorded a remarkable 73% decrease in silt levels. A survey found that SuDS solutions received endorsement from more than 75 percent of urban designers because of their track record. Research shows that SuDS results in different cities differ according to their environmental settings together with their individual uptake rates which shape the final outcome of their systems. The study demonstrates an urgent necessity to develop comprehensive implementation solutions that involve both educational programs and dependable financial resources as well as extensive social support needed for SuDS adoption in urban development projects. The findings indicate that SuDS technology improves water quality which benefits city flood protection while enabling more sustainable urban growth.
Copyright (c) 2025 Muhammad Ammad (Author) https://creativecommons.org/licenses/by/4.0
PERFORMANCE EVALUATION OF SMART GRIDS FOR OPTIMIZED ENERGY MANAGEMENT IN SMART CITIES
This study evaluates the performance of smart grids in optimizing energy management within smart cities, focusing on key metrics such as energy efficiency, grid reliability, renewable energy integration, demand-side management, and cost-effectiveness. Using a mixed-methods approach, the research analyses data from case studies in five smart cities, supplemented by interviews and secondary data sources. The findings indicate that smart grids significantly enhance energy optimization, with cities like City C exhibiting superior performance in terms of energy loss reduction (20%), fault detection efficiency (95%), and renewable energy integration (35%). Furthermore, demand-side management strategies proved effective, with smart meter penetration in City C reaching 85%, resulting in an 18% reduction in energy consumption. Smart grids delivered favourable financial returns through positive ROI and future cost savings to the cities. The achieved results illustrate how smart grids create opportunities for improved energy management and reduced operational expenses with complementary sustainable practices in cities. The study shows developing cities face two major problems involving money issues and technology standards. Further development of smart grid technology and advanced analytics demands ongoing financial support because it ensures both high performance and scalability for urban environments.
Copyright (c) 2024 Uran Abazi, Wajid Raza (Author) https://creativecommons.org/licenses/by/4.0
OPTIMIZING LEAN MANUFACTURING PRACTICES FOR WASTE REDUCTION AND PRODUCTIVITY ENHANCEMENT IN INDUSTRIAL SYSTEMS
This study investigates the optimization of Lean Manufacturing (LM) practices for waste reduction and productivity enhancement in industrial systems. The research combines qualitative and quantitative methods to assess the impact of Lean practices, technological integration, and employee involvement on operational performance. Data were collected from interviews with plant managers and Lean practitioners, surveys with employees, and secondary data from manufacturing reports. The findings reveal a 36.67% reduction in waste and a 30% improvement in productivity following the implementation of Lean practices. Technological integration, particularly through the use of Artificial Intelligence (AI) and the Internet of Things (IoT), contributed to a 25% increase in machine efficiency, highlighting the importance of modern technologies in optimizing Lean systems. The research demonstrated that when employees were deeply involved in decision-making processes their location experienced both better productivity levels and reduced waste output through a 45% productivity increase and 40% waste reduction. Operational improvements of significance occur when organizations use Lean practices alongside technological innovation and active workplace engagement. The findings demonstrate that contemporary manufacturers need complete optimization models for their facilities while offering actionable tips to optimize efficiency together with waste reduction performance. Future investigations must concentrate on verifying the permanent implementation of these methods along with their applicability across multiple industrial applications.
Copyright (c) 2024 Muzammil Ali (Author) https://creativecommons.org/licenses/by/4.0
ADVANCED MATERIALS FOR ENHANCING ENERGY STORAGE PERFORMANCE IN LITHIUM-ION BATTERIES: DESIGN, SYNTHESIS, AND APPLICATIONS
This study investigates the development and performance enhancement of lithium-ion batteries (LIBs) through the use of advanced materials, including silicon-based anodes, nickel-cobalt-manganese (NCM) cathodes, and optimized electrolytes. Silicon-based anodes, when combined with composite structures, demonstrated a significant increase in specific capacity and cycling stability, outperforming traditional graphite anodes. The results showed that silicon-based anodes exhibited an initial capacity of 1600 mAh/g, with a capacity retention of 81.3% after 100 cycles. NCM cathodes provided higher energy densities and voltage stability compared to lithium iron phosphate (LFP) cathodes, achieving a capacity retention of 89.5% after 200 cycles. Gel polymer electrolytes produced conductive characteristics slightly below ionic liquid-based electrolytes but maintained strong thermal stability to operate in high-temperature environments. Silicon-NCM full cells reached 350 Wh/kg energy density together with 500 cycle stability that outperformed graphite-LFP-based cells operating at 180 Wh/kg and 400 cycles. The electrochemical impedance spectroscopy method produced lower charge transfer resistance results and stronger ion diffusion performance when analyzing silicon-based anodes with NCM cathodes. Advanced materials present such excellent potential to improve LIB capabilities by enhancing both energy density and cycling stability for electric vehicles and grid storage applications.
