Effective Maintenance Research Articles

Upadacitinib in active non-radiographic axial spondyloarthritis: 2-year data from the phase 3 SELECT-AXIS 2 study

Abstract Background In SELECT-AXIS 2, upadacitinib improved the signs and symptoms of active non-radiographic axial spondyloarthritis (nr-axSpA) through 52 weeks versus placebo and was well tolerated. Here, we evaluated the efficacy and safety of upadacitinib through 2 years. Methods The study enrolled eligible adult patients with a clinical diagnosis of nr-axSpA who met the 2009 Assessment of SpondyloArthritis international Society (ASAS) classification criteria and had objective signs of active inflammation on magnetic resonance imaging (MRI) of sacroiliac joints and/or high-sensitivity C-reactive protein. Patients were randomized 1:1 to receive double-blinded treatment with upadacitinib 15 mg once daily (QD) or placebo for 52 weeks, after which all patients received open-label treatment with upadacitinib 15 mg QD. Efficacy results over 104 weeks were reported as observed (AO) and either AO with non-responder imputation (AO-NRI; binary endpoints) or AO with mixed-effect model for repeated measures (continuous endpoints). Treatment-emergent adverse events (TEAEs) were summarized through week 104. Results Of 313 patients randomized and treated, 224 (continuous upadacitinib n = 117; placebo/upadacitinib n = 107) completed 104 weeks of treatment. In patients who received continuous upadacitinib, sustained improvement was observed through 2 years of treatment across efficacy endpoints including disease activity, pain, function, enthesitis, quality of life, and MRI measures of inflammation. At week 104, 57.1%, 59.0%, and 31.4% of patients achieved ASAS40 response, and low disease activity and inactive disease (as defined by Axial Spondyloarthritis Disease Activity Score), respectively (AO-NRI); week 104 outcomes were generally similar in patients who initially received placebo and were switched to upadacitinib at week 52. In total, 286 patients were exposed to ≥ 1 dose of upadacitinib, comprising 378.3 patient-years (PY) of exposure. Upadacitinib was generally well tolerated, with exposure-adjusted event rates (EAERs) for TEAEs, serious adverse events (AEs), and AEs leading to study drug discontinuation of 207.5, 8.7, and 5.3 events/100 PY, respectively. EAERs of TEAEs of special interest were broadly consistent with those reported through week 52. Conclusions Treatment with upadacitinib demonstrated consistent improvement and maintenance of treatment effect across efficacy endpoints through 2 years; no new safety signals were identified with additional exposure. Trial registration NCT04169373.

Introduction to Predictive Maintenance Application using Machine Learning: The Case of the Injection System of a Diesel Engine

Diesel engines are crucially important in various fields, particularly in the automotive sector, as they ensure a reliable supply of mechanical energy. However, injection system failures, which are among the most recurrent failures, can lead to performance deterioration and increased pollutant emissions and maintenance costs. Therefore, adopting an effective maintenance strategy to analyze and predict such failures would significantly improve the efficiency of these engines. Based on collected data from engines by reliable sensors, the application of predictive maintenance coupled with a machine learning model allows effective prediction of failures for optimal appropriate maintenance. This study presents an approach to diagnosing the injection system of automotive diesel engines using a test bench based on data from temperature sensors installed on engine cylinders. These temperature data exhibit unusual variations in the event of an injection system failure. The Random Forest (RF) algorithm was employed to analyze these data and establish a clear relationship between cylinder temperatures and failure. The proposed model can detect failures associated with the injection system. Performance evaluation, particularly after parameter tuning, underscores the model's efficacy, achieving an accuracy exceeding 97%.

A deep transfer network based on dual-task learning for predicting the remaining useful life of rolling bearings

Abstract Rolling bearings are essential in rotating machinery, and accurate remaining useful life (RUL) predictions are necessary for effective maintenance and optimal performance. Poor trend of health indicators (HI) and operating condition variations can reduce the reliability and accuracy of RUL predictions.To address these challenges, we propose a deep transfer network based on dual-task learning for predicting the remaining life of rolling bearings (DTLDL-RUL). This framework integrates health status assessment and RUL prediction, leveraging task commonalities and differences to create a robust model, then transfers it to improve generalization in the target domain. Specifically, we first mine spatiotemporal features from vibration signals to generate and classify HI, labeling them for subsequent tasks. Next, we use the shared feature extractors and private residual networks to capture common and specific features of each task, merge them with the multi-gate control networks, and adaptively adjust task weights in the loss function to enhance model adaptability. Finally, the trained model is transferred to the target domain using domain adaptation to extract domain invariant features and consider target-specific features, enhancing generalization. Experiments conducted on the 2012 PHM and XJTU-SY datasets demonstrate that the proposed method achieves high accuracy and generalization in RUL predictions.

