Determination of Electrical Resistivity of Soil Based on Thermal Resistivity Using RVM and MPMR

This article adopts Relevance Vector Machine (RVM) and Minimax Probability Machine Regression (MPMR) for prediction Soil Electrical Resistivity(RE) of soil. RVM uses an improper hierarchical prior. It optimizes over hyperparameters. MPMR is a probabilistic model. Two models (MODEL I and MODEL II) have been adopted. Percentage sum of the gravel and sand size fractions (F) and Soil Thermal Resistivity(RT) has been takes as inputs in MODEL I. MODEL II uses F,RT and saturation of soils(S) as input variables. The results of RVM and MPMR have  been compared with the Artificial Neural Network (ANN). The developed RVM and MPMR proves his ability for prediction of RE of soil

Spatially varying ground motion effects on seismic response of adjacent structures considering soil-structure interaction

Spatial variation of seismic ground motions is caused by incoherence effect, wave passage, and local site conditions. This study focuses on the effects of spatial variation of earthquake ground motion on the responses of adjacent reinforced concrete (RC) frame structures. The adjacent buildings are modeled considering soil-structure interaction (SSI) so that the buildings can be interacted with each other under uniform and non-uniform ground motions. Three different site classes are used to model the soil layers of SSI system. Based on fast Fourier transformation (FFT), spatially correlated non-uniform ground motions are generated compatible with known power spectrum density function (PSDF) at different locations. Numerical analyses are carried out to investigate the displacement responses and the absolute maximum base shear forces of adjacent structures subjected to spatially varying ground motions. The results are presented in terms of related parameters affecting the structural response using three different types of soil site classes. The responses of adjacent structures have changed remarkably due to spatial variation of ground motions. The effect can be significant on rock site rather than clay site

Minimax probability machine regression and extreme learning machine applied to compression index of marine clay

2350-2356This article uses Minimax Probability Machine Regression (MPMR) and Extreme Learning Machine (ELM) for determination of Compression Index (Cc) of marine clay. MPMR is developed in a probabilistic framework. It maximizes the minimum probability of future predictions being within some bound of the true regression function. ELM is the advanced learning algorithm of single-hidden layer feed forward neural network.  Natural moisture content (wn), liquid limit (LL), void ratio (e) and plasticity index (PI) have been used as inputs of MPMR and ELM. The output of MPMR and ELM is Cc. The results of MPMR and ELM have been compared with the regression models. This study gives a powerful tool based on the developed MPMR for determination of Cc of marine clay.

Investigation of the Performance of Two Passive Controllers in Mitigating the Rotational Response of Irregular Buildings

Two passive vibration control devices (i.e., circle type tuned liquid damper (C-TLD) and a circle type tuned liquid column damper (C-TLCD)) were experimentally investigated for their performance when attached to the irregular building structure subjected to dynamic loads. The specific directions where the maximum response of the structure is expected were experimentally identified for x- and y-directions as well as for rotational direction. The power spectral density (PSD) was computed for the response of the structure based on the frequency of the first three modes and also water level changes in the device container by using fast Fourier transform (FFT). The performances of these two controllers regarding suppressing the structural vibration were compared for the seismic loads applied in an experimentally identified critical direction. The results show that these systems are effective in terms of mitigating the coupled lateral and torsional vibrations of a scaled three-story irregular model

De-noising of GPS structural monitoring observation error using wavelet analysis

In the process of the continuous monitoring of the structure's state properties such as static and dynamic responses using Global Positioning System (GPS), there are unavoidable errors in the observation data. These GPS errors and measurement noises have their disadvantages in the precise monitoring applications because these errors cover up the available signals that are needed. The current study aims to apply three methods, which are used widely to mitigate sensor observation errors. The three methods are based on wavelet analysis, namely principal component analysis method, wavelet compressed method, and the de-noised method. These methods are used to de-noise the GPS observation errors and to prove its performance using the GPS measurements which are collected from the short-time monitoring system designed for Mansoura Railway Bridge located in Egypt. The results have shown that GPS errors can effectively be removed, while the full-movement components of the structure can be extracted from the original signals using wavelet analysis

Effect of constitutive material models on seismic response of two-story reinforced concrete frame

This paper focuses on the finite element (FE) response sensitivity and reliability analyses considering smooth constitutive material models. A reinforced concrete frame is modeled for FE sensitivity analysis followed by direct differentiation method under both static and dynamic load cases. Later, the reliability analysis is performed to predict the seismic behavior of the frame. Displacement sensitivity discontinuities are observed along the pseudo-time axis using non-smooth concrete and reinforcing steel model under quasi-static loading. However, the smooth materials show continuity in response sensitivity at elastic to plastic transition points. The normalized sensitivity results are also used to measure the relative importance of the material parameters on the structural responses. In FE reliability analysis, the influence of smoothness behavior of reinforcing steel is carefully noticed. More efficient and reasonable reliability estimation can be achieved by using smooth material model compare with bilinear material constitutive model

Development of equation for determining the compression index of marine clay

1796-1802Compression index determination is one of the paramount task for the civil engineers, hence this paper target to predict the compression index of clay by adopting Genetic Programming (GP). The data was amassed by the soil property from consolidations tests on undisturbed samples at west coastal areas in Korea. The optimized equations for determining the compression index can be developed with the help of the adopted intelligent technique as it was based on the dry density (gd), initial void ratio (e), liquid limit (wl), natural water content (wn) and plasticity index (PI). The efficiency of the adopted model was compared with the existing empirical relations and GP has proved its betterment. This in turn leads to the reduction in executing the experiments in determining the compression index. These equations are very much suitable for the primitive computing on the settlement of the ground. Various statistical analyses were utilized for assessing the capability of the adopted technique