Future Maintenance Research Articles

Statistic and modal analysis of the Krk bridge utilizing ANSYS

Abstract. Connecting mainland Croatia and the island of Krk, Krk Bridge lies upon the Krk Channel of the Adriatic Sea. The bridge is one of the longest arch bridges and was the longest when it was built worldwide. However, considering the harsh environment in which the bridge is located, the slender structure of the bridge has raised concerns about the system's stability. However, with the most widely investigated studies focusing on the corrosion impact on the material and finish of the bridge from the harsh environment, there remains largely unexamined structural analysis to be done. Hence, this study seeks to use ANSYS and Workbench to investigate the reliability of Krk Bridge concerning statistical structure and modal analysis to offer insights into the future maintenance and reinforcement of the bridge itself, as well as provide a reference for other infrastructural bridge projects in the future.

Maintenance Therapy Post-Stem Cell Transplantation for Patients with T-Cell Lymphomas.

Given the poor outcomes for peripheral T-cell lymphomas (PTCL), stem cell transplant (SCT) remains an important therapeutic approach. Post-SCT relapse is common and maintenance therapy post-SCT is increasingly being utilized. Here we review the use of post-SCT maintenance therapy for PTCL patients. Maintenance therapy is increasingly utilized to decrease post-SCT relapse and improve outcomes in PTCL. Ongoing and completed post-SCT maintenance trials utilizing agents such as romidepsin, brentuximab vedotin, duvelisib, and pembrolizumab have shown efficacy in decreasing relapse. Further, additional agents with efficacy in PTCL have emerged that may inform future maintenance approaches. Maintenance therapy is a promising approach to maintain response after SCT in PTCL. While several trials are ongoing to evaluate maintenance therapy in PTCL, current data suggests this may be an effective method to decrease post-SCT relapse.

InChI 1.07 available on GitHub

Abstract The latest InChI version 1.07 has been approved by IUPAC and the InChI Trust—a transformational step in our development to support the standard. Now on Github, with a standard MIT license—fully tested for back-compatibility, many bugfixes, and a foundation for future extension and maintenance.

Characterization of Wear Patterns in Gun Barrel’s Throat Region Using Forensic Microscope: Insights Into Long-Term Effects of High-Pressure Gas Exposure

Objectives: This research aims to explore the damage sustained by the gun barrel's throat region over a long period of use, shedding light on the wear characteristics, and providing valuable insights into the long-term effects of high-pressure gas exposure during firing. Methods: To achieve this objective, the appearance of the gun barrel's throat region was characterized using forensic microscopy techniques. Visual and microscopic examinations were conducted on five (5) gun barrels with ten years or less of service usage and five (5) gun barrels with over ten years of service usage from the Philippine National Police to assess the degree of wear in the barrel’s throat region. Findings: The findings revealed varying degrees of wear in the barrel throat regions. Firearms with 10 years or less of service usage exhibited moderate corrosion, narrow cracks, and minimal soot particle presence while firearms with over 10 years of service usage showed severe corrosion, deep cracks, scattered potholes, superficial scraped surfaces, and heavy soot particle presence. Novelty: This research contributes to the understanding of barrel performance, maintenance, and potential hazards associated with aging firearms, thus providing novel insights into the long-term effects of high-pressure gas exposure during firing. By examining the changes in material properties and surface roughness of the gun barrel’s throat region, this study seeks to improve the understanding of how repeated firing impacts gun barrel performance and longevity. This, in turn, may aid in developing future maintenance practices to enhance firearm durability and safety. Keywords: Gun Barrels, Wear Characteristics, Gun Barrel Throat Region, High-Pressure Gas Exposure, Wear Patterns

Shui Su Ravine Bridge Design and its Steel Joint Fatigue Performance Evaluation

AbstractShui Su Ravine Bridge is built in Bai Shui County in Shaanxi Province, which is a part of the highway linking Heyang and Tongchuan. The bridge has been designed as a composite steel‐concrete truss bridge with 3 spans in total of 240 meters and a deck of 4 lanes. To meet the harsh environmental demand and lessen the future maintenance, the solution adopts the weathering steel Q420qENH similar strength as S420NL in European code. The bridge is erected by incremental launching and post‐installation of the pre‐cast concrete slab. Since the truss segment is all welded, two‐step models have been developed to evaluate the fatigue performance of the steel truss joint. In the simplified two‐dimensional model, Phase‐Field (PF) approach has been implemented to make a full fatigue evaluation of crack propagation in the truss.

