American Journal of Engineering , Mechanics and Architecture (2993-2637)
American Journal of Engineering, Mechanics and Architecture (2993-2637) is an international peer-reviewed journal published to reach excellence on Research and Scientific Development. The journal is not limited to a specific aspect of engineering and architecture but is instead devoted to a wide range of subfields in the engineering sciences, electrical, mechanical and architecture. Articles of interdisciplinary nature are particularly welcome. The journal strives to maintain high-quality of publications. There will be a commitment to expediting the time taken for the publication of the papers. The editorial board reserves the right to reject papers without sending them out for review. The journal also publishes innovative contributions on every aspect of the architectural endeavor.
The Future Role of Mechanical Engineers in Virtual Reality: Opportunities, Challenges, and Future Perspectives
Virtual Reality (VR) has emerged as a transformative technology that is transforming engineering practices across various industries. In mechanical engineering, VR is increasingly utilized for product design, virtual prototyping, manufacturing process optimization, maintenance planning, safety training, and engineering education. This paper explores the evolving role of mechanical engineers in the era of virtual reality by examining its current applications, emerging opportunities, key challenges, and future prospects. The study is based on a review of recent scientific literature, industry reports, and technological developments related to VR and Industry 4.0. The findings indicate that VR enables engineers to improve design accuracy, reduce development costs, enhance collaboration, and create safer and more efficient working environments. However, several challenges remain, including high implementation costs, hardware limitations, cybersecurity concerns, and the need for specialized skills. Looking ahead, the integration of artificial intelligence, digital twins, haptic technologies, and Industry 5.0 is expected to further expand the role of mechanical engineers in immersive digital environments. The paper concludes that VR will become an essential component of future mechanical engineering, requiring engineers to continuously develop digital competencies alongside traditional engineering knowledge.
Assessment of Building Forms of Student Hostels in Tertiary Institutions in Akwaibom State, Nigeria
The study assessed building forms of student hostels in tertiary institutions in Akwa Ibom State, Nigeria. The research design for this study combines survey and experimental research. The target population of the study comprises all the hostel buildings in the higher institutions under study. Purposive sampling technique was used in selecting five (5) Hostels from University of Uyo (M2, W3, W4, W6 and PGW), two (2) Hostels from Akwa Ibom State Polytechnic (Block A and D) and two (2) Hostels from Akwa Ibom State University (VVLodge, and Lemon Lodge). The instrument was validated by a senior academician in this field of study. The instrument used in measurement of this research are produced by credible factories whose products are well used all over the world. Simple random sampling technique was used for the data collection. Mean score was used as a method of analysis. The study revealed that variations in building orientation, layout configuration, and construction materials significantly influence indoor temperature and thermal comfort levels. The study also revealed that hostels with compact forms and inadequate ventilation strategies exhibited higher indoor heat accumulation. The study concluded that building forms that incorporated cross-ventilation and proper spatial planning demonstrated improved thermal performance. One of the recommendations made was that trees and fins should be used as it helps in the reduction of solar gain through radiation while vents and stack effects help in the emission of used and warm air from the interior
An Integrated Engineering Approach to Wastewater Treatment, Intensification, and Closed-Loop Reuse at Gas Compressor Stations: A Case Study of the Shurtan Oil and Gas Production Department (Uzbekistan)
Gas compressor stations in the oil and gas industry generate significant volumes of wastewater - oil-bearing production effluents, domestic sewage, and surface runoff - whose uncontrolled discharge threatens the scarce water resources of arid regions such as the Kashkadarya Province of Uzbekistan. This paper presents an integrated engineering framework for the treatment, intensification, and closed-loop reuse of such wastewater, using the booster compressor stations of the Shurtan Oil and Gas Production Department as a case study. Three complementary directions are synthesized: (I) multi-stage treatment (mechanical, physicochemical, and sorption) combined with utilization of the recovered oil and sludge; (II) intensification of treatment schemes through coagulation-flocculation, thin-layer settling, and pressure flotation; and (III) modernization of facilities with membrane and sorption technologies and the development of water-reuse systems. Theoretical analysis based on typical performance indicators shows that the multi-stage scheme reduces the oil-product concentration from about 500 to 0.5 mg/L (overall efficiency ≈ 99.9%), that thin-layer settling reduces the footprint by a factor of 2.5-3, and that modernization raises the water-reuse share from 15% to 85%. A closed (zero-discharge) cycle returning the treated water to the recirculating cooling system eliminates discharge to natural water bodies. The proposed framework couples environmental protection with resource efficiency and provides an engineering basis for sustainable water management at gas compressor stations.