Copyright (c) 2024 Syed Muhammad Shakir Bukhari, Salma Ali (Author) https://creativecommons.org/licenses/by/4.0
DEVELOPMENT AND CHARACTERIZATION OF NANOMATERIAL-BASED LUBRICANTS FOR ENHANCED WEAR RESISTANCE IN HIGH-TEMPERATURE MACHINE OPERATIONS
This study investigates the development and characterization of nanomaterial-based lubricants to enhance wear resistance and thermal stability in high-temperature machine operations. Graphene, carbon nanotubes (CNTs), and metal oxide nanoparticles (ZnO and Al2O3) were incorporated into synthetic base oils to evaluate their tribological properties, thermal stability, and environmental sustainability. Tribological tests, including pin-on-disk and four-ball wear tests, demonstrated that nanomaterial-based lubricants, particularly those with graphene, exhibited superior wear resistance, with wear rates up to 40% lower than conventional base oils. The friction coefficient was also significantly reduced, indicating improved lubrication efficiency under high-temperature conditions. Thermal stability tests revealed that graphene and CNT-based lubricants maintained their viscosity and exhibited minimal oxidation even at temperatures exceeding 250°C, outperforming traditional oils. The four-ball wear tests established the superior load-carrying properties of graphene-based lubricants because they produced the smallest wear scar diameter and most effective load-bearing capacity. Tests using life-cycle assessment (LCA) showed graphene-based lubricants started with higher costs but saved significant maintenance expenses alongside lower environmental impact over their entire lifespan. The research demonstrates that lubricating systems containing nanomaterials especially graphene-based products represent an attractive method to improve both machine performance and environmental sustainability in heat-sensitive operations. The research delivers important findings about advanced lubricant performance while showing the potential benefit of adopting them for industries demanding intense high-performance lubrication applications.
Copyright (c) 2024 Kirubanandan Shanmugam, Armaghan Umer (Author) https://creativecommons.org/licenses/by/4.0
DEVELOPMENT OF ADAPTIVE THERMAL MANAGEMENT SYSTEMS FOR HIGH-PERFORMANCE ELECTRIC VEHICLES USING PHASE CHANGE MATERIALS
The growing demand for high-performance electric vehicles (EVs) necessitates advanced thermal management solutions to ensure optimal performance, safety, and longevity of critical components such as batteries and power electronics. This study investigates the development of adaptive thermal management systems (ATMS) for EVs using Phase Change Materials (PCMs). We explored the integration of various PCM materials, including paraffin wax, salt hydrates, and fatty acids, into thermal management systems, with a focus on enhancing thermal conductivity through the incorporation of nanomaterials such as carbon nanotubes (CNTs) and graphene. Our results demonstrate that PCM-based systems, particularly when enhanced with nanomaterials, exhibit significant improvements in thermal conductivity, energy efficiency, and temperature stability compared to conventional liquid cooling systems. Energy consumption was notably lower in PCM-based systems, especially under low and medium heat generation scenarios. However, the study also highlights challenges under high heat generation conditions, where PCM systems showed greater temperature fluctuations compared to liquid cooling systems. Long-term testing revealed a gradual decline in system efficiency over multiple cycles, although the PCM systems still maintained good temperature regulation performance. These findings suggest that PCM-based ATMS can provide a promising alternative to traditional cooling methods, offering lower energy consumption, better temperature stability, and the potential for extended system lifespan, particularly when integrated with adaptive control algorithms. Future work should focus on optimizing PCM properties and exploring hybrid systems to further enhance thermal management in EVs.
