Determination of preconsolidation pressure with artificial neural network

“…Low values of RMSE, SDR and MAE satisfy the statistical evaluation of prediction for the validation [29,30]. Before the network was trained, the input and the output data had been normalized; the scale and shift factors which were used in every input and output were given in Table 2.…”

Section: Application Of Artificial Neural Networkmentioning

confidence: 99%

Determination of the apparent rate constants of the degradation of humic substances by ozonation and modeling of the removal of humic substances from the aqueous solutions with neural network

Oğuz

Tortum

Keskinler

2008

Journal of Hazardous Materials

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“…Geotechnical properties of soils are controlled by factors such as mineralogy; fabric; and pore water, and the interactions of these factors are difficult to establish solely by traditional statistical methods due to their interdependence (Yang and Rosenbaum, 2002). Based on the application of ANNs, methodologies have been developed for estimating several soil properties including the preconsolidation pressure (Celik and Tan, 2005), shear strength and stress history swell pressure (Erzin 2007 andNajjar et al, 1996a), compaction and permeability (Agrawal et al, 1994;Goh, 1995b), soil classification (Cal, 1995) and soil density (Goh, 1995b).…”

Section: In Geo-technical Engineering:-mentioning

confidence: 99%

Feasibility of Artificial Neural Network in Civil Engineering

Singh¹,

Bano²,

Ahmad³

2019

IJTSRD

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An Artificial neural network (ANN) is an information processing hypothesis that is stimulated by the way natural nervous system, such as brain, process information. The using of artificial neural network in civil engineering is getting more and more credit all over the world in last decades. This soft computing method has been shown to be very effective in the analysis and solution of civil engineering problems. It is defined as a body which works out the more and more complex problem through sequential algorithms. It is designed on the basis of artificial intelligence which is proficient of storing more and more information's. In this work, we have investigated the various architectures of ANN and their learning process. The artificial neural network-based method was widely applied to the civil engineering because of the strong non-linear relationship between known and unknown of the problems. They come with good modelling in areas where conventional approaches (finite elements, finite differences etc.) require large computing resources or time to solve problems. These includes to study the behaviour of building materials, structural identification and control problems, in geo-technical engineering like earthquake induced liquefaction potential, in heat transfer problems in civil engineering to improve air quality, in transportation engineering like identification of traffic problems to improve its flexibility , in construction technology and management to estimate the cost of buildings and in building services issues like analyzing the water distribution network etc. Researches reveals that the method is realistic and it will be fascinated for more civil engineering applications.

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Determination of preconsolidation pressure with artificial neural network

Cited by 38 publications

References 17 publications

Removal of Cu2+ from aqueous solution by adsorption in a fixed bed column and Neural Network Modelling

Removal of Cu2+ from aqueous solution by adsorption in a fixed bed column and Neural Network Modelling

Determination of the apparent rate constants of the degradation of humic substances by ozonation and modeling of the removal of humic substances from the aqueous solutions with neural network

Feasibility of Artificial Neural Network in Civil Engineering

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