Flexural Behaviour and Stiffness of Reinforced Concrete T-Beams with Bond-Slip Effects
This study investigates the stiffness and long-term flexural behaviour of reinforced concrete T-beams by incorporating the effects of bond-slip at the reinforcement–concrete interface. Traditional design approaches often assume a perfect bond, which may lead to an overestimation of stiffness and an underestimation of long-term deflection. In this work, an analytical method is developed to quantify the influence of slip on curvature, stiffness reduction, and deflection. The model integrates essential mechanical parameters of concrete, including its modulus of elasticity, time-dependent deformation characteristics, creep, shrinkage, and the nonlinear interaction between concrete and reinforcement. A comprehensive computational framework is established based on regulatory recommendations and long-term deformation models. The results demonstrate that bond-slip significantly increases deflection and reduces effective stiffness compared to conventional bonded assumptions. The analytical expressions derived in this study provide a more realistic prediction of the flexural response of reinforced concrete beams, especially under sustained loading conditions as time approaches infinity. The proposed method can serve as a practical tool for evaluating long-term serviceability performance and can be applied to further experimental and theoretical research in the mechanics of reinforced concrete structures.
Bond-slip; Modulus of elasticity; Long-term deformation; Flexural behaviour; Stiffness reduction; Reinforced concrete T-beams.
The Architecture Of Traditional Foothill Residential Buildings In The Tashkent Region In The Late 19th And Early 20th Centuries: Based On Archival And Field Research
This study examines the architectural features of traditional foothill residential buildings in the Tashkent region during the late nineteenth and early twentieth centuries using archival materials and field investigations. The research analyzes the planning, construction techniques, and artistic characteristics of dwellings in the village of Oblik (Angren District). The findings reveal distinctive architectural elements, including kush sinch wall construction, kashgarcha windows, verandas, and climate-responsive spatial organization. The results indicate the influence of regional building traditions while highlighting unique local characteristics. The study emphasizes the importance of documenting and preserving these increasingly endangered buildings and provides a scientific basis for future conservation and restoration of Uzbekistan’s vernacular architectural heritage.
Coupled Hydraulic-Genetic-Algorithm Optimization for the Joint Minimization of Water and Energy Losses in Urban Water Distribution Networks: A Case Study of an Arid-Region City
Aging urban water distribution networks in arid regions lose a substantial fraction of the water they transport, and because every cubic metre of water must be pumped, each physical leak simultaneously wastes the electrical energy used to lift and pressurize it. Excess and spatially uneven pressure is the common root cause of both losses: it drives leakage through pressure-dependent orifices and forces pumping stations to expend energy generating head that is not required for service. This paper develops and demonstrates a coupled optimization framework that treats water losses and pumping energy as a single, jointly minimized system rather than as two independent problems. A pressure-dependent (FAVAD-type) leakage formulation is embedded in a steady-state hydraulic model built on the global-gradient method, and pumping energy is expressed directly through the hydraulic-regime variables. A single economic objective function-the combined annual cost of lost water and wasted energy-is minimized under service-pressure and equipment constraints using a genetic algorithm coupled to the hydraulic solver, with pressure-reducing-valve set-points and variable-frequency-drive pump speeds as decision variables. The framework is applied to a representative model of the Karshi city network (Kashkadarya region, Uzbekistan). For the studied configuration, the optimized pressure regime lowered the network-average pressure from about 58 m to about 33 m while maintaining the minimum service head, reducing modeled physical water losses by roughly 31 % and pumping energy by roughly 27 %, with an estimated simple payback of about two years. The results confirm that district-metered-area zoning combined with active pressure control delivers water and energy savings that neither measure achieves alone.
Solution of Problems in Distribution of Oil Products
The article discusses the existence of many problems in the distribution of oil (fuel) products in any condition, including extreme conditions. For example, It is recognized that in what tanks fuel products are stored, the vertical and horizontal location of the tank depends on the size of the slot through which the liquid flows and its geometric shape (circle, cone, etc.) Based on Bernoulli's laws, the flow of liquid and the dependence of the liquid level on the height of the liquid were scientifically discussed. Most importantly, the relationship between temperature range of [-10 ... +40] °C and density of oil (gasoline) products was calculated and graphed using the Excel program. In addition, a mathematical differential formula was developed for the time distribution or reception of gasoline used for military and agricultural vehicles, large quantities of products in the field and extreme conditions. Based on the laws of molecular dynamics, the flow times of fuel (gasoline) in vertical and horizontal containers under the same conditions were analyzed.