Artificial Intelligence Techniques in Automated Vehicle Systems: A Comprehensive Review

Artificial intelligence (AI) has changed the way automatic car systems work, making them safer, more efficient, and able to drive themselves. This in-depth review looks at the many different AI methods that are used to create and improve automatic car systems. The main focus of this study is on machine learning methods, such as supervised, unsupervised, and reinforcement learning, and how they can be used to help vehicles understand, make decisions, and be controlled. Supervised learning, especially deep learning, is a key part of finding and classifying objects, which is needed to tell the difference between people, cars, and road signs. Many people use Convolutional Neural Networks (CNNs) because they are very good at handling images and videos accurately, which makes real-time responses easier in changing driving situations. Unsupervised learning methods, like grouping and anomaly detection, make systems more reliable by finding strange trends and behaviors. This makes it easier to know what's going on and plan for future maintenance. Vehicles can learn from interacting with their surroundings thanks to reinforcement learning, which is a key part of improving decision-making. This method is very important for planning routes, adaptive speed control, and avoiding collisions, which makes sure that self-driving cars can handle complicated situations safely and quickly. Also talked about are sensor fusion methods that combine data from LiDAR, radar, and video to give a more complete picture of the surroundings and provide extra information. The review also talks about the moral issues and legal problems that come up with AI-driven self-driving cars. To get a full picture of the field, things like computer openness, data protection, and the moral effects of making choices in tough scenarios are looked at. This review highlights the changing potential of AI in automated car systems by putting together the latest research and developments. It also gives us a look into the directions and improvements that will come next. The goal of this study is to be a useful resource for students, practitioners, and lawmakers who are interested in how autonomous driving technologies are changing over time.

Investigating the Role of Software Quality Managers and Software Developers in Creating Code Smells

Software code smells are regarded as signs of potential problems in code design or implementation, they might not affect functionality but impact future maintenance and readability. However, refactoring is a process of code transformation that improves code structure without changing code behavior. This process occurs in the stage of software maintenance where software developers and software quality managers/engineers must work closely in order to identify code smells and plan for refactoring. This research aims to investigate the role of quality managers and software developers in creating code smells.

The application of the Bayesian linear regression model to optimize the maintenance of a programmable logic controller

In this paper, an optimization approach, which is based on the Bayesian Linear Inference (BLI) model, has been proposed for the maintenance of Programmable Logic Controllers (PLCs). The BLI model, which is implemented using historical data, incorporates maintenance indicators like the number of failures (NF), total downtime (TD), total unexpected intervals (TUI), mean time to repair (MTTR) and mean time between failures (MTBF). It offers a probabilistic framework for determining the influence of each predictor variable on PLC maintenance. The model produces posterior means, credible intervals, and standard deviations, which provide insights into the magnitude and uncertainty of these relationships. The results from the study show that factors like NF and TD are influenced by the magnitude and direction of the maintenance levels. Also, the R-squared score (0.85) also indicates how much of the variability in maintenance in the system. From the results obtained, the study can conclude that the BLI model can optimize PLC maintenance procedures by identifying essential components and their contributions. Also, it is able to estimate future maintenance requirements and helps with resource allocation and process optimization decisions.

