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The operations and maintenance phase is typically the longest phase within the building life cycle. Proper and consistent building maintenance is imperative for several reasons, including extending the life of buildings, improving occupants' comfort and safety, and ultimately saving on long-term costs. Budgets for maintenance should be foreseen. The costs of statutory periodic inspections, the costs of replacing worn materials and elements, the costs of periodic works and repairs, the costs of reactive maintenance and the costs of operation will be shown for the analyzed building—a school. This paper outlines the development of a 15-year maintenance plan and program for the building, covering the period from 2024 to 2038. The plan incorporates a discount rate of 3.64% and accounts for inflation when calculating costs. The present value of the costs of maintenance and operation of the building for 15 years is EUR 1,978,292.20 and the largest share of these costs is the operation cost of the building with EUR 1,599,002.18 (80.83%). A sensitivity analysis was conducted by varying the discount rate and analysis period, resulting in recalculated present values for maintenance costs. The analysis reveals a correlation of 26.73% between the present value of maintenance and operation costs over a 15-year period and the associated capital costs. [ABSTRACT FROM AUTHOR]

Copyright of Fly Ash Comprehensive Utilization is the property of Hebei Fly Ash Comprehensive Utilization Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The operations and maintenance phase is typically the longest phase within the building life cycle. Proper and consistent building maintenance is imperative for several reasons, including extending the life of buildings, improving occupants’ comfort and safety, and ultimately saving on long-term costs. Budgets for maintenance should be foreseen. The costs of statutory periodic inspections, the costs of replacing worn materials and elements, the costs of periodic works and repairs, the costs of reactive maintenance and the costs of operation will be shown for the analyzed building—a school. This paper outlines the development of a 15-year maintenance plan and program for the building, covering the period from 2024 to 2038. The plan incorporates a discount rate of 3.64% and accounts for inflation when calculating costs. The present value of the costs of maintenance and operation of the building for 15 years is EUR 1,978,292.20 and the largest share of these costs is the operation cost of the building with EUR 1,599,002.18 (80.83%). A sensitivity analysis was conducted by varying the discount rate and analysis period, resulting in recalculated present values for maintenance costs. The analysis reveals a correlation of 26.73% between the present value of maintenance and operation costs over a 15-year period and the associated capital costs.

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Artificial walking survey, ground penetrating radar (GPR) and falling weight deflectometer (FWD) were used to test the actual condition of the highway pavement in reconstruction and expansion project for better maintenance decision-making. Therefore, a decision-making system with the application of four evaluation indexes including deflection value (DV), pavement surface condition within 20 m(PCI20), maintenance effect (ME) and internal crack rate (ICR) was constructed. Through the pavement empirical-mechanical calculation and the Delphi method analysis, the corresponding threshold for the DV, PCI20 and ICR were 27 (0.01 mm), 80, and 20 m/100 m2 respectively. Then, the decision-making system was applied for the 5.5 km section with the detection and evaluation results, and the advised maintenance plans of different lanes were determined. Lastly, the effect of the decision-making system was analyzed. Results show that the decision-making system can scientifically decide the treatment plans and save the maintenance cost by about 30.1 %. The deflection near the grouting holes significantly decreases resulting from the high-density cement slurry distribution. The milling and repaving have a high structural reinforcement ability of reducing deflection up to more than 66 %, which is positively related to the milling depth.

Copyright of Journal of Exact Sciences is the property of Master Editora and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Goal: This study aims to use artificial intelligence, specifically a random forest model, to predict the annual corrosion rate on FPSO offshore platforms in the oil and gas industry. Corrosion is a significant cause of equipment failure, leading to costly replacements. The random forest model, a machine learning technique, was developed using climatic and other relevant data to forecast corrosion trends based on selected variables. Design/Methodology/Approach: The methodology involved four steps: identifying influential factors affecting corrosion, selecting factors based on reliability and accessibility of measurements, applying the machine learning model to predict annual corrosion progression, and comparing the random forest model with other ML models. Results - The results showed that the random forest regression model successfully predicted corrosion rates, indicating an average yearly increase of 2.43% on the analyzed platforms. The main factors influencing this increase were wind speed, percentage of measured corrosion, and platform operating time. Regions with higher incidence of these factors are likely to experience higher corrosion rates, necessitating more frequent maintenance. Limitations of the investigation - The research sample consisted exclusively of 4 platforms located in the offshore region of Rio de Janeiro, Brazil. Thus, the results obtained must be interpreted as representative of these platforms and respective climate conditions. Practical implications - The use of data science tools to improve corrosion management allows managers to have knowledge of which areas has a greater or lesser tendency to corrode, helping to prioritize maintenance activities over time. Originality/value - This study aims to fill gaps regarding the use of random forest techniques for regression focused on predicting the rate of increase in corrosion. It offers a novel approach to assist decision-making in maintenance planning, providing insights into influential corrosion factors and facilitating more effective painting plans to preserve industrial unit integrity. [ABSTRACT FROM AUTHOR]

