Maintenance Interventions Research Articles

The Gerber bridges in Italy: retracing history to safeguarding existing infrastructures

The static scheme of the Gerber beam was formalized in 1886 when the Bavarian engineer Heinrich Gerber (1832-1912) filed the patent “Balkentrager mit freigenden Stuntzpunkten,” introducing the concept of internal hinges within the continuous girder static model. By the 1890s, large metal viaducts constructed applying the Gerber scheme became widely adopted. Subsequently, from around 1930 onwards, the Gerber scheme was extended to reinforced concrete bridges. In Italy, the Gerber scheme was widely applied to reinforced concrete bridges from the 1930s to the 1970s. In this context, the Gerber scheme evolved with the technological advancements in reinforced concrete construction, adapting to the gradual integration of prefabrication techniques, prestressing methods, and the industrialization of castings. The present paper retraces the evolution of the reinforced concrete Gerber bridge in Italy, proving the knowledge base framework for the actual assessment and maintenance intervention on existing bridges.

Techno-Economic analysis of ceramic matrix composites integration in remaining useful Life Aircraft Engine Hot Section Components

AbstractCeramic Matrix Composites (CMCs), specifically SiC/SiC composites, represent a significant innovation in aerospace material technology, offering superior performance over traditional nickel-based superalloys in high-temperature turbine blade applications. This study presents a novel techno-economic assessment, filling a critical gap in the literature by directly comparing the economic and technical viability of CMCs versus superalloys. Unlike previous studies, which primarily focus on technical performance or cost analysis independently, this work integrates both aspects, providing a holistic comparison across key economic metrics, including acquisition, machining, maintenance, and recycling costs. The results demonstrate that SiC/SiC blades offer a 15–20% higher Net Present Value (NPV) and a 17% greater Internal Rate of Return (IRR) over a 20-year lifecycle than superalloys. Despite higher initial costs, CMCs achieve an estimated 2 to 3 years reduction in payback period, mainly due to their superior thermal and creep resistance, leading to fewer maintenance interventions and longer operational lifetimes. Although machining costs for CMCs are higher, these are more than offset by the long-term savings achieved through improved fuel efficiency and lower maintenance costs. A comprehensive sensitivity analysis, incorporating fluctuations in discount rates and material costs, further validates the economic robustness of CMCs in various operational scenarios. This study is the first to compare CMCs and superalloys, offering new insights into the financial implications of material selection in aerospace manufacturing. The findings present critical engineering recommendations that empower aerospace manufacturers and decision-makers to optimise material selection for improved efficiency and cost-effectiveness in high-performance turbine applications.

Effects of Transcranial Direct Current Stimulation Targeting Dorsolateral Prefrontal Cortex and Orbitofrontal Cortex on Somatic Symptoms in Patients With Major Depressive Disorder: A Randomized, Double-Blind, Controlled Clinical Trial.

There is a lack of research on transcranial direct current stimulation (tDCS) for the treatment of somatic symptoms in major depressive disorder (MDD) and the suitable stimulating brain region. We investigated the efficacy of tDCS targeting the dorsolateral prefrontal cortex (DLPFC) versus orbitofrontal cortex (OFC) on depressive somatic symptoms and somatic anxiety in patients with MDD and aimed to identify the appropriate stimulating brain regions. In this randomized, double-blind, sham-controlled study, a total of 70 patients diagnosed with MDD were randomly allocated into DLPFC group, OFC group, and Sham group. Subjects participated in 2 weeks of 10 primary interventions and subsequently 2-week maintenance interventions weekly (20 min, 2 mA). The DLPFC group showed a more significant improvement in somatic symptoms compared to the Sham group at week 2. At the maintenance and follow-up stages, the DLPFC group outperformed the Sham and OFC groups, but the difference with the Sham group was not significant. Neither active group demonstrated superiority over the Sham group in improving depression and anxiety. In conclusion, the tDCS targeting DLPFC may be a potentially effective therapeutic target for alleviating somatic symptoms in patients with MDD.