Environmental impact and cost study of vehicles

In the traditional automobile industry, which was dominant from the mid-20th century to the early 21st century, the nitrogen oxides produced by auto mobile exhaust would pollute the air. After entering the 21st century, with lithium batteries as the representative, the energy density of energy storage units has rapidly increased, solving the endurance problem that has plagued the electric vehicle industry for nearly a century. The development path of electric vehicles has become wider and gradually become mainstream products in the market. This paper explores the environmental impact of transitioning from partial to complete tram usage in an urban area with a population of at least 500,000. Initially, this paper calculates the Ecological Evaluation Index to compare the environmental effects of the two types of vehicles. Subsequently, introducing four variables to set the entire model in motion, aiming to predict the environmental conditions of the urban area when entirely reliant on tram services. Furthermore, this paper considers the realistic financial issues that need to be faced and build a model for the financial impact that the transition to pure electric buses will soon bring. The excessive costs and potential external funds caused by the transition, which will affect the final ten-year transformation plan for the city. Finally, this transit company has successfully achieved an operational income of up to 13.3 billion over the past decade even in the absence of government subsidies. This underscores the robust foundation of its financial operations and economic sustainability, providing reliable financial support for future electric bus procurement and maintenance.

Scan-to-HBIM-to-VR: An Integrated Approach for the Documentation of an Industrial Archaeology Building

In this paper, we propose a comprehensive and optimised workflow for the documentation and the future maintenance and management of a historical building, integrating the state of the art of different techniques, in the challenging context of industrial archaeology. This approach has been applied to the hydraulic work of the “Sostegno del Battiferro” in Bologna, Italy, an example of built industrial heritage whose construction began in 1439 and remains in active use nowadays to control the Navile canal water flow rate. The initial step was the definition of a 3D topographic frame, including geodetic measurements, which served as a reference for the complete 3D survey integrating Terrestrial Laser Scanning (TLS), Structured Light Projection scanning, and the photogrammetric processing of Unmanned Aircraft System (UAS) imagery through a Structure from Motion (SfM) approach. The resulting 3D point cloud has supported as-built parametric modelling (Scan-to-BIM) with the consequent extraction of plans and sections. Finally, the Heritage/Historic Building Information Modelling (HBIM) model generated was rendered and tested for a VR-based immersive experience. Building Information Modelling (BIM) and virtual reality (VR) applications were tested as a support for the management of the building, the maintenance of the hydraulic system, and the training of qualified technicians. In addition, considering the historical value of the surveyed building, the methodology was also applied for dissemination purposes.

Spatiotemporal Analysis of Jorgo Wato Forest Cover Changes, West Wallagga, Oromia, Ethiopi

A study was conducted in the Jorgo Wato forest, located in the West Wallagga zone and extending to the Buno Bedelle zone of Oromia National Regional State, with the objective of assessing the dynamics of forest cover change in the Jorgo Wato forest over the last three decades (1986–2016). In order to assess forest cover changes, the whole study period was categorized into three periods: 1986–1995, 1995–2006, and 2006–2016. Satellite images from Landsat TM, ETM+, and OLI/TIRS were used in this study. Support Vector Machines for supervised classification and post-classification were used for image classification, with results showing an overall accuracy of up to 99.49%, maximum producer’s accuracies of up to 99.80, and User’s accuracies of up to 100%, while the Kappa coefficient ranged between 96.98% and 99.17%. The result of the change analysis revealed that forests changed from 67.62% in 1986 to 74.77% in 2016, with an average rate of change of 20.13ha/year from 1986 to 2016, whereas farmland changed from 19.60% in 1986 to 12.28% in 2016, with a variation through 30 years and an average rate of change of -20.604ha/year from 1986-2016. Changes in forest cover increment have come from the expansion of coffee plantations and plantations of different tree species by the communities and by Oromia Forest and Wildlife Enterprise till now. Monoculture plantations cannot take the place of a natural forest and cannot support a lot of biodiversity, which is crucial for the management of natural resources. Future maintenance of the region's environmental equilibrium will require a method of combining both systems and concentrating more diverse species.

