Evaluation of statistical uncertainty of cement-treated soil strength using Bayesian approach

The statistical parameters for the strength of cement-treated soil are evaluated by the strength of cored samples retrieved from cement-treated columns for a quality assurance procedure in the deep mixing method. The sample parameters include the statistical uncertainty associated with the statistical sample size and other factors. Therefore, a probabilistic characterization of the statistical parameters of strength is required to quantify the statistical uncertainty in the quality assurance process. This paper presents a quantitative analysis of the statistical uncertainty for the estimation of the strength of cement-treated columns. The Bayesian approach is adopted to evaluate the statistical uncertainty occurring in the determination of the statistical parameters of the strength from observed data. The inference is performed via a Markov chain Monte Carlo method, in which samples of the parameters are sequentially drawn from a joint posterior probability distribution. An example analysis is performed to illustrate the statistical uncertainty of the unconfined compressive strength of cored samples retrieved from cement-treated columns. The results show that the statistical parameters, inferred from the data with the sample size of approximately 40, include considerable uncertainty. The variability of the estimated statistical parameters is found to depend on both the sample size and the spatial correlation. The influence of the statistical uncertainty, caused in the estimation of the mean and standard deviations in strength, is examined within the framework of quality assurance in the deep mixing method.     

Adaptive sampling strategy for characterizing spatial distribution of soil liquefaction potential using cone penetration test

Characterizing spatial distribution of soil liquefaction potential is critical for assessing liquefaction-related hazards (e.g. building damages caused by liquefaction-induced differential settlement). However, in engineering practice, soil liquefaction potential is usually measured at limited locations in a specific site using in situ tests, e.g. cone penetration tests (CPTs), due to the restrictions of time, cost and access to subsurface space. In these cases, liquefaction potential of soil at untested locations requires to be interpreted from limited measured data points using proper interpolation method, leading to remarkable statistical uncertainty in liquefaction assessment. This underlines an important question of how to optimize the locations of CPT soundings and determine the minimum number of CPTs for achieving a target reliability level of liquefaction assessment. To tackle this issue, this study proposes a smart sampling strategy for determining the minimum number of CPTs and their optimal locations in a self-adaptive and data-driven manner. The proposed sampling strategy leverages on information entropy and Bayesian compressive sampling (BCS). Both simulated and real CPT data are used to demonstrate the proposed method. Illustrative examples indicate that the proposed method can adaptively and sequentially select the required number and optimal locations of CPTs.     

Evaluation of bio-diesel properties: a statistical approach

In this work, methyl esters of Pungamia (MEOP), ethyl esters of Pungamia (EEOP), and ethyl esters of Neem (EEON) were primarily produced by means of transesterification process using sodium methoxide (mixture of sodium hydroxide and methanol) and potassium methoxide (mixture of potassium hydroxide and methanol). Further, the fuel properties such as calorific value, flash point, density and viscosity of methyl and ethyl esters were determined using ASTM specified equipment. Also, the association between different blend ratios and fuel type with fuel properties was gauged with an assistance of two-way analysis of variance. The experimental outcomes revealed that the maximum ester content of bio-diesel was witnessed for potassium hydroxide catalyst and the quantity of catalyst used was less than that with sodium hydroxide for the same mass of the feedstock oil. Overall, the statistical analysis disclosed that there is a significant difference in the fuel properties of the different combination of fuel type and blend ratios.     

基于CPT的地基液化概率评价

通过LOGISTIC模型,以世界范围内19次大地震106个场地的液化案例为基础,构建基于CPT的液化概率评价模型,采用仅通过CPT试验即可获取的锥尖阻力和摩阻比作为评价参数,既体现严格的统计学意义又具有明确的物理意义,预测液化和非液化的可靠性分别达到91．2％和86．8％。并拟舍得到液化概率和抗液化安全系数的S型关系曲线以便于工程应用。理论分析与工程实例对比表明,相对于《岩土工程勘察规范》推荐的CPT模型,本文建议模型的评价结果和《建筑抗震设计规范》的SPT模型评价结果更为接近,且物理意义更为明确,为利用现场原位测试数据进行地基液化评价提供了更为简洁、准确的新途径,可以作为规范方法的有益补充。     