Wind Turbine Blade Fault Detection Method Based on TROA-SVM

Wind turbines are predominantly situated in remote, high-altitude regions, where they face a myriad of harsh environmental conditions. Factors such as high humidity, strong gusts, lightning strikes, and heavy snowfall significantly increase the vulnerability of turbine blades to fatigue damage. This susceptibility poses serious risks to the normal operation and longevity of the turbines, necessitating effective monitoring and maintenance strategies. In response to these challenges, this paper proposes a novel fault detection method specifically designed for analyzing wind turbine blade noise signals. This method integrates the Tyrannosaurus Optimization Algorithm (TROA) with a support vector machine (SVM), aiming to enhance the accuracy and reliability of fault detection. The process begins with the careful preprocessing of raw noise signals collected from wind turbines during actual operational conditions. The method extracts vital features from three key perspectives: the time domain, frequency domain, and cepstral domain. By constructing a comprehensive feature matrix that encapsulates multi-dimensional characteristics, the approach ensures that all relevant information is captured. Rigorous analysis and feature selection are subsequently conducted to eliminate redundant data, thereby focusing on retaining the most significant features for classification. A TROA-SVM classification model is then developed to effectively identify the faults of the turbine blades. The performance of this method is validated through extensive experiments, which indicate that the recognition accuracy rate is 98.7%. This accuracy is higher than that of the traditional methods, such as SVM, K-Nearest Neighbors (KNN), and random forest, demonstrating the proposed method’s superiority and effectiveness.

A hybrid artificial neural network–genetic algorithm back-analysis technique for local anomaly detection in railway track substructure

The UK’s ageing railway transportation network is increasingly at risk of substructure failure, often caused by malfunctioning buried drainage systems. These drainage issues lead to localised soil weaknesses in the substructure layers, which, if undetected, can result in costly maintenance interventions or, worse, catastrophic system failure. Regular non-destructive test (NDT) assessments are essential for monitoring the condition of the substructure, yet current interpretation techniques for NDT data provide limited insight into the size, location and even the presence of weakened zones. This results in an incomplete understanding of the substructure's condition, impeding effective maintenance planning. A novel hybrid back-analysis technique to detect weakened zones in railway substructures caused by drainage malfunctions is proposed, addressing a critical gap in existing solutions. The method employs an artificial neural network surrogate model, trained on virtual experimental data generated through finite-element simulations, and couples it with a genetic algorithm to optimise the match between modelled and measured deflections. This novel method is computationally efficient, independent of seed modulus values and thoroughly validated for accuracy. It delivers a precise understanding of soil weaknesses in railway substructures, transforming maintenance strategies by improving safety, reducing costs and promoting infrastructure sustainability.

P1242 Incidence of Acute Severe Ulcerative Colitis in a Changing Landscape of Increased Disease Prevalence and More Effective Therapies

Abstract Background Over the past 60 years, there has been a growing worldwide health burden of Ulcerative Colitis (UC), with increasing disease prevalence and incidence.1,2 With the rise in UC patient population, more effective therapeutic options to induce remission have become available (biologic therapies and small molecule inhibitors). Approximately 15-25% of patients with UC will experience at least one episode of Acute Severe Ulcerative Colitis (ASUC). First diagnosis of UC accounts for 10-20% of ASUC presentations, and 34% of patients will experience ASUC within a year of UC diagnosis.3 We aim to ascertain the effect of increased therapeutic options and increased UC prevalence on the incidence of ASUC hospital admissions. We hypothesised that with increased availability of more effective UC maintenance therapy through the Australian Pharmaceutical Benefits Scheme (PBS), there would be a reduction in ASUC presentations in patients with existing UC diagnosis, including within first year of UC diagnosis. We further hypothesised that with the increase in UC incidence, there would be an increase in ASUC presentations in first diagnosis of UC. Methods Data was collected retrospectively on patients admitted with ASUC to a quaternary Sydney hospital between January 2014 and December 2023.1,2 A modified Truelove and Witts criteria was used to identify ASUC presentations.4 Patients admitted with ASUC were classified as "relapse" if they were on maintenance UC therapy at time of presentation, "refractory" if presented during treatment of a UC flare, and "new diagnosis" if first presentation and no previous history of UC. Results Over the 10-year period, there were a total of 167 admissions of ASUC among a total of 122 patients. On average, there were 13.9 ± 2.5 patients with 16.8 ± 3.5 presentations each year. The highest number of ASUC presentations were in 2022 and 2018. The average number of refractory and relapse presentations were not significantly different (6.8 vs 7.1, p = 0.8). Over 38.4% of ASUC presentations were within the first year of diagnosis. The number of ASUC episodes and number of patients presenting with ASUC did not appear to increase over the past decade (Table 1, Figure 1). Conclusion Despite an increasing worldwide incidence and prevalence of UC, we did not find an increasing incidence of ASUC admissions. The increased availability of more effective options for treatment of UC may explain the stable number of ASUC presentations, reflecting better disease control in the outpatient setting. Interestingly, despite increasing UC incidence, the incidence of ASUC presentation in first diagnosis UC did not appear to increase over time.

