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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: The purpose of this study is to develop a decision support framework to select critical success factors (CSFs) and benefits for the implementation of BIM in the public works sector. Design/Methodology/Approach: The research methodology integrates multiple fuzzy multicriteria decision making (MCDM) approaches to propose a composite framework of CSFs and benefits using qualitative and quantitative approaches, with the application of two semi-structured questionnaires with CSFs and benefits identified in previous studies, through a systematic literature review (SLR). Results: The study identified 42 CSFs and 31 benefits for the implementation of BIM for public works, and the relative importance of the identified factors and benefits was measured according to the four main BIM perspectives: legal, people, process, and technology. The result of the applied MCDMs showed the relationship between the factors and benefits of the four most influential BIM perspectives according to the proposed framework. The most influential CSFs and benefits were: BIM adoption requires individual and group motivation in the organization, Establishment of BIM and Lean construction standards, codes, rules, and regulations, Financial support from the government to establish BIM system, Availability of quality, schedule, and cost information during construction with BIM Accuracy and reliability of documents and data and BIM provides knowledge sharing among stakeholders, Workflow, productivity, and efficiency are affected by the transition to BIM, BIM technology reduces cost and time, Conflict detection, integration, coordination, and design validation, and Creating more efficient projects with stakeholder involvement, coordination, and oversight. Limitations of the investigation: The research sample consisted exclusively of Brazilian professionals and academics with experience in the implementation of BIM in public works. Practical implications: The proposed framework can be applied in implementation in public construction projects that aims to implement BIM considering the selection of critical success factors and benefits. Originality/Value: BIM methodology is widely used in the construction industry. However, there are few studies on CSFs and the benefits of BIM for the public sector, and there still lack of implementation frameworks proposal with empirical evidence. [ABSTRACT FROM AUTHOR]

This paper applies a methodology for interdisciplinary Facilities Management (FM) by alingment between Building Information Modeling (BIM) and Lean. Initially, the literature review of BIM, FM and Lean principles. Afterwards, the research applies the Digital Obeya Room for improved FM and describes its application on a real-world case study. Lastly, the work presents a survey with specialists to assess the relevance of each BIM-Lean concepts and correlate their perceptions with the empirical results. The main collaborations of this work are: a conceptual framework that relates the PDCA (Plan-Do-Check-Act) cycle with BIM-Lean approaches; the identification of the most relevant BIM functionalies and Lean principles; and the real-world application of the framework procedures on FM. [ABSTRACT FROM AUTHOR]

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Over the past few years, issues of sustainability are gaining greater prominence among organizations and their stakeholders around the world and with it the effective measurement of environmental performance has been a challenge for sustainable transition. The purpose of the article is to analyse, through the the perception of market experts and researchers, what sustainable performance measures have stronger positive correlation to achieve organizational sustainability in order to help employees in making decisions that reduce the consumption of resources and that create value throughout its chain. The research is of an exploratory and descriptive nature, with qualitative and quantitative method and has a deductive logic. It aims to help professionals and academics who want to start the measurement and continuous improvement of economic, environmental, social, governance and technical performance of their organizations. Finally, the analyzes allow direct efforts to sustainable measures considered most important, allowing the transition of the organization for sustainability. [ABSTRACT FROM AUTHOR]

Purpose - This article aims to provide the guidelines to assess the risk management maturity (RMM) of construction projects. Design/methodology/approach - This article presents a literature review of the main risk management models, which aims to support a survey research to define, according to experts, the market preference and knowledge of RMM models. Findings - We pointed out in this article a set of guidelines to direct the maturity analysis of RM in the Construction Industry and proposed a model matrix to help companies to evaluate the maturity of the RM of a whole project or each activity separately. Research limitations/Implications - Limitations of this article include the search options originally defined by the researches in literature review and concepts and measures chosen to be evaluated by experts in the survey. Other approaches could explore the implementation of a RMM model considering quantitative measures in others areas. Practical Implications - The article provide to construction companies a way to measure their risk management capacity against four standard levels of maturity, allowing them to make benchmarking and to improve and increase their ability to manage risk. Originality/value - We highlighted to professionals and academics which factors were essential seeking to improve the risk management continuously. [ABSTRACT FROM AUTHOR]