Field Study of Asphalt Pavement Texture and Skid Resistance under Traffic Polishing Using 0.01 mm 3D Images

Pavement texture and skid resistance are pivotal surface features of roadway to traffic safety, especially under wet weather. Engineering interventions should be scheduled periodically to restore these features as they deteriorate over time under traffic polishing. While many studies have investigated the effects of traffic polishing on pavement texture and skid resistance through laboratory experiments, the absence of real-world traffic and environmental factors in these studies may limit the generalization of their findings. This study addresses this research gap by conducting a comprehensive field study of pavement texture and skid resistance under traffic polishing in the real world. A total of thirty pairs of pavement texture and friction data were systematically collected from three distinct locations with different levels of traffic polishing (middle, right wheel path, and edge) along an asphalt pavement in Oklahoma, USA. Data acquisition utilized a laser imaging device to reconstruct 0.01 mm 3D images to characterize pavement texture and a Dynamic Friction Tester to evaluate pavement friction at different speeds. Twenty 3D areal parameters were calculated on whole images, macrotexture images, and microtexture images to investigate the effects of traffic polishing on pavement texture from different perspectives. Then, texture parameters and testing speeds were combined to develop friction prediction models via linear and nonlinear methodologies. The results indicate that Random Forest models with identified inputs achieved excellent performance for non-contact friction evaluation. Last, the friction decrease rate was discussed to estimate the timing of future maintenance to restore skid resistance. This study provides more insights into how engineers should plan maintenance to restore pavement texture and friction considering real-world traffic polishing.

Performability analysis for the SPVC line system of leaf spring production plant using probabilistic approach

PurposeThis work ensures the higher performability of this complex system, which consists of five different subsystems, i.e. shearing machine, V-cutting machine, center hole punch, edge cutting burr and drilling machine. These subsystems are placed in combinations of both series and parallel arrangement. The concerned plant management must be aware of the failures that have the greatest/least impact on the system’s performance.Design/methodology/approachPerformability analysis has been done for the Shearing, Punch and V- Cutting (SPVC) line system by using a probabilistic approach (i.e. Markov method). This system was further divided into five subsystems, and single-order differential equations are derived using the transition diagram. MATLAB software was used to determine the performability of the system for various combinations of repair and failure rates.FindingsIn this research work, performability analysis was done using different combinations of repair and failure rates for these subsystems. Further, a decision matrix (DM) has been developed that indicates that edge cutting burr is the most critical subsystem, which requires the top level of maintenance priorities among the various subsystems. This matrix will facilitate policymaking related to various maintenance activities for the respective system.Originality/valueIn this research work, a mathematical modeling based on a single differential equation using a transition diagram has been developed for the SPVC line system. The novelty of this work is to consider interaction among different subsystem, which generates more realistic situation during modeling. The purposed DM helps make future maintenance planning, which reduces maintenance costs and enhances system's performability.

Intelligent Monitoring System for Integrated Management of Historical Buildings

This study demonstrates the effectiveness of a multi-method approach for the restoration of a historic building (train station) in Poland. The project employed field investigations, laboratory analyses, and close-range photogrammetry to create a Historic Building Information Model (HBIM). This comprehensive data set informed the development of targeted conservation strategies that addressed the station’s specific needs while respecting its historical significance. Interventions prioritized the use of locally sourced and sustainable materials, minimized the visual impact on the exterior, and achieved net-zero emissions through improvements to the building envelope and a switch to a heat pump heating system. Additionally, an intelligent monitoring system was implemented to continuously collect data on environmental conditions and structural displacement. These data will be used to develop a predictive model for future maintenance needs, allowing for a preventative approach to conservation and minimizing resource consumption. Overall, this project serves as a model for integrating advanced technologies in historical building conservation, promoting sustainable practices, and ensuring the longevity of irreplaceable cultural landmarks. The key findings derived from this approach encompass a comprehensive assessment of the station’s condition, optimized conservation strategies, insights from HBIM modeling, and the ongoing benefits of the intelligent monitoring system. Field investigations revealed several areas of concern, such as structural cracks, material deterioration, moisture infiltration, and significant heat loss through the building envelope. This information was crucial for developing targeted conservation strategies. The use of internal thermal insulation systems, particularly capillary active mineral blocks, significantly improved thermal performance. Moisture management interventions, including the restoration of the rainwater drainage system and the application of moisture-proof insulation, reduced reliance on the municipal water supply. The intelligent monitoring system, with sensors measuring temperature, humidity, and structural displacement, plays a crucial role in ongoing conservation efforts. This system allows for continuous monitoring and the development of predictive models, ensuring targeted and preventative maintenance, reducing resource consumption, and extending the lifespan of the building.

