Repair Costs Research Articles

Crack Detection and Cost Analyzer Using AI

Crack Vision is an innovative AI-powered Android application developed to detect and analyze wall cracks, providing users with real-time, automated repair cost estimates. By leveraging advanced object detection models such as YOLOv7 and Mask R-CNN, Crack Vision enhances both the accuracy and efficiency of crack detection, enabling precise measurement of crack dimensions, severity assessment, and quick identification of structural issues. This application simplifies the inspection and budgeting process for wall repairs, making it an essential tool for homeowners, contractors, and structural engineers who seek to minimize reliance on manual inspection methods. The core functionality of Crack Vision combines high-resolution image processing with state-of-the-art machine learning algorithms, ensuring reliable detection across various wall types and crack textures. Using computer vision and deep learning techniques, the app can differentiate between structural and non-structural cracks, thereby offering a comprehensive solution for repair prioritization. Furthermore, Crack Vision integrates with a robust backend that stores user data, allowing users to track the condition of their walls over time. This feature aids in preventive maintenance by identifying trends in crack expansion and deterioration, potentially mitigating costly repairs. Additionally, the application provides a detailed breakdown of estimated repair costs based on crack size, depth, and type, empowering users to make informed decisions about necessary repairs. Crack Vision is designed with user-friendliness in mind, featuring an intuitive interface that guides users through the inspection process. The app also supports multilingual options and customizable settings to cater to diverse user needs. In sum, Crack Vision redefines wall crack inspection, offering a rapid, accurate, and accessible solution for both casual users and industry professionals, ultimately contributing to improved building safety and maintenance efficiency

Legal Study on the Oversight of Overloaded Motor Vehicles: Efforts to Protect Road Safety and Infrastructure

The oversight of motor vehicles exceeding weight limits is critical in safeguarding road safety and protecting infrastructure from premature damage. Overloaded vehicles are a common issue, contributing significantly to accidents and accelerated road deterioration, which incurs high repair costs and disrupts public transport and logistics. This study examines the legal frameworks and regulatory practices aimed at controlling vehicle overloading in Indonesia. Utilizing a normative juridical approach, the research reviews pertinent laws, regulations, and real-world cases involving load limit violations to gain insights into the effectiveness of existing policies. Findings reveal that while stringent load regulations are in place, enforcement remains inconsistent due to various limitations, including outdated monitoring technology, insufficient human resources, and challenges in coordinating between regulatory agencies. The research identifies critical obstacles in the current oversight mechanisms, such as a lack of real-time data collection and limited roadside weighing facilities, which hinder comprehensive monitoring. Furthermore, penalties for overloading are often inadequate to deter repeat violations. Based on these findings, this study recommends a multi-faceted approach to strengthen vehicle load regulation enforcement. Suggested improvements include upgrading monitoring technology, such as installing automatic weigh-in-motion systems, increasing the frequency and coverage of roadside inspections, and implementing more severe penalties to enhance deterrence. Additionally, the study emphasizes the need for inter-agency coordination and public awareness campaigns to foster compliance among vehicle operators. Through these initiatives, Indonesia can better protect its roadways, ensuring both road safety for all users and the long-term sustainability of its infrastructure.

Laparoscopic transabdominal vs. totally extraperitoneal inguinal hernia repair: outcomes, healthcare utilization, and cost differences.

