Book•
Foundation analysis and design
01 Jan 1968-
TL;DR: In this paper, Fondation de soutenagement et al. presented a reference record for Dimensionnement Reference Record created on 2004-09-07, modified on 2016-08-08.
Abstract: Keywords: Fondation ; Mur de soutenement ; Pieux ; Capacite portante ; Ancrage ; Dimensionnement Reference Record created on 2004-09-07, modified on 2016-08-08
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14 Jan 2020
TL;DR: The suggested new optimization algorithm including the synergy of AAGA and AGADS demonstrates improved results comparing with former AGA and GADS.
Abstract: The purpose of the paper is to present the method implemented for a global optimization of grillage-type pile foundations introducing two advanced metaheuristics: AAGA and AGADS. The suggested new optimization algorithm including the synergy of AAGA and AGADS demonstrates improved results comparing with former AGA and GADS. Compromise objective function to be minimized involves the maximum reactive force in piles and maximum bending moment in the connecting beams. The feasibility of a simple weighting technique for the objective function is proved by numerical investigation of objective function domain for several different topologies of foundations. Sizing problem of connecting beams is solved together with the optimization problem. The original finite element program was employed for solution of direct problem.
2 citations
Cites background or methods from "Foundation analysis and design"
...A comprehensive technical data on grillages can be found in [6, 15, 3]....
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...The simple method of weighting coefficients [6] for multi-objective optimization is valid ultimately in the case of the convexity of the feasible design space....
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TL;DR: This work proposes a mathematical model based on artificial intelligence focused on Artificial Neural Network (ANN) learning capable of predicting the load capacity for driven piles and finds that the proposed neural model presented correlation with field values above 90%, reaching 96% in the best result.
Abstract: In geotechnics, several models, empirical or not, have been proposed for the calculation of load capacity in deep foundations. These models run mainly through physical assumptions and construction of approximations by mathematical models. Artificial Neural Networks (ANN), in addition to other applications, are excellent computational mechanisms that, based on biological neural learning, can perform predictions and approximations of functions. In this work, an application of artificial neural networks is presented. The objective here is to propose a mathematical model based on artificial intelligence focused on Artificial Neural Network (ANN) learning capable of predicting the load capacity for driven piles. The results obtained through the neural network were compared with actual values of load capacities obtained in the field through load tests. For this quantitative comparison, the following metrics have been chosen: Pearson correlation coefficient and mean squared error. The database used to carry out the project consisted of 233 load tests, carried out in diverse cities and different countries, for which load capacity, hammer weight, hammer drop height, pile length, pile diameter and pile penetration per blow values were available. These values have been used as input values in a multilayer perceptron neural network to estimate the load capacities of the respective piles. It has been found that the proposed neural model presented, in general, correlation with field values above 90%, reaching 96% in the best result.
2 citations
Journal Article•
TL;DR: In this paper, the authors developed a numerical method for evaluating ground movements while a precast concrete pile is being driven and obtained a comprehensive load-displacement curve for a driven pile.
Abstract: This study developed a numerical method for evaluating ground movements while a pile is being driven and obtained a comprehensive load-displacement curve for a driven pile. The tested pile exhibited a larger scale than those tested in previous studies, and was penetrated into a considerable depth of the stratum. Numerical results revealed that both the ground movement and residual force caused by the pile driving was able to be analyzed using the proposed numerical procedure. Although the load applied to the tested piles differed from that determined by numerically analyzing incremental pile displacement, the load-displacement behavior of the piles calculated using the proposed method was consistent with that measured in pile-load tests before the piles were stressed to their peak loads. This study presents a series of helpful figures to support the design of driven precast concrete piles based on the results of numerical studies and field tests.
2 citations
TL;DR: In this paper, a coiled tube single pass counter flow heat exchanger was designed and fabricated using locally available materials and its capacity to transmit heat to water to make steam was tested.
Abstract: A coiled tube single pass counter flow heat exchanger was designed and fabricated using locally available materials and its capacity to transmit heat to water to make steam was tested. The heat exchanger was part of components used in solar thermal power production using a parabolic trough solar concentrator. The design of the heat exchanger storage system was done using Auto CAD 2010 software. Higher temperatures of steam were realized when the heat exchanger was used as a steam storage system. The heat transfer fluids used were locally available and they were water, sunflower oil, Rina oil, used engine oil, unused engine oil, 2 M sodium chloride salt solution, 4 M sodium chloride salt solution and 6 M sodium chloride solution. For each of the heat transfer fluids, heat exchanger operating points were obtained and it was operated between 1 Nm -2 to 1.0 Nm -2 of pressure. In the study the number of heat transfer units of the heat exchanger obtained was 0.61 and the thermal efficiency was found as 0.91. The average rates of heat transfer were 68.4 J for 6M sodium chloride solution, 62.8 J for 4 M sodium chloride solution, 57.9 J for used engine oil. Thermal conductivity of the salt solutions was better than for other heat transfer fluids although they were more corrosive on the system. Thermal efficiencies of the heat transfer fluids were 6 M sodium chloride solution; 0.89, for 4 M sodium chloride solution ; 0.84, for 2 M sodium chloride solution ; 0.80, water ; 0.78, sunflower; 0.69, Rina oil ; 0.66, unused engine oil ; 0.75 and used engine oil; 0.71. Storage of solar thermal energy will address the problem of low solar density and its variability. The heat exchanger storage system achieved a thermal capacity of 3.26 kJ at a maximum temperature of 249.4 o C and at a pressure of 7.2 Nm -2 . The use of heat exchangers in solar thermal storage will increase the overall efficiency of total
2 citations
Dissertation•
01 Jan 2012
2 citations