AI-Driven Digital Twin Technologies for Advanced Manufacturing Systems
With the fast evolution of Industry 4.0 the use of Artificial Intelligence (AI) and Digital Twin technologies have increased in order to improve the efficiency, reliability and intelligence of advanced manufacturing systems. A Digital Twin is a virtual model of a physical manufacturing resource used to monitor, predict and make decisions based on real-world operating data throughout its lifespan. The proposed research is an AI-based Digital Twin framework to realize advanced manufacturing systems, leveraging machine learning techniques and industrial sensor data to enhance equipment health monitoring and predictive maintenance. This study uses a dataset of 10,000 observations of milling machine processes collected by various sensors, including air temperature, process temperature, rotary speed, torque, tool wear, product type, and failure indicators of milling machines. Data is cleaned, transformed and prepared using a comprehensive data processing pipeline for building a model. Random Forest, Support Vector Machine, Decision Tree, XGBoost and Artificial Neural Network are all implemented and evaluated to find the best machine learning algorithm to predict machine failures. Conventional classification metrics including accuracy, precision, recall, F1 score, receiver operating characteristic – Area under the curve (ROC-AUC) and confusion matrix analysis are used to evaluate model performance. Overall, the proposed framework illustrates the potential for using AI-powered predictive analytics to complement Digital Twin technologies, by facilitating early fault detection, intelligent maintenance planning, and continuous optimization of operations. Digital Twin technology combined with AI helps minimize equipment downtime, enhance production reliability, schedule maintenance more effectively and boost manufacturing productivity. Also, the suggested framework enables data-driven decision making and offers a scalable structure for the deployment of intelligent manufacturing solutions in the context of Industry 4.0. The study builds on the ever-expanding body of research on AI-driven Digital Twins by providing a practical framework to boost manufacturing resilience, operational efficiency, and sustainable industrial performance, leveraging predictive intelligence.
Copyright (c) 2023 Reyan Hridoy Bhuiyan, Umme Habiba Aesha, Sumaya Mahajabin Khan, Muhammad Ahnaf Jabeer
Reserves Estimation Using Production Rate Decline Trend Analyses
A constant production rate decline was simulated for reserves estimation, using flow regimes in oil and gas reservoirs. The problem of estimating oil reserves in place or cumulative recovery factor in a dynamic state calls for research. The problem has been so because no researcher has estimated accurate decline constant which can predict an accurate recovery factor or estimate accurate reserves initially in place. Flow regimes in reservoirs were established as a field of study using equations of fluid flow in porous media. The fluid flow equations were used to establish the decline trend, and the estimated production rate decline constant was also used to model the decline. The decline rate constants were subsequently used to build models to predict yearly production data for any given period. These were used to generate models for projecting oil and gas cumulative production and reserves in place. The results obtained were validated using actual yearly production field data from the original data source. The evaluation models were subsequently built using the predicted yearly data. The models were then used to estimate cumulative reserves recovery in any given period and reserves initially in place. The values obtained compared favourably with the respective tank values. The percentage accuracy for gas fields ranged from 99.86% and above, while the percentage accuracy for oil ranged from 98.64% to 99.98%. The models reduce the complexity and time used in reservoir simulation. They are very flexible and can be applied with high accuracy from the reservoir decline stage to abandonment. They are equally used for the establishment of production and for making economic decisions.
Methods for Restoring the Working Surfaces of Various Types of Parts by Surfacing with Wear-Resistant Materials
One of the perspective methods of the reconstruction worker to surfaces of the details of the different type is on melting wear-out steadfast material. For наплавке of the details of the type gross layer s on surfaces, possessing high wear-out by stability. In article is considered recovering the details of the type gross on melting wear-out steadfast metal on damaged worker of the surfaces and influence change the thickness to hardness of the gross after on swimming trunks.
Copyright (c) 2026 American Journal of Engineering , Mechanics and Architecture (2993-2637)
A Comparative Policy Analysis of Plastic Waste Governance and The Construction-Sector Recognition Gap in Five Sub-Saharan African Countries
Sub-Saharan Africa generates a rapidly growing share of global plastic waste, and over the past three years several of its largest economies have introduced ambitious bans, levies, and extended producer responsibility (EPR) regimes to address it. Yet a critical question remains under-examined: do these regulatory frameworks formally recognise plastic waste as a feedstock for construction materials, or do they remain focused on packaging and single-use items at the point of disposal? This paper presents a comparative, desk-based policy analysis of plastic waste governance in five Sub-Saharan African countries — Nigeria, Ghana, Kenya, South Africa, and Rwanda, selected for their advanced but divergent regulatory trajectories. Using a structured five-dimension framework (single-use restrictions, EPR architecture, construction-sector recognition, financing instruments, and informal-sector integration), the study synthesises national policy documents, regulatory gazettes, and recent peer-reviewed and grey literature (2017–2026) to map each country's governance architecture. Findings show that all five countries have adopted upstream interventions — bans, levies, or EPR — but none has a codified building-code provision, national standard, or certification pathway for recycled-plastic construction products, even where pilot-scale evidence, notably Rwanda's sisal-reinforced plastic tiles, demonstrates technical compliance with international structural and durability standards. Financing instruments such as Ghana's SME grant scheme and South Africa's EPR fees are similarly disconnected from materials-standards development. The paper argues that this construction-sector recognition gap, rather than technical infeasibility, is the principal regulatory bottleneck constraining the transition from technically viable recycled-plastic building materials to commercially deployable ones. It proposes a phased standards-development pathway for Nigeria that draws on lessons from Rwanda's cooperative-research model and Kenya's labelling regime.