Goal: This study aims to use artificial intelligence, specifically a random forest model, to predict the annual corrosion rate on FPSO offshore platforms in the oil and gas industry. Corrosion is a significant cause of equipment failure, leading to costly replacements. The random forest model, a machine learning technique, was developed using climatic and other relevant data to forecast corrosion trends based on selected variables. Design/methodology/approach: The methodology involved four steps: identifying influential factors affecting corrosion, selecting factors based on reliability and accessibility of measurements, applying the machine learning model to predict annual corrosion progression, and comparing the random forest model with other ML models. Results - The results showed that the random forest regression model successfully predicted corrosion rates, indicating an average yearly increase of 2.43% on the analyzed platforms. The main factors influencing this increase were wind speed, percentage of measured corrosion, and platform operating time. Regions with higher incidence of these factors are likely to experience higher corrosion rates, necessitating more frequent maintenance. Limitations of the investigation - The research sample consisted exclusively of 4 platforms located in the offshore region of Rio de Janeiro, Brazil. Thus, the results obtained must be interpreted as representative of these platforms and respective climate conditions. Practical implications – The use of data science tools to improve corrosion management allows managers to have knowledge of which areas has a greater or lesser tendency to corrode, helping to prioritize maintenance activities over time. Originality/value - This study aims to fill gaps regarding the use of random forest techniques for regression focused on predicting the rate of increase in corrosion. It offers a novel approach to assist decision-making in maintenance planning, providing insights into influential corrosion factors and facilitating more effective painting plans to preserve industrial unit integrity.

Attention to the condition assessment and conservation of cultural heritage is increasing due to growing sensitivity to the preservation of the built and natural environment and awareness of the risks associated with natural hazards. In this context, a comprehensive approach to the conservation and valorization of cultural heritage requires the combined action of different skills to achieve reliable assessment of the conditions of valuable assets and sites based on qualitative and quantitative indicators. This paper explores the issues of conservation and sustainable management of archaeological sites, considering humanistic and technical aspects. It reviews the current rules and practices concerning the maintenance and administration of archaeological heritage and outlines the implementation of a novel procedure, based on low-cost tools, to assess and describe the current condition of archaeological assets. Specific attention is paid to the interaction between experts and researchers operating in both humanistic and technical fields and to the knowledge and data contribution available on modern web-based platforms to implement reliable and low-cost data acquisition for the development of worthwhile plans for the conservation and enhancement of cultural heritage. The Samnite complex of Pietrabbondante, Molise, South Italy, is employed to apply and check the proposed tools in the real world.

This study proposes an approach for conducting comparative assessments between renovation and new construction through the use of CIB utility theory systems and applies the methodology to an adaptive case study. To this end, the theoretical underpinnings of CIB were explained in the literature review, and the method of data analysis, selection of usability functions, and standard condition assessment scale were explained. The proposed model can be used to evaluate CIB utility theory systems and for life-cycle sustainability assessment (LCSA). The current findings revealed that the maintenance planning system and 5 G information must be improved because they are the key to Taiwan’s future development. Considering that the aim of cost-optimal maintenance and renovation planning is to not only optimise the service life-cycle costs of building components but also to maintain appropriate conditions and the performance of the respective components, the model used in this study prioritises performance over cost. Therefore, proposals are made for the future development of LCSA considering stakeholders of various projects for new construction by enhancing objectivity and accessibility.