P0641 Efficacy of Vedolizumab and Ustekinumab Following Cyclosporine Induction in Acute Severe Ulcerative Colitis: A Retrospective Cohort Study

Abstract Background In corticosteroid-resistant acute severe ulcerative colitis (ASUC), second-line treatment involves infliximab or cyclosporine (CYCLO). With increasing antiTNFα exposed patients, CYCLO is often chosen to prevent emergency colectomy but is limited to the induction phase due to tolerability, serving as a bridge until effective maintenance therapy. In this setting, limited data on vedolizumab (VDZ) and ustekinumab (UST) as maintenance make the choice of an optimal bridge strategy challenging. This study aimed to compare the efficacy of VDZ and UST after CYCLO induction in corticosteroid-resistant ASUC. Methods This single-center retrospective cohort included all patients hospitalized for ASUC, responding to CYCLO induction, and starting VDZ or UST maintenance. ASUC was defined by modified Truelove and Witts criteria, with response to CYCLO defined by a Lichtiger score reduction to <10 points for 2 consecutive days, with a minimum 3-point decrease by day 7. The primary outcome was colectomy-free survival; secondary outcomes were treatment persistence and safety. Results From 2014-2024, 72 ASUC patients received CYCLO. Fourteen were excluded (non-response, tofacitinib [n=6], infliximab [n=3], and/or emergency colectomy [n=11]), leaving 58 CYCLO-responsive ASUC patients (median age 39, IQR 30-58, 60% female), with 46 on VDZ (79%) and 12 on UST (21%). ASUC was related to ulcerative colitis in 81% (68% pancolitis) and Crohn’s in 19%. Disease duration was 7 years (IQR 2-15), with 91% prior biologic exposure, primarily antiTNF. Presentation included Lichtiger score 10 (IQR 10-12), CRP 40 mg/L (22-79), albumin 29 g/L (27-30), fecal calprotectin 874 µg/g (450-1800), UCEIS 7 (5-8), and Mayo endoscopic score 3 (3-3). Colectomy-free survival was 75% at M12 and 69% at M24 with VDZ and 57% at M12 and M24 with UST (p=0.38, figure 1). Treatment persistence was 48% at M12 and 37% at M24 with VDZ, and 42% at M12 and M24 with UST (p=0.51, figure 2). After adjusting for maintenance therapy, disease type, and severity, colectomy risk factors included albumin <27 g/L (HR=3.4; 95%CI:1.2-9.8) and prior tofacitinib exposure (HR=4.3; 95%CI:1.2-15.8). Over a median 37-month follow-up (95%CI:35-40), 34% of patients experienced adverse events, with 19% severe (mainly CYCLO-related), including 2 deep vein thromboses in the VDZ group and 1 infection in the UST group. No deaths occurred. Conclusion In this large cohort of biologic-exposed, corticosteroid-resistant ASUC patients, a CYCLO bridge strategy with VDZ or UST avoided colectomy in two-thirds of patients, with almost 50% treatment persistence at 12 months. There was no significant difference in efficacy, persistence, or safety between strategies.

