Orientation effect on ground motion measurement for Mexican subduction earthquakes

The existence of the principal directions of the ground motion based on Arias intensity is well-known. These principal directions do not necessarily coincide with the orientations of recording sensors or with the orientations along which the ground motion parameters such as the peak ground acceleration and the pseudo-spectral acceleration （PSA） are maximum. This is evidenced by the fact that the maximum PSA at different natural vibration periods for horizontal excitations do not correspond to the same orientation. A recent analysis carried out for California earthquake records suggests that an orientation-dependent ground motion measurement for horizontal excitations can be developed. The main objective of this study is to investigate and provide seismic ground motion measurements in the horizontal plane, including bidirectional horizontal ground motions, for Mexican interplate and inslab earthquake records. Extensive statistical analyses of PSA are conducted for the assessment. The analysis results suggest that similar to the case of California records, the average behavior of the ratio of the PSA to the maximum resulting PSA can be approximated by a quarter of an ellipse in one quadrant; and that the ratio can be considered to be independent of the value of the maximum resulting PSA, earthquake magnitude, earthquake distance and the focal depth. Sets of response ratios and attenuation relationships that can be used to represent a bidirectional horizontal ground motion measurement for Mexican interplate and inslab earthquakes were also developed.

Influence caused by initial tectonic stress on relative measurement of borehole groundstresinviscoelasticmedia

In this paper, we discussed the influence of the initial tectonic stress on the relative measurement value of the ground stress when a long cylindrical straingauge is used in the viscoelastic media. We simulate the whole system consisting of the stratum, cement ring and the straingauge cylinder with a three layer medium model and obtain the solution of radial displacement on the inner wall of the straingauge under the action of initial tectonic stress by using the correspondence principle. The result showes: 1) The influence of initial stress making of the radial displacement on the inner wall of the straingauge increases gradually with time at a gradually reduced rate and tends to a certain value eventually; 2) The principal stress with a magnitude of [C 0+C 1 exp (- ω 1t)+ C 2 exp(- ω 2t )] σ i0 in the opposite direction of initial stress will be superimposed to the measuring result of stress change without considering the initial stress. where C 0, C 1, C 2 and ω 1, ω 2 are related only to the mechanical property of the stratum, σ i0 (i=1, 2) are the two principal components of the initial tectonic stress. It is pointed out that the initial tectonic stress affects obviously on the measurement of the relative change of ground stress in the cases of soft strata and high initial stress.

Observations on the application of artificial neural network to predicting ground motion measures

Application of the artificial neural network （ANN） to predict pseudospectral acceleration or peak ground acceleration is explored in the study. The training of ANN model is carried out using feed-forward backpropagation method and about 600 records from 39 California earthquakes. The statistics of the residuals or modeling error for the trained ANN-based models are almost the same as those for the parametric ground motion prediction equations, derived through regression analysis; the residual or modeling error can be modeled as a normal variate. The similarity and differences between the predictions by these two approaches are shown. The trained ANN-based models, however, are not robust because the models with almost identical mean square errors do not always lead to the same predictions. This undesirable behaviour for predicting the ground motion measures has not been shown or discussed in the literature; the presented results, at least, serve to raise questions and caution on this problem. A practical approach to ameliorate this problem, perhaps, is to consider several trained ANN models, and to take the average of the predicted values from the trained ANN models as the predicted ground motion measure.

Correlation study between ground motion intensity measure parameters and deformation demands for bilinear SDOF systems

The correlation between ground motion intensity measures （IM） and single-degree-of-freedom （SDOF） deformation demands is described in this study. Peak ground acceleration （APG）, peak ground velocity （VPG）, peak ground displacement （DPG）, spectral acceleration at the first-mode period of vibration [As（T1）] and ratio of VPG to APG are used as IM parameters, and the correlation is characterized by correlation coefficients p. The numerical results obtained by nonlinear dynamic analyses have shown good correlation between As（T1） or VPG and deformation demands. Furthermore, the effect of As（T1） and VPG as IM on the dispersion of the mean value of deformation demands is also investigated for SDOF systems with three different periods T=0.3 s, 1.0 s, 3.0 s respectively.

