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STRUCTURAL BEHAVIOR OF REINFORCED CONCRETE BEAMS MADE WITH NATURAL LIGHTWEIGHT AGGREGATES

Published In: 2ND INTERNATIONAL CONFERENCE ON ADVANCES IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING
Author(s): ABDELHAMID CHARIF , M. JAMAL SHANNAG , SALMAN AL NASSER

Abstract: This paper investigates the structural behavior of reinforced concrete beams made with locally available natural lightweight scoria aggregates. A series of 4 full scale structural lightweight reinforced concrete beams 2.9 m long, with a rectangular cross-section of 250×300 mm were designed, cast and tested under 4 points bending test. The effect of varying the compressive strength from 25 to 45 MPa on the load-deflection and moment-curvature responses of under reinforced beams with a reinforcement ratio of 0.25 of the balanced steel ratio was studied. Data presented include the load-deflection and moment curvature responses of the beams tested. The investigation revealed that the flexural behavior of the lightweight reinforced concrete beams was comparable to that of normal weight reinforced concrete beams with relatively larger deflections and curvatures. Test results showed a significant reduction in ductility of lightweight reinforced concrete beams with the increase in compressive

  • Publication Date: 26-Oct-2014
  • DOI: 10.15224/978-1-63248-030-9-37
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SUPPORT VECTOR MACHINE WITH NON-DOMINATED SORTING GENETIC ALGORITHM FOR THE MONTHLY INFLOW PREDICTION IN HYDROPOWER RESERVOIR

Published In: 2ND INTERNATIONAL CONFERENCE ON ADVANCES IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING
Author(s): MAHYAR ABOUTALEBI , OMID BOZORGHADDAD

Abstract: In this paper a novel tool, support vector machine (SVM) based on Non-dominated sorting genetic algorithm (NSGAII), is proposed for prediction of the monthly inflow stream in the hydropower reservoir system. The two objectives which are considered in NSGAII are minimizing the error of the prediction by SVM and minimizing the number of variables which are selected for SVM as the input variables. The statistical indicator which is considered for the evaluation of the error is root mean square error (RMSE) and the hydropower reservoir of Karoon-4 which is located in Iran is considered as the case study. In this optimization problem, the decision variables of NSGAII have two parts. The First part is the names of the input variables as predictors and the other part is the values of the SVM parameters. In order to create the data base of SVM, the input variables (monthly inflow and monthly precipitation) in the previous periods and monthly inflow of reservoir in the current period as the tar

  • Publication Date: 26-Oct-2014
  • DOI: 10.15224/978-1-63248-030-9-38
  • Views: 0
  • Downloads: 0