Copyright (c) 2024 Moaz Israr, Muhammad Amir Rafique (Author) https://creativecommons.org/licenses/by/4.0
INVESTIGATING ADVANCED SEISMIC RETROFITTING TECHNIQUES FOR IMPROVING THE STRUCTURAL RESILIENCE OF EARTHQUAKE-PRONE BUILDINGS
This study investigates the efficacy of advanced seismic retrofitting techniques in enhancing the structural resilience of buildings located in earthquake-prone regions. The research evaluates the performance of various retrofitting systems, including seismic isolation, hybrid retrofitting, and shape memory alloy (SMA)-based systems, through a combination of experimental tests, microstructural analysis, and finite element simulations. The results demonstrate that seismic isolation systems provide the most significant improvement in structural performance, achieving the lowest peak displacement and maximum stress, thereby enhancing building stability during seismic events. Hybrid retrofitting systems exhibited a balance between energy dissipation and structural integrity, making them a viable alternative. The peak displacements of SMA-based retrofitting systems surpassed other techniques but they delivered substantial energy dissipation. The investigation focuses on explaining the vital role that cooling rates play together with other process parameters in achieving superior retrofitting material performance. Quantitative research along with computational analyses indicate rapid cooling processes generate extensive material defects and raises residual stress levels which deteriorate the structural properties. The authors argue that proper management of manufacturing procedures must occur to avoid negative impacts which ensure lasting success of the retrofitting techniques. Hybrid materials with SMA alloys achieved superior material consistency containing fewer flaws than conventional steel-braced systems according to microstructural examinations. The experimental results provide essential new understanding and complete guidelines for engineers and legislators who need to select appropriate retrofitting methods based on material properties and cost-efficiency and performance requirements. Seismic retrofitting receives clarity through this research due to its experimental and computational findings that help engineers and planners develop upcoming earthquake-resistant designs for vulnerable regions.
Copyright (c) 2024 Fahad Ali, Muhammad Ammad (Author) https://creativecommons.org/licenses/by/4.0
ADDRESSING CHALLENGES IN ENHANCING STRUCTURAL INTEGRITY AND PERFORMANCE THROUGH ADDITIVE MANUFACTURING OF METAL ALLOYS
This study investigates the influence of additive manufacturing (AM) process parameters on the structural integrity and mechanical performance of titanium and aluminum alloys. Using selective laser melting (SLM), specimens were produced under varying laser power and scanning speed conditions to assess their ultimate tensile strength (UTS), yield strength, fatigue resistance, and fracture toughness. The results revealed that higher laser power significantly improved the UTS of titanium alloys but led to a reduction in elongation, indicating a trade-off between strength and ductility. In contrast, the scanning speed had a more pronounced effect on aluminum alloys, where slower speeds resulted in stronger materials. The fatigue life of materials improved considerably after post-processing heat treatment while the aluminum alloys demonstrated specific advantages through enhanced cycle performances. The strength of fractures proved better following heat treatment as aluminum alloys demonstrated outstanding toughness improvements above raw specimens. The AM processing conditions allowed titanium alloys to produce fewer defects than aluminum alloys when analyzed via microstructural evaluations. The finite element analysis (FEA) predicted both temperature distributions and cooling rate patterns which validated against experimental results while demonstrating how cooling speed affects residual stresses as well as defect distribution during AM processing. Post-processing techniques and suitable processing parameters function as essential elements for enhancing both reliability and performance characteristics of AM metal alloys according to the study outcomes. The research presented viable insights for improving the quality of components from additive manufacturing which are particularly essential for aerospace and medical products. Research in the future should work on enhancing the optimization techniques and studying extended performance behavior of components manufactured using AM technology.
Copyright (c) 2024 Armaghan Umer, Imran Bashir, Hina Saeed (Author) https://creativecommons.org/licenses/by/4.0
DEVELOPMENT AND PERFORMANCE ENHANCEMENT OF HIGH-EFFICIENCY PHOTOVOLTAIC SOLAR CELLS FOR ADVANCED RENEWABLE ENERGY SYSTEMS
This study investigates the development and performance enhancement of high-efficiency photovoltaic solar cells, focusing on perovskite, silicon, and tandem configurations. The research examines the efficiency, stability, and environmental performance of these solar cells under standard testing conditions and prolonged exposure to real-world environmental factors. Our findings indicate that perovskite solar cells achieved a maximum efficiency of 25.5%, while silicon-based cells demonstrated an efficiency of 20.8%. Tandem cells, combining perovskite and silicon materials, achieved the highest efficiency of 28.3%, surpassing the efficiency of individual materials. However, stability testing revealed that perovskite cells suffered a significant efficiency degradation of 14% after 1000 hours, while silicon cells exhibited a minimal decrease of 2%. Results from extended research demonstrated that tandem cells experienced a reduction in efficiency to 6% even though they provided better performance. The tests demonstrated improved efficiency of 8% when light intensities increased while the cells maintained reduced performance when operating at different temperatures. The investigations show that tandem solar cells possess strong potential for efficient energy transformation yet they face stability issues and sensitivity to temperature that require addressing to gain commercial success. The research demonstrates key strategies for photovoltaic enhancement along with necessary future development for better perovskite and tandem cell durability and performance.
