Multi-Modal Monitoring of Slip Along Frictional Discontinuities

Seismic wave transmission and digital image correlation (DIC) are employed to study slip processes along frictional discontinuities. A series of biaxial compression experiments are performed on gypsum specimens with non-homogeneous contact surfaces. The specimens are composed of two blocks with perfectly mated contact surfaces with a smooth surface with low frictional strength on the upper half and a rough surface with high frictional strength on the lower half. Compressional, P, and shear, S, wave pulses were transmitted through the discontinuity while digital images of the specimen surface were acquired during the test. A distinct peak in the amplitude of transmitted wave occurs prior to the peak shear strength and is considered a “precursor” to the failure. Precursors indicate that slip initiates from the smooth surface and extends to the rough surface as the shear load is increased. From the DIC data, slip is identified as a jump in the displacement field along the fracture that initiates from the smooth surface and propagates to the rough surface. Precursors are associated with an increase in the rate of slip across the discontinuity and are a measure of the reduction in the fracture shear stiffness.     

Flame Atomic Absorption Spectrometry Determination of Trace Amounts of Cadmium and Zinc in Water Samples after Preconcentration onto Modified Amberlite XAD‐4 Resin

In the present article, a procedure for the simultaneous separation and preconcentration of trace amounts of cadmium and zinc is proposed. It is based on the adsorption of cadmium and zinc ions onto a column of Amberlite XAD‐4 resin loaded with aluminon reagent. Cadmium and zinc ions are quantitatively retained on the column in the pH range from 6.5–7.5, at a flow rate of 2 mL min^–1. The cadmium and zinc ions are eluted with 5.0 mL of 5 mol L^–1 HNO₃ solution. Cadmium and zinc are measured by flame atomic absorption spectrometry (FAAS). In the present case, 0.1 μg of cadmium and 0.5 μg of zinc can be concentrated in the column from 1000 mL of aqueous sample, where their concentrations are as low as 0.1 and 0.5 ng mL^–1, respectively. The relative standard deviations, for seven replicated determinations of 1.0 μg mL^–1 of cadmium and zinc, are 1.2 and 1.1%, respectively. The detection limits for cadmium and zinc in the original solution are 0.02 and 0.11 ng mL^–1, respectively. The interference of a large number of anions and cations has been studied and the optimized conditions are utilized for the determination of trace amounts of cadmium and zinc in different environmental and standard samples.     

Optimum structural design of full-scale steel buildings using drift-tribe-charged system search

Earthquake Engineering and Engineering Vibration - In this paper, the potential of utilizing improved metaheuristic approaches in optimal design of building structures is concerned. In this regard,...     

Urmia lake water level fluctuation hydro informatics modeling using support vector machine and conjunction of wavelet and neural network

In this research, the simulation of Urmia Lake water level fluctuation by means of two models was applied. For this, Support Vector Machines (SVM), and Neural Wavelet Network (NWN) models that conjugated both the wavelet function and ANN, developed for simulating the Urmia Lake water level fluctuation. The yearly data of rainfall, temperature and discharge to the Urmia Lake and water level fluctuation were used. Urmia Lake is the biggest and the hyper saline lake in Iran. The outcome of the SVM based models are compared with the NWN. The results of SVM model performs better than NWN and offered a practical solution to the problem of water level fluctuation predictions. Analysis results showed that the optimal situation occurred with use of precipitation, temperature and discharge for all station and water level fluctuations at the lag time of one year (RMSEs) of 0.23, 0.41 m obtained by SVM, NWN, respectively, and SSEs of 0.43, 1.33 and R ² of 0.97, 0 obtained by SVM, NWN, respectively. The results of SVM model show better accuracy in comparison with the NWN model.