Repair Actions Research Articles

A Portable Surface Plasmons Resonance Sensor Based on Single-Board Computer

The development of portable surface plasmons resonance (SPRs) are recently boosted by the use of instrumentation based on small-size single-board computer (SBC) platforms. The SPR and SBC combination is ideal for advances in direction of miniaturization and portability, however many scientific and engineering solutions yet required a computer connection for operation, and reported portable sensors in which hardware and software resources are misused. Here a standalone portable SPR based on an SBC is presented. The sensor operates without the need for another device, computer, or connection. Designed to work with thicker (3 mm thick) or thin (1.5 mm) biochips to support surface plasmons (SPs) and avoid rotational parts, a set of special structures was designed to encapsulate all electronics and optical elements. Composed of modular parts manufactured in a 3-D printer, the proposed arrangement enables quick maintenance and repair actions, eventually required for field applications. The chassis piece enclosure the components and fit the touchscreen. A replaced top cover piece enables the exchange of the thicker- and thin- biochips. And a back lid gives access to the internal components, where moving parts are designed and included to improve mechanical alignment. The sensor was optimized to Raspberry Pi SBC. The Python-based embedded software implements algorithms for acquiring, processing, displaying, storing, and delivering the SPR responses. To demonstrate the solution feasibility a Raspberry pi 3 model B was used. The well knows biochips PPBIO (Prism BIOchip) (thicker) and diffractive optical coupling element (DOCE) (thin) were attached to the proposed SBC-SPR Sensor. Those biochips dismissed index-matching gels and rotate parts, which benefits the sensing labor cost. Experiments of aqueous solutions were performed with a sensor operating in the so-called angular interrogation mode (AI mode). The results indicate the capability of phenomena excitation and sensor arrangement. The sensitivity figure-of-merit (FOM) was achieved in the order of 160°/RIU, similar to the performance of traditional set-up for those biochips.

THE NEED TO IMPROVE THE MANAGEMENT OF THE EQUIPMENT TECHNICAL STATE THROUGH THE USE OF DIGITAL MONITORING TOOLS

Thanks to the development of modern electronics and information technologies, digital diagnostic systems are being actively introduced at industrial enterprises in Russia. These systems allow you to monitor the technical condition of machines and significantly simplify maintenance by transmitting data for troubleshooting directly to the service departments. (Research purpose) The research purpose is analyzing and developing the proposals for improving the management of the technical state of machines through the introduction of digital monitoring tools. (Materials and methods) Studied the materials and documentation developed at VIM, GOSNITI, included in the «Integrated system of maintenance and repair of machinery in Agriculture», as well as methodological guidelines for diagnosing and managing the reliability of agricultural machinery. It was established that in order to effectively manage the technical condition of the operated and manufactured equipment, it is necessary to develop digital diagnostic and monitoring tools. (Results and discussion) They presented the priority requirements for the use of digital monitoring tools used in the implementation of assigned repair and maintenance actions based on data from periodic or continuous monitoring by diagnostic tools. Within the framework of the implementation of the industry project «Digital Agriculture» on the basis of KSTOR to develop proposals for managing the reliability of agricultural machinery. At the same time, digital monitoring systems should provide non-selective diagnostics of components with the possibility of general and indepth monitoring; assessment of the condition without the influence of the «human» factor; the efficiency of data extraction to obtain a significant technical and economic effect. (Conclusions) In order to effectively manage the technical condition, it is necessary to increase the level of adaptability of units and aggregates to diagnostics for the use of developed digital monitoring tools.

Time to fix: repair heuristics in Estonia and Portugal

This paper discusses the relevance of repair actions for the maintenance of social bonds, suggesting that these interventions have heuristic possibilities to address the crises of our time. The general argument points to important questions, such as the distinction between caretaking, endurance and resilience. Building on a literature review and ten years of fieldwork in two different locations (Estonia and Portugal), it develops an argument for post-brokenness and repair as a heuristic of the contemporary social condition. The different examples here included show the complex temporality of fixing interventions as well as the urge to understanding contextual nuances of socio-material in/stability and damage. A multi-sited attention to fixing interventions allows us to comprehend the processes and conditions under which certain things acquire socio-material stability against the grain. In this sense, repair work is presented as socially and politically-loaded by putting things to some order, activating other kinds of relations and holding together different dimensions of care.