Copyright (c) 2024 Wajid Raza, Nimra Yasir, Hassan Javed (Author) https://creativecommons.org/licenses/by/4.0
DESIGN AND OPTIMIZATION OF GREEN BUILDING SYSTEMS UTILIZING RENEWABLE ENERGY FOR ACHIEVING LOW CARBON FOOTPRINT
This study investigates the design and optimization of green building systems utilizing renewable energy technologies to achieve a low carbon footprint. Through a combination of case study analysis, energy simulations, and optimization algorithms, the research evaluates the performance of photovoltaic (PV) solar panels, wind turbines, and geothermal systems in enhancing energy efficiency and sustainability. The results indicate significant energy savings and carbon reductions in buildings incorporating renewable energy systems, with up to 40% energy savings and 50 tons of carbon reduction annually. Moreover, the study demonstrates the effectiveness of optimizing building systems, such as insulation and HVAC technologies, in maximizing energy efficiency. The integration of multiple renewable energy sources, including PV solar and wind turbines, provided higher efficiency improvements and faster payback periods compared to single systems. When combined systems function together they deliver substantial financial advantages which shorten payback periods and generate increased yearly savings based on complete cost-benefit assessments. The research delivers essential information to construction companies because it demonstrates both financial returns and environmental advantages of green construction methods. The research increases the ongoing investigation of sustainable design through its presentation of renewable power systems as environment-friendly building solutions. Researchers should analyse implementation obstacles while targeting improved energy system designs and conducting nationwide building type and climatic analyses.
Copyright (c) 2024 Abdul Jabbar, Ayesha Tariq, Bilal Ahmed (Author) https://creativecommons.org/licenses/by/4.0
DESIGN AND FABRICATION OF FLEXIBLE AND TRANSPARENT ORGANIC ELECTRONICS FOR WEARABLE HEALTH MONITORING SYSTEMS
This study investigates the design and fabrication of flexible and transparent organic electronics for wearable health monitoring systems, focusing on material properties, fabrication techniques, and sensor performance. Various organic materials, including Poly(3-hexylthiophene) and Polyflourene, were evaluated for their electrical conductivity, transparency, flexibility, and stability. The results indicate that these materials offer an optimal balance of high conductivity and transparency, essential for wearable sensors. The device performance suffered from gravitational stress because device optical transparency and electrical conductivity became worse under higher strain levels. Roll-to-- roll printing and inkjet printing should merge to execute simultaneous scalability and cost efficiency evaluations for manufacturing purposes. A major weakness of these manufacturing techniques is their inability to create enough material waste output. These materials enhanced wearable medical tracking devices to deliver better measurement precision of body temperature and heart rate compared to existing research methods. Poly(3-hexylthiophene proved resilient to bending due to its superb capability of maintaining stability across different moisture and temperature conditions. The current investigation demonstrates vital organic electronic applications for wearable healthcare devices but confirms ongoing requirements for creating innovative materials and production processes for enhanced performance and usage.