Laparoscopic inguinal hernia repair utilizes either a transabdominal preperitoneal (TAPP) or totally extraperitoneal (TEP) approach. The literature remains mixed on outcomes comparing TAPP versus TEP. The objective of our study was to assess outcomes, healthcare utilization, and cost differences between TAPP and TEP inguinal hernia repair. Adult patients who underwent elective inguinal hernia repair between 2013 and 2021 were retrospectively identified from our institution's electronic health record. Baseline characteristics and postoperative complications were compared using chi-squared test. Multivariable logistic regression was used to model the odds of experiencing a postoperative emergency department visit, readmission, and/or reoperation within 6months were surgery. Generalized linear models were used to investigate differences in cost between TAPP and TEP groups. 1086 patients underwent TAPP repair, while 1277 patients underwent TEP repair. TAPP patients had more than double the rates of readmissions (3.1% vs. 1.3%, p = 0.002) and reoperations (1.5% vs. 0.2%, p = 0.001) within 6months of surgery. On multivariable analysis, undergoing TAPP inguinal hernia repair was associated with higher odds of reoperations and/or readmissions within 6months of surgery (OR 2.8, CI [1.5, 5.1], p = 0.001). TAPP repair had a higher index surgery cost and higher costs associated with reoperations and readmissions compared to TEP repair. Although both approaches are very safe, TAPP inguinal hernia repair had higher rates of postoperative complications, increased healthcare utilization, and higher associated costs. Future studies should be directed toward decreasing postoperative healthcare utilization in order to decrease costs in inguinal hernia repair.

Evaluating the impact of temperature variations and subgrade reactions under traffic-load on airport concrete pavement performance

This study delves into the intricate dynamics of airport concrete pavement slabs under the influence of temperature fluctuations and traffic loads. It presents a comprehensive analysis of the stress distribution and subsequent damage in concrete slabs, factoring in the critical role of dowels in load transfer. The research utilizes advanced finite element simulation ABAQUS to model concrete slabs with varying subgrade reactions, subjected to both temperature gradients and aircraft gear wheel load. Findings reveal that lower subgrade reactions mitigate stress levels under temperature variations, while higher subgrade reactions amplify the stress, particularly around dowel areas and leading to potential damage. The application of the Concrete Damage Plasticity (CDP) model demonstrates that temperature-induced stress combined with traffic load can lead to complete damage around the dowel, compromising the load transfer capability. This paper underscores the necessity of integrating temperature effects and traffic loads into pavement design and maintenance strategies to enhance durability and reduce repair costs.

Fire in house - investigation of the outbreak and spread of the fire in a countryside house

Fire in houses are a significant hazard often resulting in loss of life and substantial repair costs due to damage to the structure and contents. There is a clear need to prevent its ignition and spread to adjacent areas. This article presents the steps taken, namely: contacts, data collection, tests, analysis, and conclusions regarding the accident resulting from the outbreak and spread of a fire, on December 25, 2020, in a traditional Portuguese house in Arganil, Portugal. An expert with specialized technical-scientific knowledge was requested to conduct this investigation, which involved observations and data analysis followed by laboratory tests the work consists of carrying out observations and data analysis. Then, the observed and analysed data were laboratory tested to consistently identify characteristics that lead to the ignition and propagation of the fire. This work identified construction problems, materials used and heat sources that are responsible for the accident. The conclusions may be used to avoid future incidents or accidents with houses of same construction type.

Clinical Outcomes and Costs of Robotic-assisted versus Conventional Mitral Valve Repair: A National Analysis

Robotic approaches have been increasingly utilized for cardiothoracic operations, though concerns regarding costs remain. We evaluated short-term outcomes and costs of robotic-assisted and conventional mitral valve repair (MV-repair), hypothesizing that cost differences would be mitigated at high-volume programs. Adults undergoing elective MV-repair from 2016 to 2020 were identified in the Nationwide Readmissions Database. Patients with rheumatic heart disease, mitral stenosis, and those undergoing concomitant operations were excluded. Generalized linear models were utilized to evaluate the association between approach and in-hospital mortality, complications, length of stay, costs, and 90-day readmissions. Annual institutional MV-repair volume was modeled using restricted cubic splines, and cost differences subsequently evaluated by volume tertile. Of 40,738 patients, 9.8% underwent robotic-assisted MV-repair. Risk-adjusted outcomes including mortality, stroke, reoperation, respiratory complications, postoperative infection, and readmission were comparable between the two groups, while those undergoing robotic-assisted MV-repair had lower rates of non-home discharge. The median cost of robotic-assisted MV-repair was greater than conventional surgery ($46,800 vs $38,500, p<0.001). Despite a 1.3-day decrement (95% CI 1.1-1.6) in length of stay, robotic-assisted MV-repair was associated with greater risk-adjusted costs by $10,500 (95% CI 5,800-15,200). Programs in the highest volume tertile exhibited comparable costs for robotic-assisted and conventional MV-repair (cost difference $5,900, 95% CI -1,200-12,200, p>0.05). Robotic-assisted MV-repair had comparable short-term outcomes relative to conventional surgery. Despite increased costs of robotic-assisted MV-repair overall, high-volume programs had similar risk-adjusted costs by approach. These findings support the designation and performance of robotic MV-repair at centers of excellence in the United States.