Issues of Adaptive Neuro-Fuzzy Modeling of Operational and Predictive Control Systems for Information Flows in Telecommunication Networks
This article discusses the issues of improving the efficiency of information flow management in telecommunication networks. In particular, scientific approaches to the development of operational and predictive control systems based on adaptive neuro-fuzzy modeling are analyzed. The proposed models make it possible to reduce computational load in traffic flow distribution, enhance real-time decision-making capabilities, and ensure stable network operation under various uncertainty conditions. Furthermore, the performance of neuro-fuzzy models of different orders is comparatively evaluated, and the mechanisms by which their application in military communication networks reduces information transmission losses and increases network throughput are revealed. The research findings are of significant scientific and practical importance for identifying priority directions in the development of communication systems of the Armed Forces.
Smart Recruitment System Using Artificial Intelligence and Natural Language Processing
This paper is an intelligent and innovative application to automate and optimise the recruitment process. Today’s job market can see companies receiving hundreds, or even thousands, of resumes for one position, thus making manual screening an inefficient, time consuming and human bias sensitive process. This project proposes an AI-driven approach to overcome these limitations by leveraging Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML) techniques to automatically analyse and rank resumes for their suitability to specific job roles. The system allows candidates to upload their resumes in common document formats like PDF or DOCX. Uses state-of-the-art NLP algorithms to extract key information such as the candidate's name, contact details, educational background, technical & soft skills, and professional experience. At the same time, recruiters can upload a job description (JD) that details the qualifications, skills and experience required for the position. The AI engine then compares the extracted features from the resume with the features from the job description using similarity metrics such as TF-IDF vectorisation and cosine similarity. The system can generate a match score for each candidate by doing this comparison.
Development Of Modern Pavement Materials For Highways Under Climate Change Conditions (Challenges And Solutions)
This paper analyzes the impact of climate change on road pavements and the current scientific and technical challenges in the development and application of modern pavement materials. A comparative approach was used to evaluate the operational, economic, and environmental advantages of polymer-modified bitumen, recycled asphalt pavement (RAP), and geosynthetic materials. The study also examines pavement deterioration and reduced service life under the influence of high temperatures, moisture, and traffic loads in the sharply continental climate of Uzbekistan. The comparative analysis revealed that polymer-modified bitumen and basalt fiber-reinforced pavements exhibit higher resistance to deformation under high-temperature conditions.
Artificial Intelligence Allowed Structural Health Monitoring for Resilient Infrastructure Systems
Background: The development of Artificial Intelligence (AI)-based Structural Health Monitoring (SHM) systems brings a new advanced system which protects infrastructure sustainability and safety through established performance standards. Methods: Implemented a quantitative cross-sectional survey study through Google Forms, they used to distribute their online questionnaire. The study gathered 175 complete answers from people at worked in engineering and AI and infrastructure-related fields. Descriptive statistical methods which combined frequency distribution with percentage analysis to study people understood things and how they perceived things and their readiness to adopt and their major obstacles. Results: AI-based SHM systems well because damage detection systems received the highest recognition at 76.8%. The survey results demonstrated a strong positive perception because 82.1% of respondents agreed that AI technology boosts structural safety and 79.6% of participants reported better monitoring performance. The survey results showed that 78.9% of participants supported AI technology for smart infrastructure development. The survey showed that 68.6% of participants wanted to use AI-powered SHM systems but 19.4% of them stayed uncertain about their choice. Conclusion: AI-powered SHM systems as effective tools which improve both structural security and operational efficiency and environmental sustainability. The moderate adoption rate exists because financial obstacles combine with technological barriers and infrastructure problems. The worldwide adoption of AI-based resilient infrastructure systems needs barrier elimination because this process enables faster implementation and full system potential achievement.
Copyright (c) 2023 American Journal of Engineering , Mechanics and Architecture https://creativecommons.org/licenses/by/4.0
Investigation of Waterflooding in an Oil Reservoir by Streamline Simulation
Perhaps Waterflooding is the most popular method of secondary recovery to pressure stability and support a higher withdrawal rate but the large of produced water in waterflooding of offshore oil reservoir is one of the most important oil industry problems. So determining of optimized injection rate for injection wells is one of the important factors in a successful waterflooding. Like using conventional finite difference method, developments of streamline methods lets streamline based reservoir simulation has a lot of effects on solving complex geologically large and heterogeneous reservoir and also streamline simulation is explained in the text of producer and injector usage, relationship between injectors and producers and well allocation factors.So using of streamline simulators is a suitable method to manage waterflooding in oil reservoir. The purpose of this paper is to explain the usage of streamline method for a waterflooding simulation in an Iranian offshore oil reservoir that leads to optimization of water injection rate for decreasing the produced water from reservoir.