Optimizing regression testing with AHP-TOPSIS metric system for effective technical debt evaluation

Regression testing is essential to ensure that the actual software product confirms the expected requirements following modification. However, it can be costly and time-consuming. To address this issue, various approaches have been proposed for selecting test cases that provide adequate coverage of the modified software. Nonetheless, problems related to omitting and/or rerunning unnecessary test cases continue to pose challenges, particularly with regard to technical debt (TD) resulting from code coverage shortcomings and/or overtesting. In the case of testing-related shortcomings, incurring TD may result in cost and time savings in the short run, but it can lead to future maintenance and testing expenses. Most prior studies have treated test case selection as a single-objective or two-objective optimization problem. This study introduces a multi-objective decision-making approach to quantify and evaluate TD in regression testing. The proposed approach combines the analytic-hierarchy-process (AHP) method and the technique of order preference by similarity to an ideal solution (TOPSIS) to select the most ideal test cases in terms of objective values defined by the test cost, code coverage, and test risk. This approach effectively manages the software regression testing problems. The AHP method was used to eliminate subjective bias when optimizing objective weights, while the TOPSIS method was employed to evaluate and select test-case alternatives based on TD. The effectiveness of this approach was compared to that of a specific multi-objective optimization method and a standard coverage methodology. Unlike other approaches, our proposed approach always accepts solutions based on balanced decisions by considering modifications and using risk analysis and testing costs against potential technical debt. The results demonstrate that our proposed approach reduces both TD and regression testing efforts.

GSE-YOLOv5s: A lightweight visual detection method for first wall tile and bolts (Holes) in CFETR

The detection of the first wall tile and bolts (holes) of CFETR is crucial for improving the efficiency of remote operation maintenance. The internal environment of CFETR is complex, and the existing target detection algorithms lack precision, are prone to missed detection, and occupy substantial computational resources. We introduce a method for detecting tiles and bolts (holes) using a lightweight network. First, a preliminary research platform is established to build a dataset. Secondly, based on YOLOv5s, the GhostBottleNeck module replaces the BottleNeck in the C3 module to build a C3Ghost structure, reducing model computation and parameters. A fast spatial pooling pyramid structure, SimSPPF module, is proposed to replace the SPPF module, increasing model speed. The replacement of the CIOU loss function with EIOU enhances positional accuracy. Comparative experimental results indicate this network outperforms baseline YOLOv5s by increasing the mAP by 1.4 %, decreasing parameters by 13.8 %, and reducing floating-point operations (FLOPS) by 13.2 %. This study holds considerable reference value for future remote visual maintenance in CFETR.

Visitors' recreational & leisure experiences in urban river corridors? A case study of Hutuo River in Shijiazhuang, China

Various physical environmental factors influence the recreational experience of urban river corridors, and understanding visitors' perceptions of these factors holds significant implications for promoting the health and well-being of urban residents. This study specifically focuses on the Hutuo River corridor in Shijiazhuang, China, and conducted a semi-structured questionnaire survey with 620 participants. The findings reveal that visitors mainly come here to socialize with family and friends in a natural environment and escape the city's hustle and bustle. Safety, management and maintenance, and overall conditions are the primary features visitors are concerned about in their experience. When addressing usage constraints, visitors highlight accessibility issues, including traffic congestion and overcrowding. Additional limitations encompass deficiencies in physical facilities, such as shortages of shared bicycles, convenience amenities, fitness facilities, and services catering to vulnerable groups. Moreover,challenges include unavailable toilet services and insufficient shaded areas along the riverbanks. Sociodemographic attributes such as gender, age, marital status,education level, monthly income, and residential distance significantly influence the recreational experience of urban river corridors, emphasizing the importance of considering diversity in urban planning and design. By comprehensively exploring the dynamics of urban river corridor utilization from a social-ecological perspective, our study's findings offer insights relevant to future design and maintenance endeavors.