Implementation of Risk Management in Asset Management Governance Towards Optimizing Asset Utilization at Public Service Agency State Universities (Case Study of UPN Veteran Yogyakarta)

This study aims to analyze the application of risk management in asset governance at UPN Veteran Yogyakarta to achieve optimal asset utilization. As a Public Service Agency State University (PTN-BLU), UPN Veteran Yogyakarta faces various challenges in asset management, including budget constraints, lack of consistent monitoring, and the unavailability of formal risk evaluation standards. Qualitative descriptive research methods are used to gain in-depth understanding through interviews with asset managers and field observations. The results show that the implementation of risk management can have a significant impact on the efficiency of budget allocation, which includes more appropriate budget distribution based on priority needs and risks. Risk management also has the potential to extend the useful life of assets, with risk mitigation measures taken on assets that show signs of wear or early damage, thus preventing major repair costs due to serious damage. This research highlights that a technology-based monitoring system is an indispensable aspect to support the effectiveness of risk management in asset management. The implementation of structured risk assessment procedures helps in determining asset maintenance priorities, so that the assets that are most critical to the sustainability of campus operations receive greater attention. With the implementation of this comprehensive risk management, optimization of asset utilization can be achieved, which in turn supports the stability and sustainability of UPN Veteran Yogyakarta's operations as a quality higher education institution.

Automatic control system for the electric drive of an overhead crane considering elastic connections

Purpose. Investigation of the peculiarities of the automatic control system of an overhead crane electric drive with regard to elastic connections.. Research methods. To achieve this goal, we used the methods of system analysis and modeling with the help of software tools. This made it possible to reflect accurately the processes occurring in the system, as well as to test various operating scenarios and their impact on the overall system efficiency. Results. The study considered the automatic control system of the electric drive and the importance of taking into account elastic connections. The proposed adaptive system uses the RBF neural network. The use of the proposed controller ensures resistance to disturbing influences and allows to level the load oscillations. The adaptability of the system is ensured by changing the parameters (load, speed of movement of mechanisms, stiffness, positioning accuracy, etc.) to meet the operating conditions of the overhead crane. Thanks to this, the system is able to operate efficiently even under variable loads and external influences. Computer modeling of the proposed control system was carried out, which confirmed its effectiveness under various operating conditions. Scientific novelty.This system provides damping of load oscillations and increases the crane positioning accuracy. This is achieved by comparing it with existing control methods according to various criteria. It is proposed to use an algorithm for adapting the parameters of the control system in real time (load, trolley speed, cable length, mechanism stiffness, etc.), which significantly improves (by 5-7% positioning accuracy, by 8-10% stability) the performance of the system. In addition, the study confirmed the ability of the system to adapt to different operating conditions (changed load, variations in travel speed, uneven external disturbances), ensuring the stability and reliability of its operation, which is especially important for ensuring continuous operation of the crane in industrial environments. Practical value. The use of this system can increase the overhead crane productivity by 5-10% compared to traditional control systems. Implementing the system in an industrial environment will significantly improve the efficiency and safety of the crane, as well as reduce maintenance and repair costs. In addition, this system can be used to modernize existing cranes, which will extend their service life and improve their reliability. This opens up new opportunities to improve the efficiency of industrial processes associated with the use of overhead cranes and provides better working conditions for operators.

