A STUDY ON THE EVALUATION STRUCTURE OF MATERIALS AND CONSTRUCTION METHODS FOR URBAN SHRINES AND TEMPLES

2001 ◽  
Vol 66 (540) ◽  
pp. 133-139
Author(s):  
Ko MURAOKA ◽  
Shinichi SUGAHARA
Keyword(s):  
Construction Methods

Proposal for Refugee Housing in Southeast Europe Utilizing Regionally Appropriate, Sustainable, and Affordable Construction Methods

2020 ◽  
Vol 22 ◽  
Author(s):  
Hana Cicevic ◽  
Sarah Gamble
Keyword(s):  
Architectural Design ◽  
Housing Demand ◽  
Situational Factors ◽  
Housing Unit ◽  
Southeast Europe ◽  
Construction Methods ◽  
Permanent Housing ◽  
Emergency Housing ◽  
Communal Housing ◽  
Construction Model

This research focuses on the emergency-housing demand caused by the on-going refugee crisis in Southeastern Europe. The research and proposed solutions focus on Northern Serbia, as this region generally lacks permanent housing solutions for accommodating the increasing influx of immigrants. The outcome of this research is an architectural proposal for the refugee housing unit designed particularly for the situational factors of this focus region.    The study identifies earth architecture as the primary building technique, due to its ability to satisfy a range of defined end-product goals.  These goals include: the sustainability of the material, quality and durability of the final product, skill level required for non-professional construction, final cost of material and execution, historical regional precedent, and opportunity for communal engagement of the immigrant population. The proposed architectural design uses earth-bag construction as the sub-method most suitable for this location and in keeping with the goals outlined above. The proposed housing unit is a singular component that could be duplicated to create larger communal housing communities.  A broad overview of possible solutions is included, followed by the development of the earth-bag construction option. The development of this proposal includes material studies, sketches, and an architectural model as representation tools. The outcomes of this research serve as a guideline, rather than a precise construction model, in creating much needed refugee housing communities in North Serbia.      

DESIGN-BUILD: AN EFFECTIVE APPROACH FOR ARCHITECTURE STUDIO EDUCATION

2018 ◽  
Vol 12 (2) ◽  
pp. 146 ◽  
Author(s):  
Nabil Mohareb ◽  
Sara Maassarani
Keyword(s):  
Material Selection ◽  
Environmental Constraints ◽  
Design Project ◽  
Construction Methods ◽  
Physical Scale ◽  
Design Build ◽  
Simulation Results ◽  
Education Process ◽  
Real Target

Current architecture studios are missing an important phase in the education process, which is constructing the students’ conceptual ideas on a real physical scale. The design-build approach enables the students to test their ideas, theories, material selection, construction methods, environmental constraints, simulation results, level of space functionality and other important aspects when used by real target clients in an existing context. This paper aims to highlight the importance of using the design-build method through discussing a design project case study carried out by the Masters of Architecture design programme students at Beirut Arab University, who have built prototype units for refugees on a 1:1 scale.

scholarly journals An All-Batch Loss for Constructing Prediction Intervals

2021 ◽  
Vol 11 (4) ◽  
pp. 1728
Author(s):  
Hua Zhong ◽  
Li Xu
Keyword(s):  
Gradient Descent ◽  
State Of The Art ◽  
Prediction Interval ◽  
Feedforward Neural Networks ◽  
Important Research ◽  
Likelihood Principle ◽  
High Quality ◽  
Construction Methods ◽  
Important Research Topic ◽  
Benchmark Datasets

The prediction interval (PI) is an important research topic in reliability analyses and decision support systems. Data size and computation costs are two of the issues which may hamper the construction of PIs. This paper proposes an all-batch (AB) loss function for constructing high quality PIs. Taking the full advantage of the likelihood principle, the proposed loss makes it possible to train PI generation models using the gradient descent (GD) method for both small and large batches of samples. With the structure of dual feedforward neural networks (FNNs), a high-quality PI generation framework is introduced, which can be adapted to a variety of problems including regression analysis. Numerical experiments were conducted on the benchmark datasets; the results show that higher-quality PIs were achieved using the proposed scheme. Its reliability and stability were also verified in comparison with various state-of-the-art PI construction methods.

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

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