Remaining Service Life of Concrete Pavements

A proactive approach to protecting or maximizing the investment of concrete pavements requires periodic assessment of the pavement condition to determine deficiencies and predict deterioration patterns. Pavement management strategies must be employed at an optimal time in order to attain the most cost effective maintenance and rehabilitation solutions for the structure. Determining the remaining pavement service life is critical for making better decisions about preservation treatments, rehabilitation, or reconstruction. This paper discusses three key approaches developed for determining concrete pavement remaining service life (RSL) and presents case studies demonstrating the use of each method. The three methods include an empirical based procedure, a new approach based on survival curves, and a comprehensive mechanistic based procedure using the new National Cooperative Highway Research Program (NCHRP) Project 1-37A analysis methodology.

EFEKTIVITAS PEMELIHARAAN ASET DI KOMPLEKS SEKRETARIAT UKM BARAT DAN TIMUR

This study aims to evaluate the effectiveness of fixed asset maintenance, particularly buildings, in the UKM West and East Secretariat Complex at Universitas Padjadjaran Jatinangor. The evaluation was conducted using three indicators of effectiveness: timeliness, appropriateness, and efficiency. The findings indicate that asset maintenance has been carried out on schedule, met user needs, and utilized the budget efficiently. However, challenges such as limited funding and insufficient user participation in maintaining the facilities remain. To address these issues, this study recommends the establishment of standard operating procedures (SOPs) for damage reporting, improved coordination among stakeholders, and optimized budget allocation. These improvements are expected to enhance the sustainability and efficiency of asset management. The results of this study serve as a reference for better and more effective asset maintenance practices in the future.

Digitalization of the Workflow for Drone-Assisted Inspection and Automated Assessment of Industrial Buildings for Effective Maintenance Management

Industrial buildings are a key element in the industrial fabric, and their maintenance is essential to ensure their proper functioning and avoid disruptions and costly economic losses. Continuous maintenance based on an accurate diagnosis makes it possible to meet the challenges of aging infrastructures, which demands a reliable data-based assessment for maintenance management implementing corrective and preventive actions, according to the damage criticality. This paper researches an innovative digitalized process for the inspection and diagnosis of industrial buildings, which leads to categorizing and prioritizing maintenance actions in an objective and cost-effective way from the inspection data. The process integrates some technical developments carried out in this work, aimed to automate the workflow: the drone-based inspection, the building condition assessment from the definition of a standardized construction pathology library, and a visual analysis of pathology evolution based on photogrammetry. The use of drones for digitalized inspection involves some challenges related to the positioning of the drone for damage localization, which has been herein overcome by developing a geo-annotation system for image acquisition. This system has also enabled the capture of geo-located images intended to generate 3D photogrammetric models for quantifying the pathological process evolution. Moreover, the assessment procedure outlined through multi-criteria decision-making methodology MIVES establishes a single criterion to automatically weight the relative importance of the damage defined in the library. As a result, this procedure yields the so-called Intervention Urgency Index (IUI), which allows prioritizing the maintenance actions associated with the damage while also considering economic criteria. In such a way, the overall process aims to increase reliability and consistency in the results of inspection and diagnosis needed for the effective maintenance management of industrial buildings.

Comparative Efficacy of Lebrikizumab, Dupilumab, and Tralokinumab in Maintaining Treatment Response in Atopic Dermatitis at Varying Treatment Continuance Rates