Enhanced Ships Operation using Integrated Automation Systems

Global shipping for becoming sustainable will have to organize the ships and each field of shipping industry, in accordance efficient management and operation principles. This will require the adoption of new techniques and the transformation of companies, their ships, systems, and management practices. A modern automation and control system is a fully integrated system covering many aspects of the ship operation. Whether for cargo, passenger or special-purpose ships, Integrated Automation Systems meets every requirement in shipping and offers decisive advantages over the entire "life" of a ship. The adaptation can suit perfectly to the special requirements in shipping and creates the prerequisites for maximum economy, reliability, and safety on board. Integrated Automation includes the propulsion plant operation, power management operation on the auxiliary engines, auxiliary machinery operation, cargo loading and unloading operation, navigation and administration of maintenance and purchasing of spares. The study focuses on implementation of IAS and its contribution in ship's performance and safety. The proposed model incorporates different failure rates for the different sensors of the Integrated Automation system as well as repair actions that need to be performed. The system can experience various levels of deterioration and when a failure occurs, a repair procedure is carried out and the system is restored to its initial fully operational state. A numerical example based on the empirical data is used to illustrate the proposed model.
 
 Received: 23 September 2022 / Accepted: 28 November 2022 / Published: 20 December 2022

Investigating economic replacement policy under uncertainty for managerial application based on grey-reliability approach using QCC

PurposeThe main purpose of present study is to model the replacement policy under uncertainty for managerial application based on grey-reliability approach by considering the subjective views of quality control circle (QCC). The study objectively links the optimality between individual replacement and group replacement policies for determining the minimum operational costs. The integrated framework between QCC, replacement theory, grey set theory and supply chain management is presented to plan replacement actions under uncertainty.Design/methodology/approachThe study proposes the concept of grey-reliability index and built a decision support model, which can deal with the imprecise information for determining the minimum operational costs to plan subsequent maintenance efforts.FindingsThe findings of the study establish the synergy between individual replacement and group replacement policies. The computations related to the numbers of failures, operational costs, reliability index and failure probabilities are presented under developed framework. An integrated framework to facilitate the managers in deciding the replacement policy based on operational time towards concerning replacement of assets that do not deteriorate, but fails suddenly over time is presented. The conceptual model is explained with a numerical procedure to illustrate the significance of the proposed approach.Originality/valueA conceptual model under the framework of such items, whose failures cannot be corrected by repair actions, but can only be set by replacement is presented. The study provides an important knowledge based decision support framework for crafting a replacement model using grey set theory. The study captured subjective information to build decision model in the ambit of replacement.

Residual compressive strength of concrete after exposure to high temperatures: A review and probabilistic models

The mechanical properties of concrete degrade after exposure to high temperatures during a fire event. Proper assessment of damage, determination of the remaining capacity of the structure, and recommendations for repair actions require the quantification of the reduction in the compressive strength of concrete after fire. This paper collects and analyzes the existing data on residual compressive strength of normal strength concrete in the literature. The analyses aim to identify the influential attributes that could be used to reduce scatter in the data. Parameters related to aggregate type, concrete mix, and testing protocol are studied. Subsets of the dataset for concrete with siliceous and calcareous aggregates are created. Data for siliceous concrete is further categorized based on the retaining time of the maximum temperature during the test. The subsets are used to propose probabilistic models for the residual compressive strength of concrete with siliceous and calcareous aggregates. The models follow the Weibull distribution and are continuous functions that can be implemented in analytical and computational frameworks. The medians of the proposed model and data are close to the Eurocode reduction factors for the siliceous concrete but fall below the Eurocode reduction factors for concrete with calcareous aggregate.