Copyright (c) 2024 Muhammad Danial Ahmad Qureshi, Maria Hanif, Kamran Iqbal (Author) https://creativecommons.org/licenses/by/4.0
THE ECOLOGICAL FOOTPRINT OF INFRASTRUCTURE DEVELOPMENT: ENVIRONMENTAL IMPACT ASSESSMENTS IN MODERN ENGINEERING
The accelerating pace of global infrastructure development has raised serious concerns about its ecological consequences, particularly in rapidly urbanizing and industrializing regions. Projects involving transportation networks, energy systems, and urban expansion significantly contribute to environmental degradation through land use change, resource depletion, pollution, and greenhouse gas emissions. To address these challenges, Environmental Impact Assessments (EIAs) have emerged as essential regulatory tools for evaluating and mitigating the environmental risks associated with large-scale infrastructure initiatives. This research employs a comprehensive methodological framework integrating Life-Cycle Assessment (LCA), Geographic Information Systems (GIS), and environmental modeling to quantify the ecological footprint of infrastructure projects. Through comparative case studies and simulation-based analysis, the study captures both the direct and cumulative impacts of development activities on ecosystems, biodiversity, and resource use.The results reveal substantial ecological burdens linked to transportation and fossil fuel-based energy infrastructure, including elevated carbon emissions, deforestation, and soil erosion. Renewable energy integration, public participation in EIAs, and the adoption of green building technologies significantly correlate with reduced environmental impact scores. The case studies demonstrate that while current EIA practices provide a foundation for impact assessment, they often fall short in long-term ecological forecasting and mitigation enforcement.In conclusion, the study underscores the necessity of strengthening EIA methodologies through advanced digital tools, regulatory reform, and community engagement. A transition toward smart infrastructure design, net-zero buildings, and adaptive environmental governance is essential for minimizing ecological footprints and aligning infrastructure development with global sustainability goals. This work provides a strategic framework for policymakers and engineers to balance growth with ecological integrity in the face of escalating environmental risks.
Copyright (c) 2023 Saira Javed, Amjad Malik, Hafsa Yousaf (Author) https://creativecommons.org/licenses/by/4.0
DESIGN OF LOW-POWER, HIGH-EFFICIENCY ANALOG CIRCUITS FOR WEARABLE HEALTH MONITORING DEVICES
This study presents the design and optimization of low-power, high-efficiency analog circuits for wearable health monitoring devices. The primary objective was to reduce power consumption while maintaining the required signal integrity for accurate bio-signal processing. The key components optimized in this study include amplifiers, filters, and analog-to-digital converters (ADCs), with particular focus on minimizing energy consumption without compromising performance. The results indicate that low-power amplifiers and ADCs, operating at sub-threshold voltage levels, achieved power consumption as low as 0.35 mW, while maintaining a signal-to-noise ratio (SNR) of over 50 dB. The combination of piezoelectric generators with thermoelectric generators and supplementary energy harvest technologies minimized system dependence on outside power sources through power generation output reaching a maximum of 21.6% of the total system output. The research confirms that multi-channel amplifiers maintain excellent signal fidelity (55.0 dB SNR) by using power at 1.25 mW. Different environmental tests showed that the system preserved its stability through minor decreases in performance levels during extremes of conditions. The integrated energy harvesting system maintains continuous operation for 200 hours due to which device operation duration is markedly extended. Current wearable electronic technology utilizes optimized power-saving approaches alongside energy collecting techniques for medical tracking device optimization according to the research findings. The ongoing development of wearable health technologies for continuous medical monitoring needs progress from this research and better energy sources together with higher signal quality and extended operational duration.
Copyright (c) 2025 Fahad Ali, Imran Ali (Author) https://creativecommons.org/licenses/by/4.0
ENHANCING CYBERSECURITY IN SMART CITIES: MITIGATING THE RISKS OF IOT VULNERABILITIES AND DATA BREACHES
This study investigates the cybersecurity risks associated with the integration of Internet of Things (IoT) devices in smart cities, focusing on vulnerabilities and data breaches. As cities increasingly adopt IoT technologies, the exposure to cyber threats grows, highlighting the need for effective security strategies. The research employs a mixed-methods approach, combining qualitative data from expert interviews and surveys with quantitative analysis of IoT-related cybersecurity incidents across various smart cities. The results indicate a significant rise in IoT cybersecurity incidents from 2021 to 2023, with unauthorized device access and weak encryption identified as the most prevalent vulnerabilities. Survey findings show a high level of public concern about data privacy, with 75% of respondents expressing worry about potential breaches, and 70% indicating a willingness to adopt stronger security measures. Expert recommendations emphasize the importance of continuous monitoring, real-time threat detection, and stronger device authentication protocols. According to the report strong collaboration between public and commercial sectors stands essential for enhancing IoT security frameworks. These research conclusions teach the creators of smart city policy how to develop cybersecurity protection for the IoT through technical solutions and collaborative management systems. The research enriches smart city security studies by giving valuable recommendations which strengthen IoT security measures and shield urban structures from evolving digital threats.