Introduction: Atopic dermatitis (AD) is a chronic inflammatory skin disease with significant impacts on quality of life. Despite the effectiveness of biologics for moderate to severe AD, long-term treatment adherence can be challenging in clinical practice. This study introduces the “durability index” as a novel estimate of the maintenance of therapeutic effect under varying treatment continuance rates. Methods: A population-adjusted indirect comparison of placebo-controlled phase 3 trials of dupilumab 300 mg QW/Q2W (SOLO 1, SOLO 2, and SOLO CONTINUE), tralokinumab 300 mg Q2W (ECZTRA1 and ECZTRA 2), and lebrikizumab 250 mg Q4W (ADvocate1 and ADvocate2) was conducted. In these trials, patients who responded at week 16 (IGA 0/1 or EASI 75) were re-randomized to either continue treatment or switch to treatment withdrawal (placebo) until week 52. The durability index was developed to estimate drug performance at varying treatment continuance rates between 100% to 0% on-treatment. Unanchored simulated treatment comparison (STC) was used to estimate odds ratios (OR) adjusting for baseline covariates. Results: For IGA 0/1, patients receiving lebrikizumab had statistically significantly better odds of maintaining response at week 52 than those on dupilumab for all treatment continuance rates, ranging from 1.730 (p = 0.044) at 100% to 4.690 (p < 0.001) at 0%. ORs comparing lebrikizumab to tralokinumab ranged from 1.787 at 100% continuance to 1.516 at 0%, with significant results favoring continuance rates between 39.5% and 96.9%. For EASI 75, week-52 durability at 100% continuance was non-significantly different between lebrikizumab and dupilumab (OR 0.687, p = 0.183); however, ORs increased in favor of lebrikizumab at lower treatment continuance rates, reaching significance from 64.2% (OR 1.454, p = 0.05) to 0% (OR 3.235, p < 0.001) continuance. ORs comparing lebrikizumab to tralokinumab ranged from 2.132 at 100% to 3.891 at 0% continuance, with significant results favoring lebrikizumab for all treatment continuance rates. ORs comparing dupilumab and tralokinumab showed varying results: tralokinumab was favored for IGA 0/1 at lower continuance rates, while dupilumab was favored for EASI 75 at higher continuance rates. Conclusions: In this indirect comparative analysis, lebrikizumab had treatment response rates that were comparable to or better than dupilumab and tralokinumab, regardless of treatment continuance rates. This finding suggests that lebrikizumab may offer a more effective long-term management option for AD. This analysis was funded by Eli Lilly and Company.

Continuous monitoring of partial discharge activities in power cables and their stimulation due to the temperature rise

AbstractWith the development of the power distribution systems demands, the grid reliability becomes a strategic issue. Continuous monitoring of power transmission networks is a key component in addressing this issue. Partial discharge (PD) characterisation instruments provide a reliable solution and an important indicator of the state of cable insulation. An increase in the PD level usually indicates the development of a fault that affects the integrity of the cable and can lead to various problems related to the quality and quantity of the transmitted energy. In this context, the use of Internet of Things (IoT) systems for power networks monitoring can provide a significant improvement to the localisation and detection of faults. In this paper, the authors show the advantages of continuous monitoring of power grid cables using a new IoT based framework using an advanced signal processing technique, namely the phase diagram. Also, the authors show that variations in temperature can impact the initiation and progression of PD, affecting the reliability and lifespan of electrical insulation systems. Understanding this temperature dependence is crucial for accurate PD detection and effective maintenance of power cables. Our results show that higher temperatures can accelerate the PD activity, while lower temperatures may suppress it.

A dual decomposition integration and error correction model for carbon price prediction.

Accurately predicting carbon prices is crucial for effective government decision-making and maintenance the stable operation of carbon markets. However, the instability and nonlinearity of carbon prices, driven by the complex interaction between economic, environmental, and political factors, often result in inaccurate predictions. To confront this challenge, this paper proposed a carbon price prediction model that integrates dual decomposition integration and error correction. Firstly, the variational mode decomposition optimized by the sparrow search algorithm (SVMD) is used to decompose carbon price series into intrinsic mode functions (IMFs). Secondly, a classification-prediction module is constructed to classify IMFs on complexity using fuzzy entropy. The long short-term memory networks optimized by the whale optimization algorithm (WLSTM) is employed to capture temporal dynamics and long-term dependencies within data. Conversely, lower complexity IMFs characterized by smoother trends and less erratic behavior are predicted using computationally efficient extreme learning machines (ELM). To further refine the prediction accuracy, ensemble empirical mode decomposition (EEMD) is introduced to decompose the initially predicted error series into IMFs and then predicted by classification-prediction module. Reconstruct the initial prediction IMFs and the error prediction IMFs to obtain the final prediction results. Finally, the proposed model was validated using real carbon price data from three Chinese carbon exchanges. Compared with the 15 comparison models, the performance indicators RMSE, MAE, MAPE, and R2 of the proposed model have promoted at least 19.89%, 25.11%, 25.01%, and 0.79% on average. These results underscore the effectiveness and superiority in predicting carbon prices, providing a robust tool for carbon market stakeholders and climate change policymakers.