A Simheuristic Approach to Scheduling Sustainable and Reliable Maintenance for Bridge Infrastructure

Designing maintenance strategies for a vast portfolio of aging infrastructures requires decision-makers to ensure adequate safety levels while addressing the requirements on service interruptions, costs, and workforce availability. This study addresses the problem of scheduling maintenance interventions for a portfolio of bridges, aiming to minimize CO2 emissions while meeting minimum reliability requirements and adhering to workforce and budget constraints. To achieve this, we present a Simheuristic algorithm that combines a metaheuristic core based on the Adaptive Large Neighborhood Search metaheuristic with a Monte Carlo simulation module. This integration allows for the evaluation of optimized scheduling solutions, accounting for the inherent randomness in the structural deterioration process. The proposed approach is tested in a comparative analysis against traditional time-based and condition-based scheduling methods. Results from diverse bridge portfolios demonstrate that the proposed algorithm offers improved performance in terms of both total costs and CO2 emissions.

Evaluating the performance of water distribution network deterioration using customer-oriented performance indices

ABSTRACT Water distribution networks (WDNs) are an essential urban infrastructure, with their performance directly influencing societal well-being. Our study applied to a real network model that employs a 24-h simulation with 1-h time steps and evaluates the impact of leaks and loss in the pipe cross-sectional area on WDN hydraulic performance as experienced by end users. Adhering to a UK utility's 20-m head pressure requirement as the water main benchmark, we present two new customer-oriented performance indices (CPIs) centred on network reliability and pressure deficit severity. The new CPIs adeptly quantify network performance degradation due to pipe deterioration. This degradation translates directly to a poor customer experience, highlighting the potential for these CPIs to pinpoint areas of the network where performance levels are compromised. Furthermore, the CPIs identify individual pipes within the network where defects would severely impact network performance and the sets of pipes which, when simultaneously experiencing defects, would lead to a more severe loss in network performance. Results show that the CPIs capture relatively small performance declines and identify sensitive pipes impacting network performance, providing insights for optimised inspection and maintenance intervention to provide better customer service.

Proposal automatic water purging system for machinery in high humidity environments controlled by an ECU

This paper presents an advanced automatic water discharge system tailored for diesel-powered machinery, particularly for vehicles operating in diverse climates, including humid coastal regions. The system is designed to protect fuel circuits in tanks and diesel engines of boats, offering either automatic or semi-automatic protection against oxidation and corrosion. This enhancement extends the lifespan of diesel components and pipelines while simultaneously reducing maintenance costs and intervention times. Additionally, the system improves fuel purity, resulting in increased engine power and efficiency. The proposed solution was experimentally validated using an Arduino board coupled with a water-sensitive detector in diesel fuel, suitable for installation in diesel filters or tanks. Experimental findings demonstrated a remarkably swift separation time of 150 microseconds for diesel and water at a pressure of 1 bar, achieving an impressive efficiency of 99.84%. The system's rapid and effective separation capability highlights its practical value, particularly in scenarios where efficient water removal is critical to sustaining engine performance and longevity.

High-tech guardians: Robotics at the heart of the Future Circular Collider.

A holistic robotic concept for inspection, maintenance, and emergency interventions for the Future Circular Collider is presented.

Integrating Mixup and ArcFace for enhanced anomalous sound detection

In machine condition monitoring, accurate detection of anomalous sounds is essential for timely maintenance interventions. Traditional deep neural network models employ classification-based self-supervised learning to discriminate normal and abnormal operational sounds using classification confidence. However, these models often suffer from overfitting or excessive confidence, limiting their effectiveness. To overcome this, we introduce a hybrid technique that combines the strengths of Mixup and ArcFace techniques. Mixup enhances model generalization by training on mixed sound data, while ArcFace promotes greater inter-class distances with an angular margin loss. However, there is a challenge in combining these methods because the angular margin for ArcFace is defined for a single target class, and hence, can be hardly defined for the mixed data utilized in Mixup. The proposed technique employs the class-dependent angular margins based on the mixing ratio of data. SoftMax losses for individual classes are constructed using the class-dependent margins and then integrated into the Mixup loss. When applied to the DCASE2020 Task2 dataset, the proposed method significantly outperforms the state-of-the-art NoisyArcMix model in anomaly detection. This approach demonstrates the potential of leveraging class-dependent angular margins in mixed data for improved performance in machine condition monitoring.