Responding to the needs of quality and robustness of analysis and management of degradation of equipment, to increase their life cycle and to expand these facilities to become more and more sophisticated and agronomic. This work proposes a contribution to increase the survival of a gas turbine, installed in a gas-compression plant, with a comparative study between the two-parameter Weibull distribution. A new modified Weibull distribution was proposed also to reduce the risk of occurrence of failure in this rotating machine. A Statistical analysis and validation on the synthesis of turbine's reliability data and failures were considered, with a particular focus on the use of this data to increase the availability of this type of machine. So, developing a maintenance plan based on their reliability indices for scheduled inspections.

Attention to the condition assessment and conservation of cultural heritage is increasing due to growing sensitivity to the preservation of the built and natural environment and awareness of the risks associated with natural hazards. In this context, a comprehensive approach to the conservation and valorization of cultural heritage requires the combined action of different skills to achieve reliable assessment of the conditions of valuable assets and sites based on qualitative and quantitative indicators. This paper explores the issues of conservation and sustainable management of archaeological sites, considering humanistic and technical aspects. It reviews the current rules and practices concerning the maintenance and administration of archaeological heritage and outlines the implementation of a novel procedure, based on low-cost tools, to assess and describe the current condition of archaeological assets. Specific attention is paid to the interaction between experts and researchers operating in both humanistic and technical fields and to the knowledge and data contribution available on modern web-based platforms to implement reliable and low-cost data acquisition for the development of worthwhile plans for the conservation and enhancement of cultural heritage. The Samnite complex of Pietrabbondante, Molise, South Italy, is employed to apply and check the proposed tools in the real world. [ABSTRACT FROM AUTHOR]

The effects of climate change on coastal areas are expected to significantly influence the risk for port operations. In the present work, a novel methodology for the quantitative assessment of the performances of upgraded rubble-mound breakwaters under a changing climate is proposed. For each considered upgrading option, the failure probability related to a certain limit state is calculated through the implementation of Monte Carlo (MC) simulations, using the factor of change (FoC) method to include the projected future climate. Three indexes are defined for the immediate and intuitive interpretation of the results: i) the ratio between the calculated and the maximum acceptable failure probability during lifetime (r); ii) the rate of the growth of the failure probability during lifetime (s); iii) the coefficient of variation of the failure probability due to both the intrinsic uncertainty of the MC simulation and the variability of future climate (v). The methodology was applied to the case study of the Catania harbor breakwater, considering the failure of different upgrading solutions due to the collapse of the outer armor layer and to excessive mean overtopping discharge. The results revealed the acceptability of the structural and hydraulic performances of all the tested configurations, under both present and future climate. Moreover, a high climate-related variability of the future failure probability was found. The usefulness of the proposed indexes for designer and decision-makers was also demonstrated. In particular, r gives direct information about the acceptability of the structure performances, enabling the immediate comparison between different configurations and climate scenarios. The index s is fundamental to calculate the appropriate times to implement repair interventions during the structure lifetime. Finally, v allows the identification of those situations which requires the design of highly flexible maintenance plans, able to adapt to a very variable climate avoiding excessive costs.

Copyright of ESPOCH Congresses: The Ecuadorian Journal of S.T.E.A.M. is the property of Knowledge E DMCC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In an effort to achieve an optimal availability time of induction motors via fault probabilities reduction and improved prediction or diagnostic tools responsiveness, a conditional probabilistic approach was used. So, a Bayesian network (BN) has been developed in this paper. The objective will be to prioritize predictive and corrective maintenance actions based on the definition of the most probable fault elements and to see how they serve as a foundation for the decision framework. We have explored the causes of faults for an induction motor. The influence of different power ranges and the criticality of the electric induction motor are also discussed. With regard to the problem of induction motor faults monitoring and diagnostics, each technique developed in the literature concerns one or two faults. The model developed, through its unique structure, is valid for all faults and all situations. Application of the proposed approach to some machines shows promising results on the practical side. The model developed uses factual information (causes and effects) that is easy to identify, since it is best known to the operator. After that comes an investigation into the causal links and the definition of the a priori probabilities. The presented application of Bayesian networks is the first of its kind to predict faults of induction motors. Following the results of the inference obtained, prioritizations of the actions can be carried out.