Effect of preheating substrates and process parameters on cladding track geometry fabricated by laser cladding

Laser cladding (LC) is an advanced technique used for repairing large-scale components, such as rotors and valves, in thermal power plants and steel rolling mills. This technology is designed to facilitate efficient repairs, minimize repair costs, conserve materials, reduce processing time, and ensure adequate dilution between the substrate material and the deposited coating, while also enhancing corrosion resistance. In this research, the effects of preheating substrates and input process parameters on the geometric accuracy of single-track deposits, as well as the microstructure composition of multi-track deposits, were investigated. The Taguchi method was used to design an L9 orthogonal experimental array. The impact of process parameters, including substrate temperature, laser power, and scanning speed, on the morphology of a single track (track width, height, penetration depth, and dilution) was evaluated through ANOVA statistical analysis. SS316L stainless steel, recognized for its wear resistance, was used as the deposition material. The findings reveal that substrate temperature is the most critical factor influencing the dilution of the coating. Additionally, the microstructure of the deposited layers were systematically analyzed.

Tribomechanical characterization of ceramic reinforced composite coatings for modification of cylinder liner of heavy-duty diesel engines

Heavy-duty diesel engines are essential mechanic power generation tools for industry and transporting freight and passengers. Therefore, increasing engine efficiency and minimizing maintenance and repair costs is crucial. This study examines and characterizes three thermal spray coatings (Cr3C2/NiCr, NiCr, and Al2O3/TiO2) utilized on cylindrical gray cast iron substrate specimens. The coatings were tested for their microstructural (optical microstructure, SEM, EDS, XRD, and AFM topography), mechanical (nanoindentation and nano scratch), and tribological (short and long-distance wear test) properties. Microstructural tests showed that an average coating thickness of 120 to 170 µm was successfully achieved on the cast iron base substrate using different thermal coating methods. The Cr3C2/NiCr coating, observed through the AFM mapping method, achieved the most uniform roughness distribution on the surface. The elasticity of the coatings was measured with a nanoindentation test, and Al2O3/TiO2 coating exhibited high rigidness (177 GPa). The nano scratch test showed that the highest load-carrying capacity was achieved with the Cr3C2/NiCr coated sample. NiCr-coated sample exhibited approximately similar rigidness (61 GPa) to cast iron (67 GPa). Short- and long-distance wear tests showed that the coatings provided significantly better wear resistance than the cast iron base. The coatings’ efficacy was evident. At the long-distance tests, while the cast iron base sample had a wear rate of 19.874 (10−7mm3/Nm), the wear rates of Al2O3/TiO2, Cr3C2/NiCr, and NiCr coatings were 3.835, 4.020, and 6.466 (10−7mm3/Nm), respectively. The Al2O3/TiO2 and Cr3C2/NiCr coatings had superior tribological performance than the NiCr coating, yet the NiCr coating showed much higher wear performance than the uncoated base. The Cr3C2/NiCr coating exhibited superior tribomechanical performance in all the coatings.

Digital Flexible Ureteroscope: Evaluating Factors Responsible for Damage and Implementing a Mandatory Certification Program for Usage.