Copyright (c) 2025 Insights Sustainable Engineering Practices https://creativecommons.org/licenses/by-nc-nd/4.0
DEVELOPMENT OF AN ADVANCED HEAT RECOVERY SYSTEM TO IMPROVE ENERGY EFFICIENCY IN INDUSTRIAL MANUFACTURING PROCESSES
This study investigates the effectiveness of an advanced heat recovery system in enhancing energy efficiency within industrial manufacturing processes. The primary objective was to develop and optimize a system capable of reducing energy consumption and minimizing environmental impact across various industries. The results demonstrated a significant reduction in energy consumption, with savings ranging from 15% to 22% across sectors including steel, chemical, and automobile manufacturing. Additionally, heat recovery efficiency was found to be highest in high-temperature industries such as steel and chemical production, achieving up to 30% heat recovery. The economic benefits were substantial, with return on investment (ROI) values ranging from 35% to 45%, particularly in energy-intensive industries. Furthermore, CO2 emissions were reduced by up to 22%, highlighting the environmental advantages of implementing heat recovery systems. The findings also revealed variations in system performance across industries, indicating the need for tailored solutions to optimize results. A short duration between 18- and 22-months during payback periods shows that these systems help users achieve both immediate and postponed financial benefits. Advanced heat recovery applications now have a strong implementation basis from this study while expanding industry knowledge about energy efficiency. Organizations seeking sustainable industrial methods can benefit from this study which demonstrates how these systems create operational excellence with lower energy expenses while reducing environmental footprints.
Copyright (c) 2025 Muhammad Amir Rafique , Hammad Ur Rehman (Author) https://creativecommons.org/licenses/by/4.0
OPTIMIZATION OF POWER DISTRIBUTION NETWORKS USING MACHINE LEARNING FOR FAULT DETECTION AND PREVENTIVE MAINTENANCE
This study investigates the optimization of power distribution networks using machine learning (ML) techniques, with a specific focus on fault detection and preventive maintenance. The research evaluates the performance of three machine learning models—deep learning, decision trees, and support vector machines (SVM)—in terms of accuracy, fault detection capabilities, time efficiency, and cost-effectiveness. The results reveal that deep learning models outperformed traditional methods, achieving an accuracy of 92%, compared to 83% for decision trees and 85% for SVM. In terms of fault detection, deep learning detected 96% of faults, whereas decision trees and SVM detected only 85% and 89%, respectively. Moreover, machine learning models, particularly deep learning, significantly reduced fault detection time by 40%, and demonstrated a 25% reduction in operational costs due to fewer emergency repairs and optimized maintenance schedules. Machine learning offers the power distribution networks a chance to enhance operational performance and minimize operating downtime and optimize resource allocation. The implementation success demands resolution of data quality requirements and infrastructure compatibility alongside initial installation costs and implementation expenses. This study demonstrates the fundamental need to deploy machine learning technology in electricity distribution systems for enhancing performance while improving stability.
Copyright (c) 2025 Aiman Shabbir (Author) https://creativecommons.org/licenses/by/4.0
EVALUATING ECOLOGICAL FOOTPRINTS: THE ROLE OF ENVIRONMENTAL IMPACT ASSESSMENTS IN SUSTAINABLE ENGINEERING PRACTICES
As environmental degradation accelerates globally, the role of Environmental Impact Assessments (EIAs) in ensuring sustainable engineering practices has gained paramount importance. In particular, developing nations like Pakistan face rising ecological footprints due to rapid infrastructure development and weak environmental regulation. This study investigates the effectiveness of EIAs in mitigating environmental harm and promoting sustainability by integrating modern methodologies and renewable energy solutions into engineering projects.The research employs a hybrid methodological framework that combines conventional EIA phases with advanced tools such as Geographic Information Systems (GIS), Life Cycle Assessment (LCA), and remote sensing. Comparative analysis between developed and developing countries highlights regulatory disparities, while case studies from Pakistan demonstrate practical applications and systemic challenges in EIA execution.Results reveal that projects which incorporated rigorous EIAs—particularly those utilizing renewable energy sources like solar and wind—exhibited significantly reduced ecological footprints and lower greenhouse gas emissions. Additionally, stakeholder feedback emphasized that public participation and digital engagement platforms enhanced the legitimacy and effectiveness of the EIA process. Surveyed infrastructure initiatives showed improved compliance and environmental performance where modern tools and community involvement were embedded into the assessment framework.The findings underscore the need to strengthen EIA enforcement in Pakistan through institutional capacity-building, legal reforms, and investment in environmental data infrastructure. Furthermore, integrating ecological footprint metrics into EIAs presents an opportunity to align engineering development with global sustainability goals. This study concludes that a modernized, inclusive, and technologically enhanced EIA system is essential for fostering long-term ecological resilience and sustainable infrastructure development in emerging economies.