Use of corridors to select bridges to retrofit in road networks in seismic regions

ABSTRACT This study proposes the use of bridge clusters, defined as Corridors, to support optimal bridge retrofitting strategies for seismic risk management of road networks. A Corridor is defined as a set of bridges that works jointly to ensure connectivity and traffic flow between different areas of a region. To detect Corridors, a Markov Clustering Algorithm is proposed. Using the San Francisco Bay Area road network as a testbed, this clustering technique selects sets of bridges that correspond to main traffic arteries such as highways and high-capacity road segments. After Corridors have been detected, a two-stage stochastic optimization is implemented to detect which bridges should be retrofitted to ensure an acceptable network performance. This optimization couples retrofitting actions in a Corridor with the repair actions to damaged bridges after an earthquake. The Corridors-Supported Optimization decreases road network disruption more than other approaches based on ranking bridges according to their traffic capacity or location in the network.

Scheduling Post-Disaster Power System Repair With Incomplete Failure Information: A Learning-to-Rank Approach

This paper proposes a novel repair rule set (RRS) for scheduling the power system infrastructure repair after the occurrence of extreme events. RRS is made up of multiple repair rules, each of them can be applied in arbitrary post-disaster failure scenarios to rank the repair actions by priority. A learning-to-rank technique called AdaRank is used to train the repair rules by combining the weak learners derived from the dynamic repair scheduling model. Then, RRS is constructed by iteratively clustering the training cases and retraining the repair rule for each cluster. Increasing the number of repair rules within RRS allows it to differentiate various types of failure scenarios, thereby improving its performance. Further combined with multi-label K nearest neighbor (ML-KNN) technique, RRS is able to schedule the repair without the full knowledge of real-time failure information, such as the estimated repair time. The results of case studies on IEEE-118 test systems show that the proposed method has a desirable performance compared to the exact mathematical optimization model. Moreover, it reduces the requirement for failure information while significantly improving the computational efficiency.

Fighting Fire with Fire: Exosomes and Acute Pancreatitis-Associated Acute Lung Injury.

Acute pancreatitis (AP) is a prevalent clinical condition of the digestive system, with a growing frequency each year. Approximately 20% of patients suffer from severe acute pancreatitis (SAP) with local consequences and multi-organ failure, putting a significant strain on patients' health insurance. According to reports, the lungs are particularly susceptible to SAP. Acute respiratory distress syndrome, a severe type of acute lung injury (ALI), is the primary cause of mortality among AP patients. Controlling the mortality associated with SAP requires an understanding of the etiology of AP-associated ALI, the discovery of biomarkers for the early detection of ALI, and the identification of potentially effective drug treatments. Exosomes are a class of extracellular vesicles with a diameter of 30-150 nm that are actively released into tissue fluids to mediate biological functions. Exosomes are laden with bioactive cargo, such as lipids, proteins, DNA, and RNA. During the initial stages of AP, acinar cell-derived exosomes suppress forkhead box protein O1 expression, resulting in M1 macrophage polarization. Similarly, macrophage-derived exosomes activate inflammatory pathways within endothelium or epithelial cells, promoting an inflammatory cascade response. On the other hand, a part of exosome cargo performs tissue repair and anti-inflammatory actions and inhibits the cytokine storm during AP. Other reviews have detailed the function of exosomes in the development of AP, chronic pancreatitis, and autoimmune pancreatitis. The discoveries involving exosomes at the intersection of AP and acute lung injury (ALI) are reviewed here. Furthermore, we discuss the therapeutic potential of exosomes in AP and associated ALI. With the continuous improvement of technological tools, the research on exosomes has gradually shifted from basic to clinical applications. Several exosome-specific non-coding RNAs and proteins can be used as novel molecular markers to assist in the diagnosis and prognosis of AP and associated ALI.

A unique biomimetic modification endows polyetherketoneketone scaffold with osteoinductivity by activating cAMP/PKA signaling pathway.

Osteoinductivity of a biomaterial scaffold can notably enhance the bone healing performance. In this study, we developed a biomimetic and hierarchically porous polyetherketoneketone (PEKK) scaffold with unique osteoinductivity using a combined surface treatment strategy of a sulfonated process and a nano bone-like apatite deposition. In a beagle intramuscular model, the scaffold induced bone formation ectopically after 12-week implantation. The better bone healing ability of the scaffold than the original PEKK was also confirmed in orthotopic sites. After culturing with bone marrow–derived mesenchymal stem cells (BMSCs), the scaffold induced osteogenic differentiation of BMSCs, and the new bone formation could be mainly depending on cell signaling through adenylate cyclase 9, which activates the cyclic adenosine monophosphate/protein kinase A signaling cascade pathways. The current work reports a new osteoinductive synthetic polymeric scaffold with its detailed molecular mechanism of action for bone repair and regeneration.