Objective: Digital flexible ureteroscopes (DFUs) play a crucial role in endourological procedures, and scope breakages are often avoidable with proper measures in place. We aimed to evaluate the effect of mandatory training on DFU durability and to assess the influence of various factors on instrument damage. Materials and Methods: Mandatory training involving instructional videos on DFU care was introduced for all processing and operating room staff handling DFUs. Only certified personnel were allowed to handle DFUs, with meticulous usage documentation. The average annual usage of each DFU was compared pre and post program implementation. We examined factors such as patient demographics, procedure details, and operator experience impacting scope life using negative binomial regression. Results: The average usage of DFU increased by 21%, from 6.38 to 7.74 cases. We found that post-graduate year stood out as a significant predictive factor (estimate = 3.28, p = 0.04). Moreover, the streamlined model revealed that previous ureteral-stent use (estimate = 0.94, p < 0.001), struvite stones (estimate = 3.08, p = 0.01), and a higher number of stones (estimate = 0.11, p = 0.04) were associated with an increased number of procedures before DFU breakage, whreas in situ lithotripsy in the lower calyx was associated with a reduced number of procedures before DFU breakage (estimate = -1.11, p = 0.003). Conclusion: Implementing a mandatory training program showed an increase in DFU durability by 21%, suggesting a potential reduction in annual repair and replacement costs by the same percentage. Furthermore, outcomes were more favorable when experienced practitioners conducted treatments, especially in cases involving struvite stones.

Uniqueness of suspiciousness scores: towards boosting evolutionary fault localization

Context. Software is subject to the presence of faults, which impacts its quality as well as production and maintenance costs. Evolutionary fault localization uses data from the test activity (test spectra) as a source of information about defects, and its automation aims to obtain better accuracy and lower software repair cost. Motivation. Our analysis identified that the test spectra commonly used in research exhibit a high ratio of sample repetition, which impairs the training and evolution of Genetic Programming (GP) evolved heuristics. Problem. We investigate whether evolutionary training based on the uniqueness of suspiciousness scores can increase the ability to find software faults (defects), even in repeat-sample scenarios. Specifically, we examine whether the GP-evolved heuristic, which is based on the distinguishability of program elements in terms of faults, is really competitive. Methodology. The investigation formalized hypotheses, introduced three training strategies to guide the research and carried out an experimental evaluation, aiming to reach conclusions regarding the assessment of research questions and hypotheses. Analysis. The results have shown the competitiveness of all the proposed training strategies through evaluation metrics commonly used in the research field. Conclusion. Statistical analyses confirmed that the uniqueness of suspiciousness scores guides the generation of superior heuristics for fault localization.

Time-Driven, Activity-Based Cost Analysis of Pars Plana Vitrectomy in Rhegmatogenous Retinal Detachment at a Large Academic Center.

Purpose: To perform a time-driven, activity-based cost analysis of retinal detachment (RD) surgery and compare it with reimbursement rates. Methods: This economic analysis at a single academic institution used time-driven, activity-based costing methodology to determine the cost of rhegmatogenous RD repair with primary pars plana vitrectomy. A process flow map was created to highlight each surgical case's operative episodes, including clinical follow-ups. Time logs were obtained from the electronic health record for each operative phase and clinical follow-up. The overhead and anesthesia costs were collected from the institution's cost accounting system. The direct material and personnel costs were obtained from internal financial data. Results: Seventy-six cases that met the inclusion criteria were included in the cost analysis study. The time-driven, activity-based cost of RD was $6247.17, and the reimbursement was $5442.91. Therefore, each procedure had a net negative loss of $804.26. To break even, the average operation time would need to be reduced from the determined average of 90.49 minutes to 64.90 minutes. Conclusions: This study found that Medicare underestimates the true cost of RD surgery. Changes in referral patterns may be motivated by reimbursement rates lower than the cost of the procedure, which could ultimately affect patient access to care.

A LSTM algorithm-driven deep learning approach to estimating repair and maintenance costs of apartment buildings