Digital behaviour change intervention for weight loss maintenance in adults with obesity: a feasibility pilot study of eCHANGE

ABSTRACT Successful weight maintenance after weight loss is challenging. Digital interventions may facilitate behaviour change to prevent weight regain, however little is known about the acceptance and use of digital interventions in support of weight maintenance. This mixed methods study aims to evaluate user experiences, system use, and preliminary efficacy of eCHANGE, an application based self-management intervention for weight loss maintenance. A 3-month multi-site single group feasibility pilot study was conducted among adults (N = 60) with BMI ≥30 kg/m2, aiming to maintain weight after weight loss of ≥8%. User experiences and system use were examined through validated questionnaires, system use log data, and individual interviews (n = 15). Preliminary efficacy testing included body weight and patient reported outcome measures. Participants rated eCHANGE usability and usefulness as good in support of weight maintenance, with variation in usage. Analysis indicated that higher behavioural engagement scores were statistically significantly associated with frequency of technology usage. Weight loss was maintained by 83% of the participants at 3-months (i.e. defined as weight change of <3% of baseline body weight (kg)). Digital interventions, combining persuasive system design principles and behaviour change techniques, supporting self-regulation and maintenance of health behaviours, have the potential to facilitate weight maintenance after weight loss. Trial Registration: ClinicalTrials.gov NCT04537988 https://clinicaltrials.gov/ct2/show/NCT04537988

Contribution to Conditional Maintenance by Vibration Analysis of Rotating Machine Mechanical Failures and Proposed Solutions

Objective: The aim of this study is to analyze the Cement Separator machine and identify potential condition indicators through vibration measurements and analysis. The goal is to diagnose and repair the machine before any material damage occurs. Background: Maintenance Technology: Modern maintenance technologies are crucial for monitoring equipment performance and planning timely maintenance interventions. Vibration Monitoring: As a key component in maintenance programs, especially for mechanical systems, vibration monitoring provides early warnings of faults based on the actual state of the machine. Cement Separator Machine: Issue: The cement separator exhibits significant vibrations despite its low operating speed. Data Sources: The construction and operational details of the machine are available from its operating manual and vibration measurements. Methodology: Practical Vibration Measurements: Extensive measurements are performed in a research laboratory and on-site to monitor the machine's condition. Vibration measuring devices are used to monitor rotating machines at specified intervals according to their condition and international standards. Condition Indicators: Various condition indicators are monitored to detect possible deterioration and the onset of mechanical or electrical failures during the machine's operation. Experimental Studies: Practical experiments validate the theoretical and numerical findings and refine the vibration analysis approach. Strategy: Adequate scheduling of interventions and repairs based on the machine's condition. Continuous monitoring through vibration analysis devices. Expected Outcomes: Early detection of potential mechanical or electrical failures. Sustainable solutions to reduce vibrations and enhance the machine's performance. Improved maintenance scheduling to prevent material damage and extend the machine's lifespan. By integrating vibration monitoring and analysis with practical studies, this research aims to provide a comprehensive diagnosis and repair strategy for the cement separator machine.

Effects of degraded durability on the long-term stability of in-service slopes with reinforced concreted support structures

Engineering slope stability issues typically exhibit the impact of deteriorating durability on the susceptibility of slopes to failure. A thorough investigation was essential to explore theoretical and experimental aspects of slope durability degradation and its implications on long-term stability. Hence, a durability model was developed to accommodate slope stabilization using reinforced concrete (RC) support structures. This model was grounded in classical durability principles for RC structures. Subsequently, a model test was conducted to compare the responses of a standard slope model with a weakened counterpart subjected to environmental impacts. According to the proposed methodology for slope durability and stability, a case study involving future durability and stability predictions was performed. It was found that the theoretical solutions for the carbonation or neutralization (CN) velocity, depth, and penetration time agreed well with model test results. The slope surface displacements of the weakened slope with deteriorating coefficients between 0.6 and 0.9 were 4 to 8 times those of the standard slope, demonstrating significant degradation in stability. The case study indicated a steady reduction in the safety factor, at a rate of 2.3 to 2.4‰ per year throughout the slope’s service life. Finite-element-based predictions also suggested the potential for corrosion of slope anchor bolts within 20 years and breakage within 30 years, at an average rate of 7.5‰ per year in the ultimate bearing capacity. These findings highlight the need for timely maintenance and reinforcement interventions to ensure the long-term durability of operational slopes.