Bridge deterioration models for different superstructure types using Markov chains and two-step cluster analysis

Bridges deteriorate due to different causes such as ageing and climatic conditions. As a consequence, repair actions are considered more expensive than maintenance. Therefore, the prediction of maintenance at the bridge network level is particularly complex due to the considerable level of heterogeneity spanning various bridge types and functions. Consequently, in this article an analytical deterioration model called ‘two-step cluster analysis’ is presented and applied to predict the results of a homogeneous data set. This model had been used in different fields including civil engineering but not for superstructure deterioration forecasting. Variables that interfere with the performance of the structure were considered, such as geometry, volume of traffic, among others. The results show that deterioration drop rapidly if repair treatments were not performed. Variations of up to 20.9% at 50 years between the degradation of the database and the analysis of groups, demonstrates the importance of grouping within the analysis of the database, since the late maintenance can affect the life of the bridge. Preventive actions could significantly mitigate the deterioration process. The applied methodology benefits from current bridge inspection practice and produces a probabilistic rating that is consistent with the Markov approach for model deterioration and grouping variables.

Data-driven optimization of repair schemes and inspection intervals for highway bridges

Routine bridge inspections enable timely maintenance plans based on potential risks. These inspections are performed at fixed time intervals, which may be unnecessarily short for bridges in excellent conditions and precariously long for those in poor conditions. Also, deterioration models used in current bridge management approaches fail to include the effect of age, causing inaccurate predictions. This paper develops a hazard-based bridge management approach where inspection frequencies and repair suggestions are optimized based on hazard levels. A two-dimensional Markov model describes the age-dependent bridge deterioration, and a semi-Markov decision process optimizes inspection intervals and repair decisions. The objective is to minimize the expected long-term total annual costs, including expenses associated with maintenance and traffic detours. The modeling process and maintenance management framework was demonstrated on an example bridge superstructure in New York. The results showed lower annual and total costs than the conventional management method. As expected, sensitivity analyses indicated that the pre-specified choices of inspection intervals for low and high hazard levels have a significant effect on the calculated hazard threshold levels. The proposed maintenance management method is adaptable and practical; bridge authorities can identify appropriate repair actions and make inspection decisions based on simple threshold comparisons.

Improving Data-Driven Infrastructure Degradation Forecast Skill with Stepwise Asset Condition Prediction Models

Organizations with large facility and infrastructure portfolios have used asset management databases for over ten years to collect and standardize asset condition data. Decision makers use these data to predict asset degradation and expected service life, enabling prioritized maintenance, repair, and renovation actions that reduce asset life-cycle costs and achieve organizational objectives. However, these asset condition forecasts are calculated using standardized, self-correcting distribution models that rely on poorly-fit, continuous functions. This research presents four stepwise asset condition forecast models that utilize historical asset inspection data to improve prediction accuracy: (1) Slope, (2) Weighted Slope, (3) Condition-Intelligent Weighted Slope, and (4) Nearest Neighbor. Model performance was evaluated against BUILDER SMS, the industry-standard asset management database, using data for five roof types on 8549 facilities across 61 U.S. military bases within the United States. The stepwise Weighted Slope model more accurately predicted asset degradation 92% of the time, as compared to the industry standard’s continuous self-correcting prediction model. These results suggest that using historical condition data, alongside or in-place of manufacturer expected service life, may increase the accuracy of degradation and failure prediction models. Additionally, as data quantity increases over time, the models presented are expected to improve prediction skills. The resulting improvements in forecasting enable decision makers to manage facility assets more proactively and achieve better returns on facility investments.

