Constitutive model for gas hydrate-bearing soils considering different types of hydrate morphology and prediction of strength-band

The present study proposes a new elasto-plastic constitutive model that considers different types of hydrates in pore spaces. Many triaxial compression tests on both methane hydrate-bearing soils and carbon dioxide hydrate-bearing soils have been carried out over the last few decades. It has been revealed that methane hydrate-bearing soils and carbon dioxide hydrate-bearing soils have different strength and dilatancy properties even though they have the same hydrate contents. The reason for this might be due to the different types of hydrate morphology. In this study, therefore, the effect of the hydrate morphology on the mechanical response of gas-hydrate-bearing sediments is investigated through a model analysis by taking into account the different hardening rules corresponding to each type of hydrate morphology. In order to evaluate the capability of the proposed model, it is applied to the results of past triaxial compression tests on both methane hydrate-containing and carbon dioxide hydrate-containing sand specimens. The model is found to successfully reproduce the different stress–strain relations and dilatancy behaviors, by only giving consideration to the different morphology distributions and not changing the fitting parameters. The model is then used to predict a possible range in which the maximum deviator stress can move for various hydrate morphology ratios; the range is defined as the strength-band. The predicted curve of the maximum deviator stress obtained by the constitutive model matches the empirical equations obtained from past experiments. It supports the fact that the hydrate morphology ratio changes with the total hydrate saturation. These findings will contribute to a better understanding of the relation between the microscopic structures and macro-mechanical behaviors of gas-hydrate-bearing sediments.     

Neural computing approach for estimation of natural gas dew point temperature in glycol dehydration plant

ABSTRACT

Hydrate formation and water accumulation during transmission process can be prevented by effective dehydration of gas streams. This study investigates the application of multi-layer perceptron artificial neural network to estimate natural gas dew point temperature using contactor temperature and TEG purity as independent variables. A dataset comprised of 173 data points is extracted from the literature. The proposed model is considered a great help for engineers to have precise dew point temperature predictive tool in wide ranges of concerning variables. Results obtained from the proposed model show R-squared and mean square error of 1.000 and 0.156, respectively. Accordingly, the remarkable agreement between predicted and experimental values is observed.     

A neural network model for the prediction of compression ignition engine performance at different injection timings

Rapid depletion of fossil fuel and continuous increase in gasoline prices have stimulated the search of alternative fuels. This paper deals with the prediction of engine performance, emission and combustion characteristics of compression ignition engine fuelled with fish oil biodiesel using artificial neural network (ANN). Experimental investigations are carried out in a single cylinder constant speed direct injection diesel engine under variable load conditions at different injection timings?21⁰, 24⁰ and 27⁰ bTDC. The performance, combustion and emission characteristics are measured using an exhaust gas analyser, smoke meter, piezoelectric pressure transducer and crank angle encoder for different fuel blends and engine load conditions. For training the neural network, feed-forward back propagation algorithm is used. The developed ANN model predicts the performance, combustions and exhaust emissions with a correlation coefficients (R) of 0.97–0.99 and a mean relative error of 0.62–4.826%. The root mean square errors are found to be low. The developed model has found to predict accurately the engine performance, combustion and emission parameters at different injection timings.     

BP人工神经网络模型在建筑物沉降预测中应用

以BP人工神经网络模型为基础,建立预测模型,以小区某栋建筑物l期～8期的沉降观测数据为输入数据和输出数据,对网络模型进行训练,并对9期～12期实际观测值与预测值进行了比较,结果比较理想,从而验证了采用BP人工神经网络模型进行建筑物沉降的预测是可行的。     

基于互联网的神经网络空调负荷预测解决方案

在分析比较各种负荷预测方法的基础上，给出了一个基于互联网的应用神经网络方法进行负荷预测的方案。该方法通过互联网以“准在线”的方式可同时满足较高的逐时负荷预测精度和模型调整的要求，并已在实际工程中使用，取得了一定的效果。     