The Significance of Vibration Motion in Reducing the Traction Resistance of Technical Means for Soil Processing
This study examines the significance of vibrational motion in reducing the traction resistance and energy consumption of technical means used for soil processing. A comprehensive review of scientific literature, patent resources, and existing vibration-based tillage technologies was conducted to evaluate the effectiveness of forced vibration in improving soil cultivation performance. The study analyzes the influence of vibration parameters, including direction, frequency, and amplitude, on the interaction between tillage tools and soil. The findings indicate that the application of controlled vibrational motion decreases soil adhesion and draft resistance, enhances soil fragmentation, improves moisture retention, and reduces the wear of working components. Experimental evidence reported in previous studies demonstrates that vibration-assisted tillage can reduce traction resistance by up to 35–60%, depending on soil conditions and machine design. The results also highlight that the optimal selection of vibration parameters and oscillation-generating mechanisms contributes to improved energy efficiency, increased operational productivity, and sustainable agricultural practices. The study concludes that the implementation of forced vibration technology is particularly suitable for the soil and climatic conditions of Uzbekistan, where it can significantly enhance the efficiency of tillage operations while reducing fuel consumption and overall operating costs.
Resilient and Scalable Photovoltaic Supply Chains: Artificial Intelligence–Enabled Manufacturing Optimization Strategies
The rapid expansion of photovoltaic (PV) technologies has increased the need for manufacturing systems and supply chains that are resilient, scalable, efficient, and sustainable. Traditional PV supply chain operations often face challenges related to demand uncertainty, quality control, equipment failures, inventory imbalances, and global supply disruptions, limiting overall operational performance. This study examines the transformative role of artificial intelligence (AI) in optimizing photovoltaic manufacturing and supply chain management through data-driven decision-making and intelligent automation. AI applications, including predictive analytics, machine learning–based demand forecasting, predictive maintenance, quality assurance, process optimization, and real-time supply chain monitoring, are analyzed for their ability to improve operational efficiency, reduce costs, minimize downtime, and strengthen supply chain resilience. The study also explores the integration of Internet of Things (IoT) technologies and circular economy principles to support sustainable manufacturing and resource optimization in PV systems. The challenges associated with AI adoption, such as implementation complexity, cybersecurity risks, data dependency, and high initial investment, are critically discussed. The findings demonstrate that AI-enabled optimization strategies can significantly enhance the adaptability, reliability, and scalability of photovoltaic supply chains while supporting sustainability goals and renewable energy security. This work provides a comprehensive perspective on AI-driven manufacturing innovation as a pathway toward more resilient and sustainable photovoltaic industry development in the global clean energy transition.
Historical Foundations of Flower Garden Formation and Landscape Design In Uzbekistan
This article analyzes the historical stages of garden and park culture that has long developed in the territory of Uzbekistan. The study focuses on the formation of horticulture, landscape organization, and irrigation systems during the periods of the Bactrian, Sogdian, and Khorezmian civilizations, highlighting the social, aesthetic, and economic significance of the gardens created in these regions. The article also provides a scholarly explanation of the symbolic meaning of plants in Zoroastrian teachings, including the interpretation of trees and flowers as sacred symbols. The findings of the study demonstrate that the ancient garden culture of Uzbekistan serves as an important historical and methodological foundation for modern landscape design.
Public Eating Spaces as Catalysts for Cultural and Educational Life: A Historical-Architectural Analysis of Uzbekistan's Public Catering Infrastructure
This paper analyzes the historical-architectural evolution of public catering buildings in Uzbekistan, tracing their transformation from traditional spatial forms into the Soviet network of "Red Teahouses" (qizil choyxona), alongside their socio-cultural roles. Focusing on the southern regions of Uzbekistan (Kashkadarya and Surxandarya), the study investigates the socio-spatial transformation of catering infrastructure. The methodological framework combines historical-typological analysis of archival and statistical data with mathematical spatial density modeling. The results demonstrate that the Soviet authorities leveraged the high social communication potential of traditional teahouses as part of an "ideological appropriation of space" strategy. While the architectural shells of the buildings were preserved, their internal functional substance was systematically replaced with socialist propaganda and anti-religious hubs (the "League of Militant Atheists"). Furthermore, the research proves that the conversion of sacred architectural sites, such as the Charmgar Mosque in Karshi, into public catering spaces served to reprogram the collective spatial memory of the local population. By the late 1930s, the network transitioned from static built structures to seasonal field-based service models integrated into the agrarian sector, marking a new evolutionary phase. The scientific contribution of this study lies in conceptualizing public catering infrastructure not merely as a commercial asset, but as an active spatial catalyst shaping societal dynamics.
