5 research outputs found

    A SUSTAINABLE ALTERNATIVE TO ARCHITECTURAL MATERIALS: Mycelium-based Bio-Composites

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    ConCave Ph.D. Symposium 2020: Divergence in Architectural Research, March 5-6, 2020, Georgia Institute of Technology, Atlanta, GA.In the history of architecture, technologies adapted from other disciplines have created new paradigms for design and production. During the first Industrial Revolution, for instance, developments in mechanical and material engineering, and the introduction of wrought-iron, steel, and concrete, led to revolutionary changes in architecture. In the nineteenth and twentieth centuries, electrical engineering and electronics had a similar groundbreaking effect on architecture and design. It seems that regarding the necessities and problems that exist in the 21st century, such as dependency on fossil fuels for construction that lead to carbon emission, the abundance of solid and liquid waste and unjustifiable costs, another change in the paradigm of construction is required. One possible way to address these issues is to return to nature and take advantage of biomaterials. This research studies the integration of mycelium-based bio-composites into the field of architecture. Mycelium is the vegetative part of mushrooms by which they absorb nutrients from the soil. When treated, mycelium results in a foam-like composite material that is lightweight, and biodegradable. Over the past couple of years, designers started to use mycelium-based composites in several applications ranging from product design and furniture to building panels and masonry blocks. In this research, the aim is to explore novel methods to use mycelium-based bio-composites in temporary and/or low- rise constructions. The focus of the research is on enhancing the material properties by investigating the factors that affect the nature and growth of the cultivated mycelium-based bio-composites and exploring novel structural systems based on the constraints and affordances of mycelium-based bio-composites, using computational form- finding techniques, generative design and optimization methods. In this paper, the initial incentives for conducting the research and the proposed methodology are discussed

    Mycelium-Based Composite Graded Materials: Assessing the Effects of Time and Substrate Mixture on Mechanical Properties

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    Mycelium-based composites (MBC) are biodegradable, lightweight, and regenerative materials. Mycelium is the vegetative root of fungi through which they decompose organic matter. The proper treatment of the decomposition process results in MBC. MBC have been used in different industries to substitute common materials to address several challenges such as limited resources and large landfill waste after the lifecycle. One of the industries which started using this material is the architecture, engineering, and construction (AEC) industry. Therefore, scholars have made several efforts to introduce this material to the building industry. The cultivation process of MBC includes multiple parameters that affect the material properties of the outcome. In this paper, as a part of a larger research on defining a framework to use MBC as a structural material in the building industry, we defined different grades of MBC to address various functions. Furthermore, we tested the role of substrate mixture and the cultivation time on the mechanical behavior of the material. Our tests show a direct relationship between the density of the substrate and the mechanical strength. At the same time, there is a reverse relation between the cultivation time and the material mechanical performance

    Tilted Arch; Implementation of Additive Manufacturing and Bio-Welding of Mycelium-Based Composites

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    Bio-based materials have found their way to the design and fabrication in the architectural context in recent years. Fungi-based materials, especially mycelium-based composites, are a group of these materials of growing interest among scholars due to their light weight, compostable and regenerative features. However, after about a decade of introducing this material to the architectural community, the proper ways of design and fabrication with this material are still under investigation. In this paper, we tried to integrate the material properties of mycelium-based composites with computational design and digital fabrication methods to offer a promising method of construction. Regarding different characteristics of the material, we found additive manufacturing parallel to bio-welding is an appropriate fabrication method. To show the feasibility of the proposed method, we manufactured a small-scale prototype, a tilted arch, made of extruded biomass bound with bio-welding. The project is described in the paper

    Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction

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    Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS

    Mask-MCNet: instance segmentation in 3D point cloud of intra-oral scans

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    Accurate segmentation of teeth in dental imaging is a principal element in computer-aided design (CAD) in modern dentistry. In this paper, we present a new framework based on deep learning models for segmenting tooth instances in 3D point cloud data of an intra-oral scan (IOS). At high level, the proposed framework, called Mask-MCNet, has analogy to the Mask R-CNN, which gives high performance on 2D images. However, the proposed framework is designed for the challenging task of instance segmentation of point cloud data from surface meshes. By employing the Monte Carlo Convolutional Network (MCCNet), the Mask-MCNet distributes the information from the processed 3D surface points into the entire void space (e.g. inside the objects). Consequently, the model is able to localize each object instance by predicting its 3D bounding box and simultaneously segmenting all the points inside each box. The experiments show that our Mask-MCNet outperforms state-of-the-art for IOS segmentation by achieving 98% IoU score
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