PurposeThis study proposes a deep learning algorithm-based model to predict the repair and maintenance costs of apartment buildings, by collecting repair and maintenance cost data that were incurred in an actual apartment complex. More specifically, a long short-term memory (LSTM) algorithm was adopted to develop the prediction model, while the robustness of the model was verified by recurrent neural networks (RNN) and gated recurrent units (GRU) models.Design/methodology/approachRepair and maintenance cost data incurred in actual apartment complexes is collected, along with various input variables, such as repair and maintenance timing (calendar year), usage types, building ages, temperature, precipitation, wind speed, humidity and solar radiation. Then, the LSTM algorithm is employed to predict the costs, while two other learning models (RNN and GRU) are taught to validate the robustness of the LSTM model based on R-squared values, mean absolute errors and root mean square errors.FindingsThe LSTM model’s learning is more accurate and reliable to predict repair and maintenance costs of apartment complex, compared to the RNN and GRU models’ learning performance. The proposed model provides a valuable tool that can contribute to mitigating financial management risks and reducing losses in forthcoming apartment construction projects.Originality/valueGathering a real-world high-quality data set of apartment’s repair and maintenance costs, this study provides a highly reliable prediction model that can respond to various scenarios to help apartment complex managers plan resources more efficiently, and manage the budget required for repair and maintenance more effectively.

Smart retrofitting for office buildings: Comparison of decision-making criteria between developing and developed regions

Given the constantly evolving nature of modern infrastructure, retrofitting existing buildings with state-of-the-art smart technologies presents a pivotal opportunity, enabling promising advancements in sustainability, efficiency and adaptability. Focusing on Hong Kong and Sri Lanka, which are developed and developing regions respectively, this study investigates the decision-making criteria for selecting smart systems as retrofits for office buildings. Using the analytic network process (ANP), 24 expert interviews were conducted in Hong Kong and Sri Lanka to determine the importance weights of the decision-making criteria. The findings show varied importance weights for each criterion between the two regions, with Hong Kong emphasizing upfront financial considerations, specifically 'investment cost' while Sri Lanka emphasizing long-term operational sustainability, specifically 'recurring operation, maintenance and repair cost.' Despite these differences, there are similarities in the importance of core criteria such as 'reliability of retrofit option' and 'ability to increase returns.' The lack of focus on user-centric issues in Hong Kong and implementation of retrofit projects in Sri Lanka underscore the need for decision-makers to prioritize these aspects in future projects. The principal contribution of this study is in offering insights from a thorough analysis of the importance weights of the essential criteria in making decisions regarding smart retrofitting (SR). It is also beneficial to various stakeholders, including building owners and facility managers. The cross-region study approach, enabling the investigation on both developed and developing regions, further serves as a foundation for future research aiming to explore and analyze the influence of regional difference in decision-making for SR.

Analysis of postdisaster economy using high-resolution disaster and economy simulations.

We present an integrated framework that utilizes high-resolution seamless simulations of disasters and national economies for estimating the economic impacts of disasters. The framework consists of three components: a physics-based simulator to simulate the disaster and estimate the response of the infrastructure; a tool that estimates the losses suffered by the infrastructure based on its response; and an agent-based economic model (ABEM) that simulates the national economy considering the infrastructure damage and postdisaster decisions of the economic entities. The ABEM used in the framework has been implemented in a high-performance computing environment to simulate large economies at 1:1 scale. Furthermore, it has been calibrated to the Japanese economy using publicly available macroeconomic data and validated to the Japanese economy under the business-as-usual scenario. We demonstrate the integrated framework by simulating a potential Nankai-trough earthquake disaster and estimating its impacts on the Japanese economy. The seismic response of 1.8 million buildings of the Osaka-bay area has been estimated using a large-scale earthquake disaster simulator and corresponding repair costs are estimated using the Performance Assessment Calculation Tool. As per our estimates, repair costs amount to approximately 15 trillion Yen. Considering the investments made by impacted households and firms toward recovery, the postdisaster economy is simulated using the ABEM for 5 years under two recovery scenarios. Industrial production is expected to recover in three quarters whereas 10-13 quarters will be required to finish all the repairwork.