大体积混凝土温升预测的非线性模型

大体积混凝土的水化热若不能及时散发,会产生很大的温度应力,导致出现温度裂缝。为了避免温度裂缝的产生,人们必须预测和控制大体积混凝土的温度形成。针对大体积混凝土温度场的非稳态特性,提出了一种基于灰色人工神经网络的温升预测模型,介绍了灰色神经网络预测方法在工程中的应用,采用Matlab进行计算。预测结果表明,该模型收敛速度快,预测精度较高,实现了对大体积混凝土温升的准确预测。说明了灰色人工神经网络方法的可行性和实用性。     

基于人工神经网络的大体积混凝土温度场预测

大体积混凝土的水化热不容易及时散发,内部温升将会很高,从而产生很大的温度应力,导致出现温度裂缝。预测分析温度场为研究温度裂缝以及进行温控设计、制定合理的防裂措施提供了依据。针对大体积混凝土温度场的非稳态特性,提出了一种基于BP人工神经网络Levenberg—Marquardt算法的温度场预测模型。预测结果表明,该模型收敛速度快,预测精度较高。     

造价估测方法的研究和BP神经网络模型的应用

比较分析了现行的造价估测模型的特点及其存在的问题,突出BP神经网络模型进行造价估测的理论优势,引入工程分类思想,以学校类建筑为例,建立了BP神经网络估测模型并进行了造价估测.     

一个深海能源土的温度-水压-力学二维微观胶结模型

天然气水合物以胶结形式赋存时,对深海能源土的强度和变形特性影响显著,且其影响程度与所处温度、水压与力学环境密切相关。旨在建立可考虑温（温度）-压（水压）-力（力-位移与胶结破坏准则）耦合影响的深海能源土微观胶结模型。首先,依据能源土中水合物胶结可发生于两种接触形式（直接接触与有间距）的土颗粒间,提出适用于两种胶结模式的力-位移准则和胶结破坏准则。其次,提出温压距离参数L（表示在无量纲化处理后的温度-水压坐标平面,土体所处温度与水压点到水合物相平衡线的最小距离）,并依据文献资料分析,建立水合物胶结强度、刚度与参数L间的关系。最后,建立由水合物饱和度确定的粒间水合物胶结尺寸计算方法,并据此进一步建立了胶结强度与刚度同水合物饱和度间的关系。该模型可以方便地植入离散元程序,从而用于深海能源土的宏微观力学分析。     

基于人工神经网络的土工合成材料加筋挡墙临界高度预测模型(英文)

提出了一种基于人工神经网络(ANN)技术的加筋挡墙设计高度预测方法。通过分析挡墙失效的原因,确定了7个主要因素作为网络的输入神经元。收集23组挡墙离心模型试验数据,2组足尺试验数据,1组实际工程的破坏数据,共26组样本作为训练及检验样本,建立了可用于加筋挡墙设计高度预测的径向基函数网络(RBFN)及误差反传网络(BPN)模型。结果表明径向基函数网络在学习速度,预测准确性及网络推广能力方面均优于BP网络,本文方法可用于加筋支挡结构的设计参考。     

用人工神经网络预测饱和砂土的液化势

介绍了预测饱和砂土的液化势的人工神经网络法，结合工程实例详细阐述了该方法的建模、预测结果与实测值较为吻合，表明在工程抗震中运用这一方法的有效性。     

An artificial neural network model for the prediction of critical submergence for intake in a stratified fluid medium

Density differences may occur because of temperature differentials, suspended sediments, dissolved salts or other chemicals. Most of the large surface reservoirs are stably stratified throughout most, or all, of the year. One of the means of assisting the management is to allow a selective withdrawal from the reservoir. And while an intake is used for withdrawal (from the lower layer), a maximum discharge is required not allowing the uptake of the upper layer fluids. The value of the intake's vertical distance from the upper layer elevation (submergence) when the upper layer fluids begin to be drawn into the intake is known as ‘critical submergence’. In this study, the critical submergence for a circular intake pipe in a stratified body (which has different layer thickness) is investigated. Experiments were conducted on a vertically flowing downward intake pipe in a still-water reservoir. Artificial neural network (ANN) models and formulas, which are found by the theoretical analysis of critical spherical sink surface (CSSS), are used for the analysis of experimental results. The CSSS has the same centre and discharge as the intake. The ANN model and CSSS results are compared with the experimental results.     