Copyright (c) 2023 Rabia Khan, Arslan Nadeem (Author) https://creativecommons.org/licenses/by/4.0
HARNESSING THE POWER OF NATURE: INTEGRATING SOLAR, WIND, AND OTHER RENEWABLE ENERGY SOURCES INTO MODERN ENGINEERING DESIGNS
The global energy crisis and environmental degradation have intensified the need for clean, sustainable energy solutions. Renewable energy technologies such as solar, wind, and biomass offer promising pathways to reduce carbon emissions, enhance energy security, and support sustainable development goals. This study explores the integration of renewable energy into engineering design with a particular focus on optimizing energy efficiency, minimizing emissions, and improving grid stability.A comprehensive methodology was employed, combining quantitative performance analysis of solar and wind energy systems with simulation-based modeling of smart grid responses. The design framework also incorporated AI-based predictive analytics to assess storage efficiency and environmental impact under varying regional and policy conditions. The results demonstrate that renewable energy integration significantly improves system efficiency, with solar and wind output contributing to measurable reductions in greenhouse gas emissions. Storage efficiency and emission savings varied across regions, highlighting the influence of localized factors in performance optimization. Simulation models also confirmed the potential of hybrid energy systems and AI-assisted control mechanisms to enhance energy reliability and reduce operational costs.The findings underscore the importance of data-driven design, policy alignment, and advanced engineering frameworks in accelerating renewable energy deployment. This research concludes that with proper infrastructural upgrades, AI integration, and community engagement, renewable energy systems can be feasibly and efficiently scaled in emerging economies. The study offers a roadmap for engineers, policymakers, and energy planners aiming to implement resilient, low-carbon technologies in the face of growing environmental and energy challenges.
Copyright (c) 2023 Adnan Saleem, Mahnoor Ijaz (Author) https://creativecommons.org/licenses/by/4.0
ECO-FRIENDLY SOLUTIONS: THE ROLE OF GREEN TECHNOLOGIES IN COMBATING ENERGY WASTE AND POLLUTION
The growing urgency of climate change, energy depletion, and environmental degradation has intensified the global pursuit of sustainable solutions. Green technologies—encompassing renewable energy systems, energy-efficient infrastructure, and waste management innovations—have emerged as pivotal tools for mitigating pollution and conserving resources. This research investigates the practical implementation of green technologies across Pakistan’s energy, building, transportation, and waste management sectors using a multidisciplinary, systems-based methodology.The study employs a comparative assessment framework integrating efficiency metrics, cost-benefit analyses, emission modeling, and energy savings evaluations. Renewable energy technologies such as solar, wind, and biomass are analyzed for regional viability and environmental performance. In parallel, sustainable architectural strategies, smart home systems, and building automation platforms are assessed for their role in minimizing energy consumption and operational emissions.The findings reveal substantial reductions in energy waste and carbon emissions across multiple domains, with energy-efficient buildings demonstrating up to 50% lower power usage, electric vehicles achieving a 65% reduction in urban transport emissions, and waste-to-energy plants significantly contributing to clean energy production and landfill volume reduction. Smart automation in residential and commercial buildings led to consistent monthly energy savings, while the integration of public transport electrification showed promising environmental returns.In conclusion, the study underscores the transformative potential of green technologies in driving sustainable development and environmental resilience in developing economies. Despite infrastructural and policy-related challenges, the results indicate that coordinated implementation of these technologies can significantly advance national sustainability goals. The research provides critical insights for policymakers, urban planners, and environmental stakeholders aiming to align economic growth with ecological responsibility.
Copyright (c) 2023 Samina Arif, Mehwish Latif (Author) https://creativecommons.org/licenses/by/4.0