A Dual-Method EES Framework for Temperature-Dependent Elastic Modulus Simulation and Material Selection in Five Metallic Alloys
The paper describes a dual-method Engineering Equation Solver (EES) based simulation tool to calculate the temperature-dependent elastic moduli E(T) for five engineering metallic alloy-type materials: Copper C110, Ti-6Al-4V, Inconel 718, Hastelloy C276, and SS 316L from 20 °C to 600 °C. Two complementary computational techniques were developed and validated: Method A is based on using linear interpolation from experimental lookup tables published in the ASM Handbook, MMPDS-14, Haynes International, and MatWeb; Method B implements quadratic polynomial regression E(T)=aT²+bT+c. Cross-validation of the two methods indicated a maximum deviation of 2.70% for all alloys and the entire thermal range, demonstrating good agreement between the two methods and high predictive accuracy of the polynomial equation model. The five alloys tested exhibited a monotonic decrease in elastic modulus with increasing temperature, with the average elastic modulus decreasing 21.3% at 600 °C when compared to the equivalent elastic moduli at ambient temperature. The alloy Inconel 718 exhibited the greatest relative elastic modulus retention, decreasing only 18.59% at 600 °C from ambient. The maximum thermal stress developed in alloy SS316L (1415 MPa at 600 °C) is primarily due to its relatively high coefficient of thermal expansion (16x10⁻⁶/°C). Results of the thermal sensitivity assessment resulted in the identification of two groups of metal alloys: one group having low sensitivity to temperature fluctuations (Cu C110 and Ti-6Al-4V, |dE/dT| < 0.048 GPa/°C) and a second group having high sensitivity to changes in temperature (Inconel 718, Hastelloy C276, and SS 316L, |dE/dT| > 0.057 GPa/°C). The development of an integrated multi-criteria methodology for evaluating metal alloys based on stiffness, retention loss, thermal sensitivity, and thermal stress produced a ranking of Inconel 718 > Hastelloy C276 > Ti-6Al-4V > Cu C110 > SS 316L, which has provided quantitative criteria that will allow for the selection of alloys that can be utilized in thermally demanding structural applications. The establishment of the dual-method EES process has simplified the entire thermal elastic response of each alloy into three scalar coefficients, which allows for the direct analytical incorporation of those three coefficients into finite element analysis equation solvers and digital twin platforms.
Landscape Design of the Mazlumkhan Sulu Mausoleum Territory: Principles of Cultural Heritage Preservation and Sustainable Development
This article examines the landscape design project developed for the territory surrounding the Mazlumkhan Sulu Mausoleum, one of the significant historical and architectural monuments of Karakalpakstan. The study focuses on the integration of cultural heritage preservation principles with modern landscape architecture approaches. Special attention is paid to spatial organization, green infrastructure, pedestrian circulation systems, environmental sustainability, and visual harmony between the historical monument and its surrounding environment. The proposed landscape design aims to enhance the aesthetic, ecological, and functional value of the site while preserving its historical authenticity. The findings indicate that sustainable landscape planning can significantly contribute to the conservation and promotion of cultural heritage sites.
Copyright (c) 2026 American Journal of Engineering , Mechanics and Architecture https://creativecommons.org/licenses/by/4.0
Eco-Reurbanization and Regenerative Design in Arid Climate Conditions: Transformation of the Architectural Environment of Uzbekistan
This article explores the concept of eco-reurbanization as a tool for adapting the urban environment of Uzbekistan to the challenges of climate change and rapid urbanization. Against the backdrop of increasing building density in megacities such as Tashkent, a decline in air quality and a reduction of green areas are being observed. The study substantiates a transition from harm-minimization strategies to regenerative design, in which architecture functions as a “living ecosystem” that replenishes natural resources.