Enhanced bearing RUL prediction based on dynamic temporal attention and mixed MLP

Bearings are critical components in machinery, and accurately predicting their remaining useful life (RUL) is essential for effective predictive maintenance. Traditional RUL prediction methods often rely on manual feature extraction and expert knowledge, which face specific challenges such as handling non-stationary data and avoiding overfitting due to the inclusion of numerous irrelevant features. This paper presents an approach that leverages Continuous Wavelet Transform (CWT) for feature extraction, a Channel-Temporal Mixed MLP (CT-MLP) layer for capturing intricate dependencies, and a dynamic attention mechanism to adjust its focus based on the temporal importance of features within the time series. The dynamic attention mechanism integrates multi-head attention with innovative enhancements, making it particularly effective for datasets exhibiting non-stationary behaviour. An experimental study using the XJTU-SY rolling bearings dataset and the PRONOSTIA bearing dataset revealed that the proposed deep learning algorithm significantly outperforms other state-of-the-art algorithms in terms of RMSE and MAE, demonstrating its robustness and accuracy.

A NON-SELECTIVE METHOD OF FORMING A DATA BANK ON THE TECHNICAL CONDITION OF THE INTERNAL COMBUSTION ENGINE FUEL SYSTEM OF TECHNOLOGICAL MACHINES IN REAL TIME

BACKGROUND: The economic component of the effective functioning of technological machines consists in their uninterrupted operation when performing various tasks in such industries as construction, road, agriculture, etc. It is currently possible to take into account the variety of operating conditions of technological machines and constantly changing loading modes by using digital technologies that allow creating an array of databases not only in real time, but also for an individual machine. An urgent task is to create methods for collecting information about the operating modes of the machine, the factors causing changes in the efficiency of operation, the development of algorithms for making decisions about maintaining the working condition of all units and systems of the machine, preventing their failures and non-production downtime of the machine as a whole. AIMS: to ensure the effective functioning of a single technological machine by managing the risks of failures, by adjusting the frequency of maintenance and periods of repair and restoration effects based on real-time data on the technical condition and digital processing of decision-making information. MATERIALS AND METHODS: the nature of changes in the technical condition of aggregates and systems of technological machines in the theory of systems is most often considered as random due to the high probability of uncertainty of factorial influence. It is proposed to consider the problem of failure risk management of system elements and aggregates of technological machines using the basic provisions of the theory of emissions of random processes. The object of the study is a diesel engine of a technological machine with an example of monitoring the technical condition of the fuel system. RESULTS: The substantiation of the expediency of performing maintenance and repair and restoration effects of technological machines according to need is presented. The adjustment of the maintenance frequency was carried out based on the results of these changes in the performance of the machine, in particular the internal combustion engine fuel system. To develop an algorithm for the formation of a data array, typical architectures for collecting and processing information using digital platforms for decision-making on the intensity of parameter changes are used, as an example, oscillograms of pressure changes during the operation of the internal combustion engine power system are presented. CONCLUSIONS: A module for diagnosing the technical condition and efficiency of the internal combustion engine of a single technological machine in real time has been developed. It is proposed to introduce an intelligent decision-making system with subsequent transformation into the image of a digital double of failure risk management and control of the efficiency of the machine for various operating conditions.

IoT-Enabled monitoring system for Plant Health Growth

In current scenario, plant health monitoring plays a crucial role in effective health maintenance of plants in climate changes. Internet of Things (IoT) played an efficient role in realizing the remote and real-time monitoring of any physical things and activities through internet connectivity. In this study we have proposed a system that is able to monitor the plant health with the assimilation of wireless sensors and wireless network. The proposed system is able to log the sensor values on the plants on the cloud server through internet connectivity.

Analysis and Development of Surface Distress Index Modified Based on Pavement Condition Index Criteria for Pavement Evaluation

The surface distress index (SDI) method has been used in Indonesia to assess road conditions, especially for areas with limited access, inadequate equipment, and limited local resources, which cause inaccuracies in the resulting pavement condition assessment. This research aims to develop a more accurate and efficient pavement condition assessment model based on three types of damage: cracks, potholes, and rutting. To generate accurate SDI values, we adopt and modify the deduct value (DV) curve based on the PCI method to determine the corresponding damage weight and the new road condition assessment. Based on the research results, the three modified SDI damage models showed an average accuracy value of 90%, which means that there is a good match between the models and the conditions on the ground, which is reinforced by the analysis of the mean absolute percentage error (MAPE) and root mean squared error (RMSE) values. In addition, the resulting development includes new assessment criteria and parameters, such as customized DV curve models and specific damage equations, condition index, condition rating, and maintenance types. Which in turn can support more effective and efficient infrastructure management and maintenance. Doi: 10.28991/CEJ-2025-011-01-014 Full Text: PDF