Structural assessment and expected utility gain

This paper contains an introduction, probabilistic formulation, and exemplification (1) of system state and utility actions, (2) system state and utility action value analysis and (3) a threshold formulation for a predicted information and action decision analysis. The approaches build upon structural condition assessment and provide a basis for the condition management by maximising the expected utility for information and actions before information acquirement and action implementation. Following the basic distinction of system state and utility actions, strengthening, replacement, repair, load reduction and consequence reduction actions are formulated. With an exemplary study encompassing an expected utility calculation and an action value analysis, it is demonstrated how expected utility optimal physical system changes can be identified before implementation. The threshold formulation for a predicted information and action decision analysis relies on the equality of the decision theoretical posterior action optimality condition. By extending the exemplary predicted action decision analyses with different structural health information (SHI), the threshold formulation is exemplified and the optimal condition management strategies are identified.

Rehabilitating from Environmental Misconduct: The Potential of Orchestrating Corrective Actions

Most firms attempt to regain lost public reputation after reports of corporate misconduct. Prior research has focused on identifying individual corretive actions for reputation repair, suggesting a costly and disruptive action such as replacing organizational leadership is necessary. This research, however, gave limited consideration to the use of multiple corrective actions over time. Integrating signaling theory and the literature on rehabilitation and corrective actions, we argue that the orchestration of corrective actions can restore reputation, even if individually taken these actions do not meet the conventional criteria of successful repair signals. We find support for our theory in a sample of publicly traded U.S. firms implicated in environmental misconduct between 2008 and 2018. We advance the literature on rehabilitation by providing insights into the orchestration of corrective actions over time and caution firm stakeholders that well-crafted reform efforts do not necessarily reflect underlying attributes of the firm. In doing so, we also alert signaling research to the importance of signal orchestartion when combining multiple signals to form a set.

Full-scale experimental investigation on the seismic performance of PC columns before and after repair of grouting defects

This research focused on investigating the influence of insufficient grouting on the seismic performance of precast concrete (PC) columns, connected using grouted sleeves, and validating the feasibility and reliability of the repair method for insufficient grouting. To realize these, five full-scale PC columns were tested under cyclic loads, including one specimen without grouting defects, two specimens with defect ratios of 45% and 60%, respectively, which were determined by previous cyclic test results of grouted sleeve connections (GSCs), and two repaired specimens which had initial defect ratios of 45% and 60%, respectively. The effects of defect ratios and repair actions were analyzed, and the test results indicated the following: 1) The bar slip dominated the failure of two specimens with defect ratios as expected, rather than the flexural failure mode for the specimen without defects. A rocking phenomenon was observed since the occurrence of bar slip, and the pinching phenomenon appeared for the corresponding hysteretic curves. The load-bearing, deformation and energy dissipation capacities were significantly lower than those of the specimens without defects. The increase in the defect ratio decreased the drift ratio associated with the bar-slip phenomenon, but the influence of the defect ratio on the seismic performance after the bar slip was negligible. 2) The bar-slip behavior was effectively prevented after the repair of insufficient grouting. Hence, the repaired specimens exhibited the expected flexural failure mode, and the load-bearing, deformation, and energy dissipation capacities were generally comparable with those of the specimen without defects. Rocking behavior and pinching phenomenon were not observed after repair. The negative influences of insufficient grouting were almost eliminated, thus validating the reliability of the repair method. The research outcome can serve as a reference for quality control of PC structures using GSCs.

Improving circularity in municipal solid waste management through machine learning in Latin America and the Caribbean

Machine Learning (ML) consists of a set of methods that allow a system to learn data patterns and has applications in many stages of MSWM. Improvements in MSWM focused on resources recovery in LA&C can be speed up by the use of the ML methods. This study aims to analyze the opportunities and challenges faced when using the ML methods to improve circularity in MSWM in LA&C. The methodology adopted was a systematic literature review using the PRISMA protocol in the Web of Science® database from 2010 to 2021, and bibliometric analysis using the Biblioshiny® application, the web interface for Bibiometrix® package from Rstudio® software. A total of 188 papers were obtained from the bibliographic search. The advancement of MSWM in LA&C has as challenges the lack of reliable data on the composition and production of the waste, the low rate of waste used as a resource, the need to change consumption patterns, social inclusion of informal waste collectors, and the inclusion of repair and reuse actions to reduce waste generation. Meanwhile, the main challenges when considering the use of ML in LA&C are the inexistence or dispersion of data with reliable time series and the lack of knowledge of decision makers about the potential use of the ML methods. Specifically in LA&C, it was observed that hybrid models that apply ML to waste composition data, ML methods to improve IoT applications and GIS data usage aggregated with ML methods could speed up the transition to Circular Economy in LA&C.