Sustainable Construction Practices in USA
The construction industry is a critical sector in promoting sustainability objectives because constructions form a major portion of energy use and greenhouse emission levels in the world. Sustainable construction practices have gained importance in the United States in the quest to deal with the environmental issues and achieve long-term climate goals. This study investigates the success of sustainability construction and energy efficiency programs in New York City with particular emphasis to the enactment and implementation of Local Law 33 (LL33), requiring buildings to publicly report their energy performance yearly. The research design adopted in the study is a quantitative research design based on secondary data of the NYC Department of Finance (2021), which comprises more than 18,000 large buildings and such variables as Energy Efficiency Grades (A-D), Energy Star Scores (1-100), and Gross Square Footage. Python, Excel and Tableau were used to calculate descriptive statistics, correlation analysis, and visualization of data to measure the performance trends and spatial variation. Its findings show that most buildings have a Grade A and B, which are very energy-performing, but a significant fraction of buildings are in Grade D and indicate inefficiencies majorly on older or small buildings. Weak correlation between building size and energy efficiency is also found in the analysis indicating that operational practices and adoption of technology are considered to have more influence than scale. The 8th Avenue and Exchange Place are high-performing zones, which point to the areas of successful sustainability implementation. The conclusion of the study is that the use of data transparency policies such as LL33 has made a huge increase in the accountability and performance awareness of building owners. Further policy development and retrofitting incentives or technological advancement is however necessary to realize fair, city-wide development toward sustainable construction and carbon neutrality.
Urban Sustainability and Green Building Policy
Machine Learning-Based Career Recommendation System Using User Interests and Behavioural Analysis
Career choice is an important but often confusing decision for students and people who are laying the path for their future in today’s fast-paced and competitive world. There are so many professional options out there that many find it difficult to identify a career that aligns with their true interests, skills, and long-term goals. The project aims to provide a smart, data-driven solution to make this important decision much easier through a machine learning-based career recommendation system. The system works by analyzing users' hobbies and interests, as well as their responses to a well-designed set of binary (yes/no) questions. Then, the answers are processed by a tree-based classification model to determine the suitable career fields for each person. It provides customized career suggestions in a user-friendly manner, considering each user's preferences and behavioral patterns. This approach demonstrates the increasing potential of artificial intelligence in educational guidance and counseling. This system provides a faster, more flexible, and scalable path to career exploration than traditional aptitude tests or manual evaluations. The results of this project demonstrate how interests and passions, combined with smart algorithms, can provide useful insights into possible future professional opportunities. In the end, this tool helps people make more informed, confident, and meaningful career decisions.
Temporal Evolution of Dominant Periodicity in All India and Kerala Monsoon Rainfall (1901-2024): A Comparative Spectral Analysis
Understanding the temporal organization of monsoon rainfall variability is essential for characterizing long-term climatic behavior over the Indian subcontinent. This study investigates the dominant periodicities in All-India and Kerala annual monsoon rainfall using long-term observational records spanning 1901–2024. Fast Fourier Transform (FFT) analysis is applied to the complete datasets to identify primary variability scales. To assess potential non-stationarity, the 124-year record is divided into three equal epochs and examined through epoch-wise spectral analysis. The results reveal that All-India rainfall exhibits pronounced temporal shifts in dominant periodicities, transitioning from interannual (~2–3 years) variability in the early twentieth century to decadal (~8–14 years) dominance in subsequent epochs. In contrast, Kerala rainfall demonstrates persistent decadal-scale variability throughout the record, with a gradual increase in dominant periodicity from approximately 10 years to around 14 years across successive epochs. The complete Kerala dataset further indicates a prominent quasi-decadal to multi-decadal peak (~17–18 years), underscoring the importance of longer-term oscillatory components at the regional scale. The comparative spectral analysis demonstrates clear regional contrasts in the temporal organization of monsoon rainfall variability. While the aggregated All-India rainfall series reflects evolving and non-stationary dominant periodicities, the Kerala record maintains relatively stable decadal-scale dominance. This contrast suggests that national-scale rainfall integrates multiple time-varying variability modes, whereas regional rainfall may preserve more persistent oscillatory characteristics. Overall, the findings provide statistical evidence of structural evolution in dominant variability scales and contribute to improved understanding of the evolving multi-scale behavior of Indian monsoon rainfall.
The Significance of Measures for the Protection of Mountain and Foothill Roads from Tectonic Movements, Washing, and Erosion
This article examines the significance of protective measures for highways located in mountainous and foothill regions against the impacts of tectonic movements, soil erosion, washing, and related natural processes. The study highlights that intensive precipitation, surface runoff, wind activity, landslides, and geological deformations significantly deteriorate road stability and reduce transportation safety. The research analyzes different types of erosion processes, including water, wind, and irrigation erosion, and their influence on roadbed degradation. Special attention is given to engineering and agrotechnical solutions such as drainage systems, slope reinforcement, geosynthetic materials, and vegetation cover. The findings indicate that integrated protection strategies substantially reduce soil loss, prevent road deformation, and improve long-term durability of road infrastructure. The study concludes that sustainable road management in complex mountainous conditions requires a combination of hydraulic, geotechnical, and ecological approaches to minimize the adverse effects of natural and anthropogenic factors and ensure safe transportation networks across regions worldwide.