A geomatics-based road surface temperature prediction model

A GIS-based model for the prediction of road surface temperature is presented that has the ability to explain up to 74% of the spatial variation in road surface temperature in the West Midlands, UK. The approach combines basic spatial data sets with a synergy of surveying techniques to produce a geographical parameter database that drives the spatial component of a road weather prediction model. By measuring and modelling geographical parameters such as altitude, landuse, road construction and the sky-view factor, pre-existing components of road ice prediction systems can be united to provide a dynamic road ice prediction system.     

Development and validation of a drinking water temperature model in domestic drinking water supply systems

Domestic drinking water supply systems (DDWSs) are the final step in the delivery of drinking water to consumers. Temperature is one of the rate-controlling parameters for many chemical and microbiological processes and is, therefore, considered as a surrogate parameter for water quality processes. In this study, a mathematical model is presented that predicts temperature dynamics of the drinking water in DDWSs. A full-scale DDWS resembling a conventional system was built and run according to one year of stochastic demands with a time step of 10 s. The drinking water temperature was measured at each point-of-use in the systems and the data-set was used for model validation. The temperature model adequately reproduced the temperature profiles, both in cold and hot water lines, in the full-scale DDWS. The model showed that inlet water temperature and ambient temperature have a large effect on the water temperature in the DDWSs.     

Hybrid intelligent inference model for enhancing prediction accuracy of scour depth around bridge piers

Bridge-pier scouring is a main cause of bridge failures. Thus, accurately predicting the scour depth around bridge piers is critical, both to specify adequate depths for new bridge foundations and to assess/monitor the safety of existing bridges. This study proposes a novel artificial intelligence (AI) model, the intelligent fuzzy radial basis function neural network inference model (IFRIM), to estimate future scour depth around bridge piers. IFRIM is a hybrid of the radial basis function neural network (RBFNN), fuzzy logic (FL), and the artificial bee Cclony (ABC) algorithm. In the IFRIM, FL is used to handle the uncertainties in input information, RBFNN is used to handle the fuzzy input–output mapping relationships, and the ABC search engine employs optimisation to identify the most suitable tuning parameters for RBFNN and FL based on minimal error estimation. A 10-fold cross-validation method finds that the IFRIM model achieves at least 21% and 14.5% reductions in root mean square error and mean absolute error values, respectively, compared with other AI techniques. Study results support the IFRIM as a promising new tool for civil engineers to predict future scour depth around bridge piers.     

大体积混凝土温升神经网络预测

针对大体积混凝土温度场的非稳态特性,提出了一种基于BP人工神经网络的温升预测模型,介绍了BP神经网络预测方法在工程中的应用,预测结果表明,该模型收敛速度快,预测精度较高,从而实现了对大体积混凝土温升的准确预测。     

预测学在火灾科学中的应用

通过对火灾预测的发展过程和火灾预测可行性的讨论，介绍了火灾预测的基本原理，分析了定性预测法、灰色预测法、人工神经网络预测法、数理统计法、回归预测法等在火灾预测和统计中的应用。     

岩爆预测的人工神经网络模型

选取岩石抗压强度、抗拉强度、弹性能量指数和洞壁最大切向应力作为岩爆预测的评判指标 ,建立了岩爆预测的神经网络模型 ,对岩爆的发生及其烈度进行预测。实例计算表明 ,用人工神经网络方法进行岩爆预测是可行有效的     

神经网络在钻孔灌注桩承载力预测中的应用

探讨了桩基在外荷载作用下荷载传递的机理,分析了影响钻孔桩极限承载力的主要因素,并将神经网络技术引入桩基的极限承载力预测工作中,指出该技术能考虑传统的各种分析方法所考虑的因素,还能考虑非确定性的和非数值型的因素,工程实例说明,神经网络预测桩基承载力有广泛的应用前景。     

基于BP神经网络的隧道岩爆预测模型研究

在分析岩爆主要影响因素的基础上,建立了基于BP神经网络岩爆预测模型,采用已有岩爆发生数据作为训练样本对网络进行训练,利用收敛的网络进行岩爆烈度预测,预测结果与实际吻合,说明利用人工神经网络预测岩爆发生烈度是一种可行的方法。