Low impact development devices DNA of cities for long term stormwater management strategies

In 2024 the Low Impact Development Devices (LID) open-source international database ClimateScan consist of over 14.000 climate adaption related projects uploaded in the period of 2014–2024. For cities with over 500 projects, this offers an opportunity to construct a LID-DNA of the city. LID-DNA presents the ‘genetic information of the development and functioning of LID in a city’ and was first used in The Netherlands during ClimateCafés as evaluation for future design and maintenance of stormwater management strategies. The LID-DNA of several cities based on the quantity and categories of LID is visualized. The LID structure of early adaptor Amsterdam with over 500 LID measures implemented in 2000–2024, shows a large variety of over 20 types of individual LID. The relative new adaptor Riga shows a LID-DNA with a focus on bio-filtration with raingardens and swales (based on 40 data points). Stakeholders from different departments concluded that cities benefit from the insights of their urban LID-DNA earlier in the process. An early insight will support a targeted LID strategy choosing a limited cost-efficient group of LID than having a wide range of different LID without evaluation of their efficiency. Departments in the city asked for more detailed insights (earlier in the process) to prevent mal-adaptation and disinvestments and be more efficient with their capacity. The ClimateScan database holds over 300 monitored LID projects with research results in North America and Europe in cities as Vancouver, New Orleans, Amsterdam and Riga. Future work will focus on more detailed LID-DNA visualisation based on not only the amount of LID but on the dimensions such as water storage (m3) and surface (m2). Monitoring of LID will be stimulated to make strategic decisions on measured infiltration rates (m/d) of LID as most important criteria for possible damage by floodings and maintenance (clogging). Raising awareness and capacity building targeted on the high-ranking cost-efficient LID is set up in both cities focused on the design, construction and maintenance of LID.

Long-term paddy use influences response of methane production, arsenic mobility and speciation to future higher temperatures

Anaerobic microbial metabolisms make flooded paddy soils a major source of the greenhouse gas methane (CH4) and mobilize toxic arsenic (As), threatening rice production and consumption. Increasing temperatures due to climate change enhance these microbially mediated processes, increasing their related threats. Chronosequence studies show that long-term paddy use (“age”) changes soil properties and redox biogeochemistry through soil organic carbon (SOC) accumulation, its association to amorphous iron (Fe) phases, and increased microbial activity. Using paddy and non-paddy soils from a chronosequence as proxies of soil development and incubating them at different temperatures, we show that paddy soil age influences the response of paddies to changes in temperature. Older paddies showed up to a 6-fold higher CH4 production with increasing temperature, compared to a 2-fold increase in young ones. Contrarily, changes in As mobility were higher in non-paddies and young paddies due to a lack of Fe-SOC-sorption sites. Temperature increased the formation of phytotoxic methylated As in all paddies, posing a risk for rice production. Mitigation strategies for future maintenance, abandonment, or management of paddy soils should include the consideration that history of use shapes the soils' biogeochemistry and microbiology and can influence the response of paddy soils to future temperature increases.

Predicting maintenance costs of an IT system using artificial intelligence models

Predictive maintenance is a maintenance policy where the goal is to detect potential future maintenance risks in a system so that the maintenance process can be optimised before system faults occur. This paper describes a deep learning model that does not require domain expertise. Deep learning approaches have several benefits over explicit statistical modelling: (1) they require far less domain-specific knowledge; (2) if the underlying data-generating mechanism of assets changes, a deep learning model would only need to be retrained to learn these new changes; (3) they can capture non-linear and complex multidimensional relationships; and (4) they may outperform rule-based or statistical methods. The paper describes how the model predicts maintenance-relevant events, along with the cost of the upcoming event and the time when it will happen. The paper describes the use of a long short-term memory architecture for our deep learning model. By doing so, the cost values represent a real, quantitative value of the potential maintenance costs in the future of an asset. Event, cost and time prediction are all achieved with high accuracy. This allows for the development of maintenance solutions without the initial high degree of domain process knowledge required. The artificial intelligence model can be used to raise an alarm when the cost values exceed some threshold, when the frequency of high-cost events increases significantly over the lifetime of an asset, or when the expected cost exceeds the cost of maintenance.