The rotating machines like gas turbine types are highly valuable in the gas transportation industry. They are often strategic and have a major impact on the proper operation of gas transport and compression facilities. In this context, the aim of this work is to increase efficiency and production by developing an approach for this kind of installations using real data collected from the operation of the gas turbine. The objective is to provide a database relating to the reliability, availability, and maintenance of gas turbines while using standard reliability approaches. In addition, ensuring maximum availability of this type of rotating machine by preventing its failures and reducing emissions, and by minimizing start-up sequences, which reduces emissions when starting this machine. Also, the proper operation of these gas turbine installations with the reliability approaches developed in this work makes it possible to model the effects of failures in order to predict optimal operating performance and increase the life of their components. This, therefore, ensures a reliable and safe operation of the gas turbine in a compression station for economically profitable gas recovery. [ABSTRACT FROM AUTHOR]

Combined laboratory and field research examining the possible alterations caused by herbicide treatments applied to the construction materials (schist and some granite, bound with mortar) in the Roman wall of Lugo (NW Spain), declared a World Heritage site by UNESCO in 2000, was performed in three separate studies in the past 20 years. In the summers of 1998 and 1999, the herbicides glyphosate, sulphosate and glufosinate–ammonium, as well as physical treatments (infrared and burning) were separately applied to different areas of the wall. In the spring of 2016, the oxyfluorfen herbicide Goal Supreme® was applied to test areas. In the winter of 2018, three essential oils, Origanum vulgare L., Thymus zygis Loefl. ex L., and Thymus vulgaris L., were each applied to test areas. Mineralogical modifications in the materials (determined by X-ray diffraction analysis), as well as visible physical changes, such as colour changes, and the appearance of saline residues were evaluated after the treatments. In the 1998/9 trial, glyphosate and both physical treatments triggered changes in the vermiculite clay minerals in the schists, and the physical treatments also caused changes in the kaolinite. None of the treatments caused highly perceptible colour changes. The oxyfluorfen herbicide did not cause any mineralogical alterations in the construction materials, but it did generate an increase in chloride, nitrate and sulphate contents of the granite and a slight darkening of this material. In the most recent study, the only deleterious effect observed was a perceptible increase in lightness and reduction in the yellow component after the application of Thymus zygis Loefl. ex L. essential oil to granite. [ABSTRACT FROM AUTHOR]

This study aims to develop a bridge maintenance plan in consideration of the resilience of a road network against natural disasters. Toward this end, the reliability of a road network is calculated using the failure probability of bridges as an evaluation index. Bridges are a form of social infrastructure that are essential for maintaining daily life; however, their repair and reinforcement can cause the partial disruption of road networks. Nonetheless, insufficient bridge maintenance can cause serious harm to people in an emergency, such as after an earthquake. Therefore, studies have aimed to develop long-term plans to minimize the life-cycle cost (LCC) of bridges. Other studies have measured the probability of bridge failure and developed countermeasures against natural disasters to improve the reliability and resilience of bridges. Unfortunately, past studies have focused on these aspects independently of one another, even though an effective bridge maintenance plan requires them to be considered together. A plan that both minimizes LCCs and improves resilience is expected to be useful for sustainable bridge management. We search for candidates for optimal plans for long-term bridge maintenance by using a genetic algorithm (GA). The proposed method has three objectives: (1) minimization of bridge repair and reinforcement costs, (2) minimization of disruption to social activities (i.e., user cost) due to bridge maintenance, and (3) maximization of resilience against natural disasters. First, the GA is used to search for a plan with minimized maintenance cost and a flexible work period by considering the uncertainties related to bridges. The remaining indices are optimized based on the flexible period. The resilience of bridges is measured through Monte Carlo simulations by using the failure probability based on the bridge state at a certain point. Numerical experiments are conducted to demonstrate the usefulness of this method for establishing a sustainable bridge maintenance plan.