A UAV Based Concrete Crack Detection and Segmentation Using 2-Stage Convolutional Network with Transfer Learning

This study explores a non-destructive testing (NDT) method for crack detection using a two-stage convolutional neural network (CNN) model, incorporating a combination of AlexNet and YOLO models through transfer learning. Crack detection is pivotal for assessing structural integrity and ensuring timely maintenance interventions. The developed model was rigorously tested in simulated environments and through physical experimentations with the use of a UAV to evaluate its effectiveness. A 2-stage model, based on AlexNet and YOLO, was developed for crack classification and segmentation. The developed model leveraged transfer learning to address limitations from traditional CNN models. A known dataset was used to evaluate the developed model, benchmarking it against other models. The classification network achieved an accuracy rate exceeding 90%, while the segmentation network successfully identified and delineated cracks in 85.71% of the images. Finally, the developed model was deployed using a UAV to perform crack detection and segmentation in a controlled environment. These results underscore the model's proficiency in both detecting and segmenting structural cracks, highlighting its potential as a reliable tool for enhancing the maintenance and safety of architectural structures. Doi: 10.28991/HIJ-2024-05-03-010 Full Text: PDF

An Assessment of the Impact of Locally Recycled Cementitious Replacement Materials on the Strength of the Ultra-High-Performance Concrete

Withstanding extreme events is increasingly a significant challenge for the construction industry. Where civil infrastructures remain using traditional concrete, which has low tensile strength, poor durability, and weak crack resistance, in this regard, ultra-high-performance concrete (UHPC), with its outstanding mechanical properties and high strength, offers the prospect of wide application. This advanced technology allows for the fabrication of thin and light-dimensional structures to accelerate construction while increasing corrosion resistance to minimize maintenance intervention and extend the service life of the infrastructures. Despite this, UHPC is less eco-friendly due to consuming more cement than the usual material, which requires replacement materials, such as silica fume (SF) and rice husk ash (RHA), which are readily available from other local material production. This study proposes an experimental approach to assess the influence of SF and RHA content on the properties of UHPC. Different SF and RHA compositions will be adjusted to analyze their effects on slump flow, compressive strength, flexural strength, tensile strength, and the stress–strain relationship in UHPC tension testing. Based on the results, the most effective ratio is RHA replacing 50% of the SF in the UHPC mixture. Specialized tensile experiments reveal enhanced tensile strength with judicious RHA incorporation at 5-day and 28-day stages, particularly in initial crack and damage conditions. Stress–strain curves for 5% to 15% RHA samples show increased ductility, indicating that optimal RHA-SF ratios enhance UHPC cracking characteristics. Based on the results, a discussion on the appropriate proportions for utilizing most local materials will be derived, especially for regions of Vietnam. It is evaluated as a feasible and promising solution to reduce greenhouse gas emissions threatening global climate change.

Maintenance optimization for multi-component systems with a single sensor

We consider a multi-component system in which a single sensor monitors a condition parameter. Monitoring gives the decision maker partial information about the system state, but it does not reveal the exact state of the components. Each component follows a discrete degradation process, possibly correlated with the degradation of other components. The decision maker infers a belief about each component’s exact state from the current condition signal and the past data, and uses that to decide when to intervene for maintenance. A maintenance intervention consists of a complete and perfect inspection, and may be followed by component replacements. We model this problem as a partially observable Markov decision process. For a suitable stochastic order, we show that the optimal policy partitions in at most three regions on stochastically ordered line segments. Furthermore, we show that in some instances, the optimal policy can be partitioned into two regions on line segments. In two examples, we visualize the optimal policy. To solve the examples, we modify the incremental pruning algorithm, an exact solution algorithm for partially observable Markov decision processes. Our modification has the potential to also speed up the solution of other problems formulated as partially observable Markov decision processes.