Analisis Tingkat Kerusakan Jalan Menggunakan Metode PCI dan Strategi Penanganannya Pada Ruas Jalan Meulaboh-Tutut

The analysis of road damage levels on the Meulaboh-Tutut road segment is crucial in determining effective maintenance priorities. This study aims to evaluate the damage levels on the Meulaboh-Tutut Road at KM 19 in Kaway XVI District, West Aceh Regency, using the Pavement Condition Index (PCI) method. The PCI method is a visual evaluation technique that provides an objective assessment of pavement conditions based on the type of damage, the area affected, and the severity of the damage. The PCI scale ranges from 0 to 100, where a PCI of 100 indicates total failure, while a score of 0 denotes a pavement in perfect condition. Field surveys revealed that the dominant types of damage in this road segment are alligator cracking, potholes, and subsidence. The average PCI score for this road section is recorded at 43.25, placing it in the "fair" category and indicating the need for intensive maintenance to restore road functionality. Detailed analysis shows variations in conditions across different road segments, with the lowest PCI score of 0 in certain segments indicating a perfect road, while the highest PCI score of 98.5, recorded between STA 0+350 and STA 0+400, indicates severe or complete failure. Based on the observed damage conditions, recommended repair strategies include the P5 and P6 repair methods for potholes, the P2 and P5 methods for alligator cracking, and the P5 and P6 methods for subsidence. These repair methods follow guidelines from the Ministry of Public Works, Directorate General of Highways, No. 001-01/M/BM/2011.

Identifying risks for effective maintenance of renewable energy plants in the new green Capital City of Indonesia

This study explores the use of Failure Mode and Effects Analysis (FMEA) to identify and mitigate risks in Solar Power Plants (PLTS) in Indonesia’s new National Capital City (IKN). As renewable energy is vital to Indonesia’s sustainability goals, managing risks in PLTS is essential for ensuring reliable and efficient energy production. The research identifies key challenges such as PV Array module failures, shading effects, and control system disruptions, which significantly impact electricity generation. Using the Risk Priority Number (RPN) methodology, the study ranks PV Array modules as the highest risk component (RPN 192), followed by Control and Management Systems (RPN 140) and PV Circuit Breakers and Video Monitoring Systems (RPN 120). These findings underline the need for targeted mitigation strategies. Recommendations include regular PV module inspections, hotspot monitoring technology, firmware updates, and enhanced fire protection systems. Preventive measures like grounding current maintenance and fire sensor upgrades further minimize operational disruptions, ensuring component durability and system efficiency. By leveraging the FMEA framework, this study systematically identifies and prioritizes risks while providing actionable solutions to enhance operational resilience. The results align with Indonesia’s vision of achieving 80 % renewable energy utilization in IKN by 2045. This research offers broader applicability for renewable energy systems in similar contexts, contributing to clean energy initiatives, reducing fossil fuel dependency, and supporting sustainable urban planning. It serves as a critical resource for integrating renewable energy into Indonesia’s green and resilient capital city vision

Features of Terrestrial Laser 3D Scanning Technology for Pavement Condition Assessment

Abstract. Problem. Traditional methods of road condition monitoring have limitations in terms of data accuracy and completeness, particularly in the detection of surface defects. There is a need for methods that provide accurate, rapid data collection to support effective maintenance and repair planning. Goal. The goal of this study is to develop and optimise a methodology for terrestrial 3D laser scanning and to provide practical recommendations for its application in road surface monitoring. Methodology. The proposed methodology examines the effectiveness of terrestrial laser scanning on road segments, addressing parameters such as optimal station count, placement and scanning mode to ensure accurate and comprehensive data collection. The Trimble TX6 scanner is used as an example, with specifications including a range of up to 120 metres, 2 mm accuracy and an adjustable scanning speed of up to 500,000 points per second. Results. The research identifies optimal setups for different road segments, covering short (~100–150 m) and long (up to 1000 m) sections, as well as complex road infrastructure such as multi-level interchanges. The results demonstrate the ability of the 3D scanning method to detect various linear and areal pavement defects. Originality. This study advances the field by developing specific recommendations for station positioning and scanning protocols, addressing blind spots, and ensuring data integrity for 3D modelling. Practical value. The application of this technology supports detailed analysis of pavement condition, significantly increasing the efficiency of data processing and improving repair planning and material estimation. The results are beneficial for transport infrastructure management, enabling more reliable monitoring and maintenance practices