An evaluation of soil moisture from AMSR-E over source area of the Yellow River, China

In this study,in-situ soil moisture measurements are used to evaluate the accuracy of three AMSR-E soil moisture prod ucts from NASA(National Aeronautics and Space Administration),JAXA(Japanese Aerospace Exploration Agency)and VUA(Vrije University Amsterdam and NASA)over Maqu County,Source Area of the Yellow River(SAYR),China.Re sults show that the VUA soil moisture product performs the best among the three AMSR-E soil moisture products in the study area,with a minimum RMSE(root mean square error)of 0.08(0.10)m3/m3 and smallest absolute error of 0.07(0.08)m3/m3 at the grassland area with ascending(descending)data.Therefore,the VUA soil moisture product is used to describe the spatial variation of soil moisture during the 2010 growing season over SAYR.The VUA soil moisture product shows that soil moisture presents a declining trend from east south(0.42 m3/m3)to west north(0.23 m3/m3),with good agreement with a general precipitation distribution.The center of SAYR presents extreme wetness(0.60 m3/m3)dur ing the whole study period,especially in July,while the head of SAYR presents a high level soil moisture(0.23 m3/m3)in July,August and September.

An improved ground motion intensity measure for super high-rise buildings

Ground motion intensity measure (IM) is an important part in performance-based seismic design. A reasonable and efficient IM can make the prediction of the structural seismic responses more accurate. Therefore, a more reasonable IM for super high-rise buildings is proposed in this paper. This IM takes into account the significant characteristic that higher-order vibration modes play important roles in the seismic response of super high-rise buildings, as well as the advantages of some existing IMs. The key parameter of the proposed IM is calibrated using a series of time-history analyses. The collapse simulations of two super high-rise buildings are used to discuss the suitability of the proposed IM and some other existing IMs. The results indicate that the proposed IM yields a smaller coefficient of variation for the critical collapse status than other existing IMs and performs well in reflecting the contribution of higher-order vibration modes to the structural response. Hence, the proposed IM is more applicable to seismic design for super high-rise buildings than other IMs.

Train wheel-rail force collaborative calibration based on GNN-LSTM

Accurate wheel-rail force data serves as the cornerstone for analyzing the wheel-rail relationship.However,achieving continuous and precise measurement of this force remains a significant challenge in the field.This article introduces a calibration algorithm for the wheel-rail force that leverages graph neural networks and long short-term memory networks.Initially,a comprehensive wheel-rail force detection system for trains was constructed,encompassing two key components:an instrumented wheelset and a ground wheel-rail force measuring system.Subsequently,utilizing this system,two distinct datasets were acquired from the track inspection vehicle:instrumented wheelset data and ground wheel-rail force data,a feedforward neural network was employed to calibrate the instrumented wheelset data,referencing the ground wheel-rail force data.Furthermore,ground wheel-rail force data for the locomotive was obtained for the corresponding road section.This data was then integrated with the calibrated instrumented wheelset data from the track inspection vehicle.Leveraging the GNN-LSTM network,the article establishes a mapping relationship model between the wheel-rail force of the track inspection vehicle and the locomotive wheel-rail force.This model facilitates continuous measurement of locomotive wheel-rail forces across three typical scenarios:straight sections,long and steep downhill sections,and small curve radius sections.

An integrated methodology for soil moisture analysis using multispectral data in Mongolia

Soil moisture(SM)content is one of the most important environmental variables in relation to land surface climatology,hydrology,and ecology.Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions.The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data.The study area is Tuv(48°40′30″N and 106°15′55″E)province in the forest steppe zones in Mongolia.In addition to this,land surface temperature(LST)and normalized difference vegetation index(NDVI)from Landsat satellite images were integrated for the assessment.Furthermore,we used a digital elevation model(DEM)from ASTER satellite image with 30-m resolution.Aspect and slope maps were derived from this DEM.The soil moisture index(SMI)was obtained using spectral information from Landsat satellite data.We used regression analysis to develop the model.The model shows how SMI from satellite depends on LST,NDVI,DEM,Slope,and Aspect in the agricultural area.The results of the model were correlated with the ground SM data in Tuv province.The results indicate that there is a good agreement between output SM and SM of ground truth for agricultural area.Further research is focused on moisture mapping for different natural zones in Mongolia.The innovative part of this research is to estimate SM using drivers which are vegetation,land surface temperature,elevation,aspect,and slope in the forested steppe area.This integrative methodology can be applied for different regions with forest and desert steppe zones.