Deep Learning-Based Prediction and Analysis of the Influence of Lubrication on Gear Efficiency
This paper involves an analysis and prediction of the effects of spur gears on efficiency based on the use of deep learning. The efficiency of the gear transmission is highly influenced by friction of the two meshing tooth faces, the formation of films of lubricant, power loss, and temperature of operation. Five lubrication conditions were studied in this work: unlubricated reference condition, grease, low-viscosity oil, medium-viscosity oil, and high-viscosity oil. Calculations of torque and speed were used to calculate input power, output power, power loss, and gear efficiency using the experimental data. Alongside, three deep learning algorithms, such as Deep Neural Network, 1D-Convolutional Neural Network, and Long Short-Term Memory network, were implemented to forecast gear efficiency and power loss based on various lubrication circumstances. The experimental data revealed that gear performance was greatly enhanced by the presence of lubrication as opposed to the absence of lubrication. The lowest efficiency of 87.9, highest power loss of 75.9 W and highest operating temperature of 58o C was observed with the unlubricated gear pair, whereas the lowest power loss of 37.2 W and the highest efficiency of 95.4 and 95.7 was observed with the low and high viscosity oil respectively. Medium-viscosity oil produced the best performance with the highest efficiency of 97.7 percent, the lowest power loss of 14.7 W, and the lowest operating temperature of 27 C compared to the unlubricated condition which had higher efficiency, lower power loss, and operating temperature. Deep learning outcome indicated that Deep Neural Network model had the most optimal prediction performance with an efficiency prediction RMSE of 0.29, power loss RMSE of 0.78 W and an R. 2 value of 0.98. The 1D-CNN and LSTM models made acceptable predictions, although, their errors were larger compared to the DNN model. In general, the findings support the view that adequate lubrication enhances the efficiency of gears by preventing the occurrence of frictional losses and heat. The article also shows that deep learning can become an efficient method to predict the gear performance and find the most appropriate lubrication condition.
Optimization of Series and Shunt Capacitor Compensation for Power Factor Improvement in Electrical Power Systems
Background: Power factor improvement stands as a vital requirement because low power factor generates multiple challenges which include rising power loss during transmission and unstable voltage levels and decreased system operational capacity. The solution to these problems arrives through capacitor compensation which uses shunt capacitors as the main solution for actual operational systems. Methods: This study conducted a quantitative data gathering first-hand information from 185 electrical experts completed a standardized survey. The study selected participants demonstrated specific expertise through its purposive sampling technique. Descriptive statistics together with Pearson correlation analysis to study power factor improvement relates to shunt capacitors and series capacitors and load levels and harmonic distortion and voltage levels and power loss reduction. Results: Shunt capacitors serve as the primary solution which users select at 68.1% frequency while hybrid systems follow at 57.8% and users select series capacitors at 51.9%. The primary advantage exists in power factor enhancement which accounts for 40.0% of the benefits followed by efficiency improvement at 28.1%. The correlation analysis demonstrates that shunt capacitor usage produces the most substantial positive correlation with power factor improvement at r = 0.72 and series capacitors create a substantial effect at r = 0.65. Power loss reduction (r =0.60) and voltage level (r =0.54) demonstrate moderate positive relationships. The system performance shows negative effects from harmonic distortion because of the correlation coefficient which reached -0.51. Conclusion: The study establishes that shunt capacitor compensation serves as the main power factor enhancement method which delivers the most effective results.
Copyright (c) 2024 American Journal of Engineering , Mechanics and Architecture (2993-2637)
Advanced Low-Carbon Concrete for Sustainable Infrastructure Development in the United States
Background: The construction industry stands as a major source of worldwide carbon emissions because standard concrete manufacturing generates about seven to eight percent of all global CO2 emissions. The construction industry now receives advanced low-carbon concrete (LCC) technology which functions as an environmentally friendly building material solution. The United States has not accepted these technologies because they face multiple obstacles which include technical problems and economic barriers and regulatory restrictions. Methods: This study employs a quantitative cross-sectional design based on a structured survey of 175 construction industry professionals. A Likert-scale questionnaire was used to assess four key dimensions: environmental awareness, technical performance, economic feasibility, and policy support. The research team used descriptive statistics and Pearson correlation and multiple regression analysis to study the connections between variables which affect LCC adoption. Results: The research data shows that people have strong environmental knowledge with a score of 4.18 and they trust system performance at a level of 4.05 which indicates positive views about LCC technologies. The economic viability assessment shows a score of 3.72 which creates a moderate obstacle for project implementation. The regression model shows that environmental awareness and technical performance and policy support have major effects on adoption rates through R² = 0.64 and p = 0.001 but cost-related elements create adverse effects on decision-making processes. Conclusion: The technical and environmental capabilities of LCC technologies show great potential but their wide adoption faces two major obstacles which include financial restrictions and government policy barriers.
Copyright (c) 2024 American Journal of Engineering , Mechanics and Architectur https://creativecommons.org/licenses/by/4.0