Classification of Construction Methods of Arch Bridges

2019 ◽  
pp. 698-705
Author(s):  
Manyop Han ◽  
Byungsuk Kim
Keyword(s):  
Construction Methods ◽  
Arch Bridges

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.

Continuous Arch Bridges Over Lake Tisza, Hungary

2021 ◽  
Author(s):  
Gábor Pál ◽  
Attila Dési ◽  
András Kemenczés
Keyword(s):  
Natural Environment ◽  
Construction Process ◽  
Tourist Destination ◽  
Unesco World Heritage ◽  
Continuous Structure ◽  
Truss Bridges ◽  
Total Length ◽  
Construction Methods ◽  
Tisza River ◽  
Arch Bridges

<p>The design and construction process of the unique cycling bridges at Lake Tisza are presented in this article. The 4 new bridges are parts of the closing segment of the cycle route around the artificial reservoir, which is a popular tourist destination in Hungary, and part of the UNESCO World Heritage. The proximity of the natural environment motivated the use of organic, flowing shapes.</p><p>The unique Eger- and Szomorka bridges are independent continuous half-through arch bridges, 8 spans with a total length of 308.46m, and 3 spans with a total length of 86.30 m, respectively. The bridge over River Tisza is a 5 span bridge with a total length of 279.47 m, which is placed on the extended piers of the existing roadway bridge. It consists of 2 deck truss bridges on the side-spans and 3 network arch bridges in the mid- spans. A 5.70 m span bascule bridge over one of the draining canals of the lake was also accomplished as part of the project.</p><p>The Eger and Szomorka bridges are internationally unique due to the fact that the Designers have dreamed a continuous sinusoid wave on the supports; which, by twirling under and above the deck, results in a continuous structure. The successful construction of the Tisza River Bridge also required some special and unprecedented construction methods.</p>

Additive Manufacturing: A Layered Taxonomy and Classification for Material Engineering Process

2021 ◽  
Vol 1045 ◽  
pp. 157-178
Author(s):  
Onuchukwu Godwin Chike ◽  
Norhayati Binti Ahmad ◽  
Uday M. Basheer Al-Naib
Keyword(s):  
Additive Manufacturing ◽  
State Of The Art ◽  
Focal Point ◽  
Engineering Process ◽  
Energy Depletion ◽  
Conventional Steel ◽  
Material Engineering ◽  
Manufacturing Operations ◽  
Construction Methods

Material engineers continuously make every effort for the evolution of novel and prevailing production performances to supply our biosphere with resource-proficient, economical, and hygienic substances with superior package operation. The mitigation of energy depletion and gas releases as an utmost significance worldwide is a renowned datum; which also needs the improvement of delicate substances employing budget-proficient and ecologically pleasant methods. Consequently, copious exploration has been aimed in the study of methods retaining a potential to wrestle these widespread essentials. Material engineering processes have advanced as a feasible substitute for conventional steel fragment construction methods. CE has experienced an extraordinary advancement throughout the previous three decades. It was originally utilised uniquely as a state-of-the-art reserve of the paradigm. Referable to the expertise development which permits merging countless engineering procedures for the output of a modified portion that employed intricate configurations, CE expertise has got cumulative responsiveness. As such, this article intends to furnish a comprehensive appraisal of chemical fabrication progressions for steel substance evolution utilised in different applications. The inspection encompasses the current advancement of CE know-hows, a detailed taxonomy and classification of manufacturing operations. The focal point of the upcoming perspective of CE in substance investigation and application is further deliberated

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

2020 ◽  
Author(s):  
João Batista ◽  
Ana Cabral ◽  
Maria 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 Remote Sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs Feature Construction by evolving hyper-features from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyper-feature from satellite bands to improve the classification of land cover types. We add the evolved hyper-features 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 hyper-features 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 hyper-features in the binary classification problems with those created by other Feature Construction methods like FFX and EFS.

scholarly journals Improving Land Cover Classification Using Genetic Programming

2020 ◽  
Author(s):  
João Batista ◽  
Ana Cabral ◽  
Maria Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Binary Classification ◽  
Multiclass Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models ◽  
Burnt Areas

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 Remote Sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs Feature Construction by evolving hyper-features from the original ones. In this work, we use the M3GP algorithm on several satellite images over different countries to perform binary classification of burnt areas and multiclass classification of land cover types. We add the evolved hyper-features 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 the multiclass classification datasets, with no significant effect on the binary classification ones. We show that adding the M3GP hyper-features 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 hyper-features in the binary classification problems with those created by other Feature Construction methods like FFX and EFS.

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

2020 ◽  
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 Remote Sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs Feature Construction by evolving hyper-features from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyper-feature from satellite bands to improve the classification of land cover types. We add the evolved hyper-features 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 hyper-features 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 hyper-features in the binary classification problems with those created by other Feature Construction methods like FFX and EFS.

scholarly journals A Study on the Structural Classification of Historical Stone Arch Bridges

1990 ◽  
Vol 10 ◽  
pp. 41-52
Author(s):  
Shunsuke BABA ◽  
Kohki NINOMIYA ◽  
Motohide OGAWA
Keyword(s):  
Structural Classification ◽  
Arch Bridges

Note on the spectral classification of carbon stars in the photographic infrared region

1966 ◽  
Vol 24 ◽  
pp. 21-23
Author(s):  
Y. Fujita
Keyword(s):  
Infrared Region ◽  
Spectral Classification ◽  
Carbon Stars

We have investigated the spectrograms (dispersion: 8Å/mm) in the photographic infrared region fromλ7500 toλ9000 of some carbon stars obtained by the coudé spectrograph of the 74-inch reflector attached to the Okayama Astrophysical Observatory. The names of the stars investigated are listed in Table 1.

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