Smart structural health monitoring using computer vision and edge computing

Structural health monitoring (SHM) provides real-time data on the condition and performance of infrastructure, enabling timely and cost-effective maintenance interventions, and hence enhanced safety and extended service life. The computer vision-based non-contact sensor has emerged as a promising alternative to conventional contact-type sensors for structural displacement measurement and SHM. Many of the currently reported vision-based structural displacement measurement systems typically temporarily set up a video camera from a distance to the structure. The collected images or videos are usually stored locally and post-processed offline to obtain structural displacement responses, which is cumbersome and limited to short-term SHM applications. The recent development of technologies empowered by the Internet of Things (IoT) and edge computing has enabled real-time video processing and analysis at the source, minimizing latency, reducing bandwidth requirements, and enabling prompt decision-making, thereby enhancing efficiency and responsiveness compared to traditional offline video recording and processing systems. In this paper, an edge computing vision-based displacement measurement system (EdgeCVDMS) is developed. Video recording, processing, and displacement response identification are entirely performed on an edge device integrated with the vision-based displacement tracking algorithm, thereby greatly reducing the amount of data transmitted to the cloud server. The feasibility and applicability of the developed sensing system are experimentally validated on a laboratory-scaled transmission tower structure. The proposed EdgeCVDMS is cost-effective, easily deployable, and of great potential to be applied for the condition assessment of a larger population of aging civil infrastructure.

Effects of Weight Loss and Weight Maintenance on Lipoprotein Insulin Resistance Scores in Adults with Overweight and Obesity.

Background: An elevated lipoprotein insulin resistance (LP-IR) score corresponds to insulin resistance in adults with overweight and obesity, yet data are lacking regarding the impact of exercise interventions on LP-IR. The purpose of this secondary analysis was to evaluate the effects of a weight loss and weight maintenance intervention on LP-IR score in adults with overweight and obesity. Methods: Thirty sedentary adults with overweight and obesity completed a 10-week OPTIFAST® weight loss program with supervised aerobic exercise to achieve clinical weight loss (CWL) (≥7% from baseline). Aerobic exercise volume increased weekly until 700 MET min/week was reached. Participants who reached CWL were randomized to groups at volumes at either physical activity (PA-REC) or weight maintenance (WM-REC) recommendations (weeks 11-28). Plasma blood samples were analyzed via nuclear magnetic resonance spectroscopy at baseline, after weight loss (week 10), and following weight maintenance (week 28). Results: Following the weight loss phase, on average, participants significantly (p < 0.001) reduced LP-IR score (-12.1 ± 13.5), body weight (-8.9 ± 2.7%), and waist circumference (-7.7 ± 4.1 cm). During the weight maintenance phase, there were no changes in LP-IR score between exercise groups (PA-REC: 4.1 ± 13.6; WM-REC: -2.0 ± 11.2; P = 0.7). The PA-REC group had improvements in LP-IR score from baseline (49.8 ± 24.6 to 36.6 ± 27.6, P < 0.001), yet there were no within-group changes during the weight maintenance phase (P > 0.05). Conclusion: LP-IR score improved during weight loss in adults with overweight and obesity and were sustained during the weight maintenance phase in the PA-REC group. Aerobic exercise at least at minimum guidelines following CWL can preserve LP-IR score improvements and may indicate a reduced T2DM risk in adults with overweight and obesity.

Digital twins-boosted intelligent maintenance of ageing bridge hangers exposed to coupled corrosion–fatigue deterioration

The corrosion–fatigue coupled deterioration of ageing steel bridge hangers presents significant structural challenges, demanding rigorous condition assessments and timely maintenance interventions. This paper introduces a framework of digital twins-boosted intelligent maintenance (DTIM) tailored for ageing hangers, integrating the prediction model, monitoring data and inspection results. The DTIM features a suite of algorithms adaptation and innovations, including dynamic Partially Observable Markov Decision Processes (POMDP), Asynchronous Advantage Actor Critic (A3C), and Bayesian Dynamic Linear Models (BDLM). The DTIM emphasises regular early-life repairs, strategic inspections, and timely replacements towards life-end, tailored to the condition of specific bridge hangers in the case study presented. By coordinating actions across hangers, the DTIM enables opportunistic maintenance to further optimise resource allocation. The output highlights digital twins in exploring the add-on value of monitoring and inspection for the proactive and sustainable maintenance of ageing infrastructure, promising enhanced structural integrity and serviceability in a cost-effective and well-informed manner.

