Multiple Tuned Mass Damper Based Vibration Mitigation of Offshore Wind Turbine Considering Soil–Structure Interaction

The dynamics of jacket supported offshore wind turbine （OWT） in earthquake environment is one of the progressing focuses in the renewable energy field. Soil-structure interaction （SSI） is a fundamental principle to analyze stability and safety of the structure. This study focuses on the performance of the multiple tuned mass damper （MTMD） in minimizing the dynamic responses of the structures objected to seismic loads combined with static wind and wave loads. Response surface methodology （RSM） has been applied to design the MTMD parameters. The analyses have been performed under two different boundary conditions： fixed base （without SSI） and flexible base （with SSI）. Two vibration modes of the structure have been suppressed by multi-mode vibration control principle in both cases. The effectiveness of the MTMD in reducing the dynamic response of the structure is presented. The dynamic SSI plays an important role in the seismic behavior of the jacket supported OWT, especially resting on the soft soil deposit. Finally, it shows that excluding the SSI effect could be the reason of overestimating the MTMD performance.

Prediction of Compressive Strength of Self-Compacting Concrete Using Intelligent Computational Modeling

In the present scenario,computational modeling has gained much importance for the prediction of the properties of concrete.This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete(SCC).Three models,namely,Extreme Learning Machine(ELM),Adaptive Neuro Fuzzy Inference System(ANFIS)and Multi Adaptive Regression Spline(MARS)have been employed in the present study for the prediction of compressive strength of self compacting concrete.The contents of cement(c),sand(s),coarse aggregate(a),fly ash(f),water/powder(w/p)ratio and superplasticizer(sp)dosage have been taken as inputs and 28 days compressive strength(fck)as output for ELM,ANFIS and MARS models.A relatively large set of data including 80 normalized data available in the literature has been taken for the study.A comparison is made between the results obtained from all the above-mentioned models and the model which provides best fit is established.The experimental results demonstrate that proposed models are robust for determination of compressive strength of self-compacting concrete.

Dynamic soil-structure interaction analysis in time domain based on a modified version of perfectly matched discrete layers

Analysis of soil-structure interaction is commonly conducted by dividing the infinite domain of the soil into two domains:interior and exterior domains.The interior domain is bounded in a small region,while the exterior domain is replaced by artificial boundary conditions.The choice of artificial boundary conditions is a critical issue in the analysis of soil-structure interaction problems.Perfectly matched discrete layer(PMDL)has been proved as a good approach for modeling the exterior domain.In this study,a modified version of the PMDLs,i.e.PMDLs with analytical wavelengths(AW-PMDLs),is used in the soil-structure interaction analysis in time domain,which essentially can be regarded as an extension of the analysis in frequency domain,being previously proven to be effective.Numerical verifications are implemented.The results demonstrate that the proposed method performs well in the analysis of soilstructure interaction problems in time domain.

An Advanced Probabilistic Neural Network for the Design of Breakwater Armor Blocks

In this study, an advanced probabilistic neural network （APNN） method is proposed to reflect the global probability density function （PDF） by summing up the heterogeneous local PDF which is automatically determined in the individual standard deviation of variables. The APNN is applied to predict the stability number of armor blocks of breakwaters using the experimental data of＇ van der Meet, and the estimated results of the APNN are compared with those of an empirical formula and a previous artificial neural network （ANN） model. The APNN shows better results in predicting the stability number of armor bilks of breakwater and it provided the promising probabilistic viewpoints by using the individual standard deviation in a variable.

Determination of reservoir induced earthquake using support vector machine and gaussian process regression

The prediction of magnitude （M） of reservoir induced earthquake is an important task in earthquake engineering. In this article, we employ a Support Vector Machine （SVM） and Gaussian Process Regression （GPR） for prediction of reservoir induced earthquake M based on reservoir parameters. Comprehensive parameter （E） and maximum reservoir depth] （H） are considered as inputs to the SVM and GPR. We give an equation for determination oil reservoir induced earthquake M. The developed SVM and GPR have been compared with] the Artificial Neural Network （ANN） method. The results show that the developed SVM and] GPR are efficient tools for prediction of reservoir induced earthquake M. /

Kinetic study on catalytic gasification of a modified sludge fuel

A new type of mixture fuel, sludge–oil–coal agglomerate (SOCA), was catalytically gasified with steam in a thermobalance reactor under atmospheric pressure. All the four catalysts studied (K2CO3, CaO, NiO and Fe2O3) were found capable of enhancing the steam gasification rate and significantly increasing the conversion of carbon. The ranking of catalytic activity was found to be K2CO3 CaO > NiO > Fe2O3. A modified volumetric-reaction model in the literature was used to describe the conversion behavior of the steam gasification studied by evaluating the kinetic parameters. Expressions of the apparent gasification rates for SOCA were presented for the design of catalytic gasification processes.

基于最小二乘支持向量机算法的小地锚抗拔承载力研究（英文）

目的:基于最小二乘支持向量机算法预测小地锚的抗拔承载力。方法:最小二乘支持向量机算法中的输入参数包括等效地锚直径,地锚埋置深度,平均顶椎阻力,平均椎套摩擦力以及安装工艺。使用现场试验的119组数据中的83组数据进行最小二乘支持向量机回归模型分析,并使用剩余的36组数据测试模型的拟合良好性;同时用敏感度分析研究每个输入参数的作用。结论:通过与人工神经网络模型的对比,发现最小二乘支持向量机的性能表现优异。