In an effort to achieve an optimal availability time of induction motors via fault probabilities reduction and improved prediction or diagnostic tools responsiveness, a conditional probabilistic approach was used. So, a Bayesian network (BN) has been developed in this paper. The objective will be to prioritize predictive and corrective maintenance actions based on the definition of the most probable fault elements and to see how they serve as a foundation for the decision framework. We have explored the causes of faults for an induction motor. The influence of different power ranges and the criticality of the electric induction motor are also discussed. With regard to the problem of induction motor faults monitoring and diagnostics, each technique developed in the literature concerns one or two faults. The model developed, through its unique structure, is valid for all faults and all situations. Application of the proposed approach to some machines shows promising results on the practical side. The model developed uses factual information (causes and effects) that is easy to identify, since it is best known to the operator. After that comes an investigation into the causal links and the definition of the a priori probabilities. The presented application of Bayesian networks is the first of its kind to predict faults of induction motors. Following the results of the inference obtained, prioritizations of the actions can be carried out. [ABSTRACT FROM AUTHOR]

Combined laboratory and field research examining the possible alterations caused by herbicide treatments applied to the construction materials (schist and some granite, bound with mortar) in the Roman wall of Lugo (NW Spain), declared a World Heritage site by UNESCO in 2000, was performed in three separate studies in the past 20 years. In the summers of 1998 and 1999, the herbicides glyphosate, sulphosate and glufosinate–ammonium, as well as physical treatments (infrared and burning) were separately applied to different areas of the wall. In the spring of 2016, the oxyfluorfen herbicide Goal Supreme® was applied to test areas. In the winter of 2018, three essential oils, Origanum vulgare L., Thymus zygis Loefl. ex L., and Thymus vulgaris L., were each applied to test areas. Mineralogical modifications in the materials (determined by X-ray diffraction analysis), as well as visible physical changes, such as colour changes, and the appearance of saline residues were evaluated after the treatments. In the 1998/9 trial, glyphosate and both physical treatments triggered changes in the vermiculite clay minerals in the schists, and the physical treatments also caused changes in the kaolinite. None of the treatments caused highly perceptible colour changes. The oxyfluorfen herbicide did not cause any mineralogical alterations in the construction materials, but it did generate an increase in chloride, nitrate and sulphate contents of the granite and a slight darkening of this material. In the most recent study, the only deleterious effect observed was a perceptible increase in lightness and reduction in the yellow component after the application of Thymus zygis Loefl. ex L. essential oil to granite.

The development of five phases based on Design Thinking (Empathize, Define, Ideate, Prototype, Evaluate) is presented in order to design a methodology integrating Reliability Centered Maintenance (RCM), World Class Manufacturing (WCM) and Lean Manufacturing tools, which allows generating a maintenance plan applicable in wire drawing processes, particularly in a Mexican company in the steel sector. In the "Empathize, Define and Devise" phases, the main problems in processes of the steel sector of Colombian companies and one in Mexico are identified, proposing solution alternatives based on needs and realities, considering the expected Key Performance Indicators (KPI). Subsequently, tools are selected according to the problem and the alternative solutions proposed. In the "Prototype and Evaluate" phases, the selected tools are integrated, generating a maintenance plan which is described and represented by a diagram as an evaluation of the designed methodology. [ABSTRACT FROM AUTHOR]

With increasing application of Fused Deposition Modeling (FDM), it is important to develop a reliability model and maintenance plan strategies for FDM machines. This paper studies the impact of machine component degradations and process parameter deviations on the FDM system reliability. The contribution of this paper is to integrate both FDM machine reliability and process-dependent product quality in a system reliability model. The machine reliability is determined by component degradations. All component failures are subject to exponential distributions with time-dependent failure rates. The process-dependent product quality is used to quantify the impact of process parameters on product quality. The Flashforge Guider fabrication machine is used as a case study. The reliability model of this machine is analyzed during its operation time. Based on the reliability model, the optimal maintenance plans using multi-objective optimization method are obtained. The minimum life cycle cost and cumulative failure probability functions are taken as optimization objectives. Comparisons among the optimal maintenance plans and the solution without preventative maintenance (PM) are investigated. The implementation of PM improves the system reliability and reduces the life cycle cost. Through the Pareto frontiers, the trade-off is obtained between life cycle cost and cumulative failure probability functions. Numerical results for the optimization of FDM system operations are provided. [ABSTRACT FROM AUTHOR]

In accordance with the concept of "RCM - Reliability Centered Maintenance", the paper presents a study on the possibility to improve maintenance plan for the most frequently replaced parts from a sample of cars, followed during the operation period: front/rear brake pads. After analyzing the reliability of these components, it is found that there is the possibility of introducing the obligatory replacement operation of brake pads who have reached the third technical inspection without being replaced, sacrificing resource exploitation of them, but improving the benefits for the client - the automotive availability.