Improved four-level criteria for reliability assessment of timber components subjected to decay

Safety evaluation of existing timber structures is of paramount importance for making reliable and timely decisions on repair actions. Among the inspection items, biological decay is a key factor that seriously affects the structural performance of timber components. Given the unclear limits of safety grades in the existing assessment criteria and the increased reliability requirements by the latest Chinese code GB50068-2018, improved four-level criteria are proposed to update the grading rules for timber components subjected to decay. The criteria are developed by probabilistic models and limit state functions with the depth of decay as a key variable. The revised target reliability index and load partial coefficients are taken into account. The JC method and importance sampling method are applied to obtain the relationship between the reliability index and the relative depth of decay under various load conditions. The proposed four-level criteria give explicit grading rules for the safety evaluation of decayed timber components and satisfy the revised reliability requirements. It is suggested that the ratio of the decayed area to the whole cross-section ρcr is set to be 0, 5.58%, and 8.36% for main flexural components; 0, 7.40%, and 11.92% for main tension components; 0, 5.91%, and 8.61% for main compression components as limits between au and bu, bu and cu, cu and du.

The Growing Infrastructure Crisis: The Challenge of Scour Risk Assessment and the Development of a New Sensing System

Scour action is one of the main factors that add significant stress to the growing infrastructure crisis as it is considered one of the most destructive flood-related hazards occurring around underwater foundation elements. Recent cases of bridge failures have highlighted the need for a reliable scour monitoring and early warning system to assess flood and geo-hazards in real-time, providing advanced key info for repair and maintenance actions. Despite the past efforts to provide such a system for scour assessment, most of the developed instruments were not able to offer a reliable solution for scour monitoring, due to technical and cost issues. As a result, there currently exists a gap in the knowledge and understanding of scour mechanism during flood incidents. This study presents the development of a new sensing system to assess hydro-hazards at bridge infrastructure. It initially focuses on factors contributing to the growing infrastructure crisis and provides an overview of the current practices and assessment procedures to assess scour processes and a summary of advantages and limitations of existing monitoring efforts. A new monitoring concept for assessing scour and sediment deposition processes is then presented focusing on modelling the geometric components of a new sensor which is evaluated in simulations under different environments that represent prospective field conditions. Main results are analysed and presented focusing on key criteria that maximize sensitivity of the sensor to scour and sedimentation processes. The obtained results indicate that the sensor has the potential to provide a new monitoring device for scour and sediment deposition monitoring, and it is proposed to be further developed and assessed in laboratory and field conditions. This study aspires to contribute to the ongoing discourse on the use of sensing techniques to monitor, assess, and manage scour action effectively.

Reinforcement learning for adaptive maintenance policy optimization under imperfect knowledge of the system degradation model and partial observability of system states

Maintenance policy optimization usually is faced with challenges that arise from an imperfect knowledge of system degradation models and from the partial observability of system degradation states. This paper proposes a reinforcement learning method to address these two challenges for a class of maintenance problems with Markov degradation processes. The reinforcement learning approach consists of a learning component and a planning component. Using sequentially collected observations, at each step of decision-making the learning component improves the knowledge of system degradation in terms of the probability distributions of the transition rates based on sequential Bayesian inference. Using the updated transition rates, at each step of decision-making the maintenance policy optimization problem is then formulated as a partially observable Markov decision problem, and the planning component computes the optimal maintenance policy that maximizes the expected cumulative reward. The proposed method is illustrated using a numerical example with repair and inspection maintenance actions. The result shows that as more observations are collected, the learning component progressively learns the true system degradation process, and the planning component adjusts the optimal maintenance policy accordingly as well, which leads to increased reward.