Machine Learning Framework for Real-Time Pipeline Anomaly Detection and Maintenance Needs Forecast Using Random Forest and Prophet Model

This paper introduces an Intelligent Model for Real-Time Pipeline Monitoring and Maintenance Prediction to enhance infrastructure integrity and operational efficiency in Nigeria&amp;apos;s oil and gas sector. Given the country&amp;apos;s economic dependence on oil and gas revenue, efficient pipeline transportation is crucial. However, pipelines face challenges such as corrosion, mechanical failures, vandalism, and theft, leading to economic losses and environmental risks. Current monitoring systems are mainly reactive, lacking timely anomaly detection and predictive maintenance capabilities. To tackle these challenges, the study utilized sophisticated machine learning methods by combining the Random Forest classifier for real-time anomaly detection with the Prophet model for predictive maintenance forecasting. Datasets from Kaggle were used. The Random Forest classifier demonstrated robust performance with an accuracy of 93.48%, precision of 93.75%, recall of 96.77%, and an F1-score of 95.24%. The Prophet model provided accurate hourly forecasts of operational parameters, aiding proactive maintenance scheduling. Despite some errors encountered (RMSE: 21.48 and MAE: 18.17), the Mean Absolute Percentage Error (MAPE) of 14.87% indicates relatively minor discrepancies compared to actual values. In conclusion, the Intelligent Model shows significant advancements in pipeline monitoring and maintenance prediction by leveraging machine learning for early anomaly detection and timely maintenance interventions. This proactive approach aims to reduce downtime, prevent environmental damage, and optimize operational efficiency in Nigeria&amp;apos;s oil and gas infrastructure. Future research could focus on enhancing system scalability across diverse terrains, employing advanced deep learning techniques such as Transformer Networks and Autoencoders for improved prediction accuracy, and exploring cybersecurity measures like blockchain integration to ensure data integrity and protect critical infrastructure from cyber threats.

Maintainability Assessment during the Design Phase: Integrating MTA and UNE 151001

The focus on maintenance actions in the early design phases has been a trend in recent years. The main sources of information during the design of the maintainability process include operator reports, maintainer experience, failure history, and manufacturer recommendations. During this process, an important aspect is related to the configuration of maintenance tasks and interventions, such as their main phases, activities, and durations. The allocation or estimation of maintainability involves identifying and/or estimating the mean time to repair (MTTR) for each component or system. The time of the maintenance tasks or the repair time are fundamental for companies, as the availability of equipment directly depends on this parameter. In this study, a new method for evaluating maintainability during the design phases is proposed. The method is based on the integration of the maintenance task analysis (MTA) principles and the UNE 151001 maintainability evaluation standard. A data structure is proposed that serves the application of the UNE151001 procedures, obtaining a data-based maintainability evaluation. As a validation procedure, an application of the proposed approach is presented using two overhead cranes. Comparisons and recommendations are made regarding the maintainability of both pieces of equipment. Finally, some managerial and engineering insights are presented.

A System-Level Reliability Growth Model for Efficient Inspection Planning of Offshore Wind Farms

Fatigue damage can lead to failures of structural systems. To reduce the failure risk and enhance the reliability of structural systems, inspection and maintenance interventions are required, and it is important to develop an efficient inspection strategy. This study, for the first time, develops a system-level reliability growth model to establish efficient inspection planning. System-level reliability growth is defined as an increase in the percentage of the system reliability index with and without inspection. The probabilistic S-N approach is used to obtain the reliability index without inspection. Moreover, advanced risk analysis and Bayesian inference techniques are used to obtain the reliability index with inspection. The optimal inspection planning is obtained by maximizing system-level reliability growth. This model is applied to an offshore wind farm. The results show that inspection efficiency can be improved by increasing the number of repair objects in response to a ‘detection’ inspection outcome, changing the inspection object for each inspection, and increasing the inspection quality. The maximum system-level reliability growth gained from one additional inspection decreases as the number of inspections increases. This study quantifies the inspection efficiency of offshore wind farms by explicit system-level reliability growth computation, offering valuable insights for promoting sustainable energy solutions.