Electrical resistivity of cement-based materials through ion conduction mechanisms for enhancing resilient infrastructures

Cement-based materials (CBM) in humid environments, influenced by their inherent defects and the presence of pore solution, exhibit poor electrical insulation performance. Low electrical resistivity of cement-based materials poses a threat to the safety of resilient infrastructures, shortens the lifespan of materials, and increases the costs of maintenance and repair. In this work, we first elucidate two primary mechanisms for enhancing electrical resistivity: (1) inhibition of ion electromigration and (2) disruption of conduction paths. We then systematically summarize and discuss 16 potential methods for improving their electrical resistivity based on these mechanisms. It is indicated that among these 16 methods, early carbonation curing, the addition of high-activity mineral admixtures, and surface hydrophobic modification are particularly effective approaches. The combination of two or more methods can simultaneously exert their functions, thus maximizing the overall effectiveness. Future work is outlined with the aim of meeting the growing demand for cement-based materials with high electrical resistivity in the construction of resilient infrastructures.

An aero-engine remaining useful life prediction model based on clustering analysis and the improved GRU-TCN

Abstract Accurately predicting the remaining useful life (RUL) of engines is paramount for implementing effective preventive maintenance strategies, preventing injuries and fatalities caused by equipment failures, and significantly reducing routine repair and replacement costs. However, existing deep learning models often ignore the variable operating conditions in real engineering applications and do not sufficiently consider the interaction between time series and degradation laws, which directly leads to the inability to effectively extract to degradation feature extraction. To address this problem, this study developed a novel combined network model named CA-DRGRU-TTCN, aimed at accurately predicting the RUL of engines. Firstly, a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to identify multiple operating conditions, and incorporate the recognition results into the model as additional new features. The first degradation time point is determined by JS divergence. Secondly, the deep connectivity of the residual Gated Recurrent Unit (GRU) module is designed to extract deeper degradation features, and an improved TMSE loss function based on the first degradation time point is applied to Temporal Convolutional Networks (TCN) to better capture the dependency between the time series and the real degradation degree of the engine. Finally, experiment results on the C-MAPSS dataset show that the proposed method achieves better performance compared to existing state-of-the-art methods.&amp;#xD;

Performance of the polyurethane foam injection technique for road maintenance applications

Road infrastructure plays an important role in strengthening transportation and driving the economic advancement of countries. However, the increasing traffic volume has accelerated road deterioration, particularly at critical points like bridge-road junctions. Traditional repair methods involving demolition and reconstruction lead to extended closures and high costs. This study explores the polyurethane (PU) foam injection technique as an alternative solution, which can reduce both repair time and costs. The research evaluates the application of PU foam in various road projects across Thailand, highlighting its ability to repair pavement surfaces and structures, even in severely damaged areas. Despite its advantages, the use of PU foam faces challenges due to a lack of standardized quality control. This paper proposes a set of working guidelines for PU foam injection, aimed at key stakeholders such as the Department of Highways, the Department of Rural Roads, and the Department of Local Administration. The findings underline the importance of establishing standardized methods to ensure the long-term effectiveness of PU foam in road maintenance. Future research should focus on refining these guidelines for diverse road conditions to support the sustainable development of national transportation infrastructure

The study of an Economic Integration of a Microgrid for the University of Management and Technology Sialkot

The finest microgrid design has been proposed in this research to ensure a smooth energy supply for the University of Management and Technology (UMT) Lahore, Sialkot Iqbal campus, Pakistan at the cheapest price. The design methodology considered resource availability, environmental feasibility, economic feasibility, and renewable integration. The university can utilize the energy from the Sun, fossil fuel, and national grid. Integrating the available energy resources, data collection, and forecasting multiple parameters were simulated to design the optimal microgrid. The optimal design is selected based on the net present cost (NPC), renewable fraction (RF), operating cost (OC), repair and maintenance cost (O and MC), etc. Out of the 48 viable simulated designs, the optimal design has a PV system, generators, and the grid to fulfill energy demand at a net present cost of $116,987, with a renewable portion of 95%. In contrast, the worst simulated design depends on generators and battery backup, resulting in a $2.93 million NPC with a renewable segment of 24.3%.