Ultimate Shear Strength Prediction for Slender Reinforced Concrete Beams without Transverse Reinforcement Using Machine Learning Approach (Record no. 815198)

MARC details
000 -LEADER
fixed length control field 02310aab a2200217 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240527b20242024|||br||| |||| 00| 0 eng d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 0889-3241
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Ju Dong Lee
9 (RLIN) 882107
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Thomas H.-K. Kang
9 (RLIN) 882108
245 ## - TITLE STATEMENT
Title Ultimate Shear Strength Prediction for Slender Reinforced Concrete Beams without Transverse Reinforcement Using Machine Learning Approach
300 ## - PHYSICAL DESCRIPTION
Extent 87-98 p.
520 ## - SUMMARY, ETC.
Summary, etc. A great deal of attention has been applied recently to machine learning (ML) algorithms to solve difficult engineering problems in the field of structural engineering. Using borrowed features of ML algorithms (implemented), a solution to one of the most troublesome problems in concrete structures—namely, shear—is proposed. The understanding of shear failure in reinforced concrete (RC) structures has led to numerous laboratory investigations and analytical studies over the last century. Due to decades of efforts afforded by researchers, significant experimental shear test results have been created and archived. This data provides an opportune environment to implement ML techniques and evaluate model efficiency and accuracy. The focus of this paper is on ML modeling of the shear-transfer mechanism for slender RC beams without transverse reinforcement. Test results for 1149 RC beams were incorporated in the ML analysis for training (80%) and testing (20%) purposes. Prior to the ML analysis, a correlation coefficient analysis was conducted to determine if given design parameters affected shear strength. When compared to the data used, code-based shear equations provided with large safety margins gave reasonable predictions. Exponential-based Gaussian process regression (GPR) ML models yielded comparable predictions. Of the 19 ML models employed, most were considered as an effective strength predictive tools. These ML model predictions were compared to each other and with design provision shear equations.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine Learning
9 (RLIN) 845
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Prediction
9 (RLIN) 675736
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element RC Slender Beams
9 (RLIN) 882109
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Shear Database
9 (RLIN) 170979
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Shear Strength
773 0# - HOST ITEM ENTRY
International Standard Serial Number 08893241
Title ACI Structural Journal
Place, publisher, and date of publication Farmington Hills,MI, U.S.A : American Concrete Institute
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="DOI: 10.14359/51740246">DOI: 10.14359/51740246</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Suppress in OPAC No
Koha item type Articles
-- 14993
-- Mr. Muhammad Rafique Al Haj Rajab Ali (Late)
Holdings
Not for loan Home library Serial Enumeration / chronology Total Checkouts Date last seen Koha item type
  Periodical Section Vol.121, No.2 (March 2024)   27/05/2024 Articles
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