BIM and Knowledge Based Risk Management System

The use of Building Information Modelling (BIM) for construction project risk management has become a growing research trend. However, it was observed that BIM-based risk management has not been widely used in practice and two important gaps leading to this problem are: 1) very few theories exist that can explain how BIM can be aligned with traditional techniques and integrated into existing processes for project risk management; and 2) current BIM solutions have very limited support on risk communication and information management during the project development process. To overcome these limitations, this research proposes a new approach that two traditional risk management techniques, Risk Breakdown Structure (RBS) and Case-based Reasoning (CBR), can be integrated into BIM-based platforms and an active linkage between the risk information and BIM can be established to support the project lifecycle. The core motivations behind the proposed solution are: 1) a tailored RBS could be used as a knowledge-based approach to classify, store and manage the information of a risk database in a proper structure and risk information in RBS could be linked to BIM for review, visualisation and communication; and 2) knowledge and experience stored in past risk reports could contribute to avoiding similar risks in new situations and the most relevant cases can be linked to BIM to support decision making during the project lifecycle. The scope of this research is limited to bridge projects; however, the basic methods and principles could be also applied to other types of projects. This research is in three phases. In the first stage, this research analysed the conceptual separation of BIM and the linkage rules between different types of risk and BIM. Specifically, an integrated bridge information model was divided into four Level of Contents (LOCs) and six technical systems based on the analysis of the Industry Foundation Classes (IFC) specification, a critical review of previous studies and the author’s project experience. Then a knowledge-based risk database was developed through an extensive collection of risk data, a process of data mining, and further assessment and translation of the data. Built on the risk database, a tailored RBS was developed to categorise and manage this risk information and a set of linkage rules between the tailored RBS and the four LOCs and six technical systems of BIM was established. Secondly, to further implement the linkage rules, a novel method to link BIM, RBS, and Work Breakdown Structure (WBS) to be a risk management system was developed. A prototype system was created based on Navisworks and the Microsoft SQL Server to support the implementation of the proposed approach. The system allows not only the storage of risk information in a central database but also to link the related risk information in the BIM model for review, visualisation and simulation. Thirdly, to facilitate the use of previous knowledge and experience for BIM-based risk management, the research proposed an approach of combining the use of two Natural Language Processing (NLP) techniques, i.e. Vector Space Model (VSM) and semantic query expansion, and outlined a new framework for the risk case retrieval system. A prototype was developed using the Python programming language to support the implementation of the proposed method. Preliminary testing results show that the proposed system is capable of retrieving relevant cases automatically and to return, for example, the top 10 similar cases. The main contribution of this research is the approach of integrating RBS and CBR into BIM through active linkages. The practical significance of this research is that the proposed approach enables the development of BIM-based risk management software to improve the risk identification, analysis, and information management during the project development process. This research provides evidence that traditional techniques can be aligned with BIM for risk management. One significant advantage of the proposed method is to combine the benefits of both traditional techniques and BIM for lifecycle project risk management and have the minimum disruption to the existing working processes

Analysis of Multivariat

Salah satu e-book terkait dengan materi analisis multivaria

xxAI - Beyond Explainable AI

This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.https://digitalcommons.unomaha.edu/isqafacbooks/1000/thumbnail.jp

xxAI - Beyond Explainable AI

This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science

xxAI - Beyond Explainable AI

This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science

Progress in Landslide Research and Technology, Volume 1 Issue 2, 2022

This open access book provides an overview of the progress in landslide research and technology and is part of a book series of the International Consortium on Landslides (ICL). It gives an overview of recent progress in landslide research and technology for practical applications and the benefit for the society contributing to understanding and reducing landslide disaster risk

In vitro expanded human CD4+CD25+ regulatory T cells suppress effector T cell proliferation.

Regulatory T cells (Tregs) have been shown to be critical in the balance between autoimmunity and tolerance and have been implicated in several human autoimmune diseases. However, the small number of Tregs in peripheral blood limits their therapeutic potential. Therefore, we developed a protocol that would allow for the expansion of Tregs while retaining their suppressive activity. We isolated CD4+CD25 hi cells from human peripheral blood and expanded them in vitro in the presence of anti-CD3 and anti-CD28 magnetic Xcyte Dynabeads and high concentrations of exogenous Interleukin (IL)-2. Tregs were effectively expanded up to 200-fold while maintaining surface expression of CD25 and other markers of Tregs: CD62L, HLA-DR, CCR6, and FOXP3. The expanded Tregs suppressed proliferation and cytokine secretion of responder PBMCs in co-cultures stimulated with anti-CD3 or alloantigen. Treg expansion is a critical first step before consideration of Tregs as a therapeutic intervention in patients with autoimmune or graft-versus-host disease