Fraunhofer Chalmers Research Centre for Industrial Mathematics
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Mechanical deformation of a wooden panel due to a varying climate – Numerical simulations
No full textABSTRACT
Wood has been used for thousands of years as interior decoration. It is therefore
logical that many objects made of this material are found in a cultural heritage
context. A large amount of historical art pieces, such as decorated wooden panels,
have been stored in historical buildings. Heating systems are not common in historical
buildings, not even after renovations. In some occasions it is not allowed to apply an
HVAC-system because of the cultural heritage value of the building itself. Since these
buildings are often not climate controlled it is important to know how the indoor
climate is affecting the historical art.
The aim of this study is to understand the cause-effect relationship between a
fluctuating climate and wooden panels. Wood is a hygroscopic material, meaning that
it will absorb and desorb moisture during changes in relative humidity and
temperature of the ambient air. Changing moisture content in the wood comes with
expansion or contraction of the wood cells, which could cause deformations of the
panel.
A simulation model is developed to compute the state of deformation, which is
expressed in curvatures. Two types of simulations are made; simulations of moisture
transport in wooden panels subjected to a fluctuating climate during a year; and
isothermal simulations for mechanical deformation of the panel due to changes in the
vapour concentration at the surface. Before running the simulations, a literature study
was done to get a greater view of the possible outcome of the simulations. With the
numerical tool, moisture transport was analysed first and afterwards the curvature of
the panel was computed. Six indoor climates were taken as an input, which gave six
simulations of the curvature for one panel. The model confirms that deformation of
the panel is in line with fluctuations of relative humidity of the ambient air. By
increasing the surface vapour resistance factor of the panel, the range of the curvature
decreases.
The calculations show many repetitive and alternating cycles of curvature. However,
it is not known how many and how big these cycles need to be before cracks occur.
Therefore a proposal for future research is to develop a scale that represents damage
against deformation.
Keywords: Wood, relative humidity, moisture content, temperature, moisture
transport, moisture transport coefficients, expansion coefficients, deformation, pores,
paintings, historical building
Running with machine learning - A study on running technique using foot placed IMUs and multinomial logistic regression
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FE-analysis of a deep excavation in soft soil - A case study of the Marieholm tunnel project
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Defined by nature - an exploration of how a hotel can be designed with the experience of nature in focus
No full textDrivers and constraints of Corporate Social Responsibility in Swedish construction organizations
No full textCorporate Social Responsibility (CSR) is a concept that has evolved in the last decades. It refers to the debate concerning businesses broader responsibility towards society other than return of investment. Historically in Sweden, private and public sectors have not been interacting in social responsibility issues because the political climate has been that such public responsibilities belong to the state. Today the political climate is different and thus there is an ongoing change in the business world. Meanwhile, the political situation is more unstable than ever and the progress of individuals philanthropic and moral agenda have gained momentum, demanding a shared social responsibility among the community.
The aim of this study is to answer the two research questions, how is CSR interpreted by employees in construction organizations? And, what are the drivers and constraints of CSR in the construction sector? A qualitative research study consisting of 14 interviews has been conducted. In total six companies of different sizes, varying from medium-sized to very large have participated in the research.
The empirical data indicates that CSR is a broad and complex concept, which has many different interpretations among the employees. People working with CSR issues in their role at the company have a definition of CSR that correlates with what is written in the theory, meanwhile employees with other functions often refers CSR to the environmental aspect of sustainability and technical solutions. Sponsorship and recruitment-programs seems to be two CSR activities that are well integrated in the construction sector.
The identification of several drivers and constraints, which are organized into three different CSR levels have been conducted. The first level related to individuals and their commitment. The second level is linked to the organizations and their strategies. The third level refers to the public and stakeholders demands related to the external environment outside the organization. The study indicates that in the construction sector all three levels need to be considered, interact and have a proactive approach to push forward businesses CSR behavior. Moreover it is suggested that clients demands and procurement conditions, stakeholder pressure and external events, employees and management's commitment, employees requests and, the organization's CSR strategy are factors that are important in
2 the development of CSR. The study highlights the importance of management to focus on employee people with CSR knowledge and give them mandate, for the progress of CSR in the industry. However, lack of resources in terms of both money, time, interest and knowledge, lack of clients CSR demands, CSR complexity and difficulty in measuring CSR effects have shown to be constraints to the development of CSR
Deep Learning for Drug Discovery, Property Prediction with Neural Networks on Raw Molecular Graphs
No full textThe lengthy and expensive process of developing new medicines is a driving force in the development of machine learning on molecules. Classical approaches involve extensive work to select the right chemical descriptors to use as input data. The scope of this thesis is neural network architectures learning directly on raw molecular graphs, thereby eliminating the feature engineering step. The starting point of experimentation is a reimplementation of the previously proposed message passing neural networks framework for learning on graphs, analogous to convolutional neural networks in how it updates node hidden states through aggregation of neighbourhoods. Three modifications of models in this framework are proposed and evaluated: employment of a recently introduced activation function, a neighbourhood aggregation step involving weighted averaging and a message passing model incorporating hidden states in the graph’s directed edges instead of its nodes. The resulting models are hyperparameter optimized using a parallelized variant of Bayesian optimization. Comparison to literature benchmarks for machine learning on molecules shows that the new models are competitive with state-of-the-art, outperforming it on some datasets