适于不同深度土层液化的剪切波速判别公式

剪切波速测试是工程上常用的现场技术,正逐步成为液化判别方法的基本指标之一。利用早期的Andrus数据库对中国《岩土工程勘察规范》方法和Andrus方法进行检验,发现了二者存在的问题。提出了双曲线形式的剪切波速判别模型和公式。采用新的Kayen数据库对三者进行了对比检验,并讨论了提高判别精度的可能性和方式。结果表明:中国现行的《岩土工程勘察规范》中剪切波速判别液化方法,无论对浅层还是深层土,判别结果均严重保守,甚至会把十分密实的砂土判成液化,十分不合理;国际上应用广泛的Andrus方法对浅层土判别结果过于保守,对深层土判别成功率可以接受,但其临界剪切波速曲线在深处存在回弯的不合理现象;所提出的双曲线液化判别模型和公式,能够深浅兼顾,无论对浅层还是深层土都能给出较好的判别结果,克服了中国规范方法和Andrus方法的弊端,且形式简单便于工程应用;采用剪切波速进行液化判别时,应采取多次测试,以降低数据离散性,提高判别的准确性。     

某场地地震液化危害评价及地基处理建议

分析了砂土液化原理,结合工程实际,对某高速公路特大桥的液化场地土液化等级、危害性进行了评价,并结合工程特点,给出了切实可行的处理建议,从而保证该特大桥地基处理的效果。     

Evaluation of multiple column curves using the experimental data-base approach

The paper describes an extensive survey of column tests and an evaluation of information on steel column strength. During the past twenty-five years numerous series of column tests have been conducted in European countries, North America and Japan. A total of 1665 individual test results which have been stored in an experimental data-base is retrieved and a statistical appraisal carried out to evaluate experimental column strength. Comparisons are made between the surveyed test data and the ECCS and SSRC multiple column curves. The authors also propose new multiple column formulae which explain more accurately the test data from a statistical viewpoint.     

基于可拓学理论的砂土液化势综合评价研究

将可拓学理论引入到砂土液化势评价工作中,利用网络编程技术开发了基于Web平台的可拓综合评价系统,选取最关键的7个参数作为评价指标,建立了砂土液化势的可拓评价模型,从而找到了砂土液化势评价行之有效的新途径。     

Liquefaction potential at Izmit Bay: comparison of predicted and observed soil liquefaction during the Kocaeli earthquake

The 1999 Kocaeli earthquake resulted in large-scale damage to structures and a death toll of more than 20,000. Numerous liquefaction and associated ground deformations caused by the earthquake were particularly observed along the southern coasts of Izmit Bay and Sapanca Lake, as well as the city of Adapazari. The paper discusses the soil liquefaction and lateral spreading along the southern coast of Izmit Bay. A microzonation map was created using the data from liquefaction susceptibility analyses and a liquefaction severity index. The predicted liquefiable zones showed good agreement with site observations. The assessment suggests that the amount of liquefaction-susceptible layers in the soil profile reduces away from the coastal zone.      

Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

This study investigates the performance of four machine learning (ML) algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network (BBN) learning software Netica. The BBN structures that were developed by ML algorithms-K2, hill climbing (HC), tree augmented naive (TAN) Bayes, and Tabu search were adopted to perform parameter learning in Netica, thereby fixing the BBN models. The performance measure indexes, namely, overall accuracy (OA), precision, recall, F-measure, and area under the receiver operating characteristic curve, were used to evaluate the training and testing BBN models’ performance and highlight the capability of the K2 and TAN Bayes models over the Tabu search and HC models. The sensitivity analysis results showed that the cone tip resistance and vertical effective stress are the most sensitive factors, whereas the mean grain size is the least sensitive factor in the prediction of seismic soil liquefaction potential. The results of this study can provide theoretical support for researchers in selecting appropriate ML algorithms and improving the predictive performance of seismic soil liquefaction potential models.     

基于现代原位测试CPTU的土体液化势统一评价方法

静力触探(CPT)测试是国际上常用的砂质土液化判别技术,但无法反映孔隙水压力(孔压)对液化行为的影响.能够测试孔压的现代原位测试技术孔压静力触探(CPTU)对砂质土、粉质土具有更高的辨识度和灵敏度,因此在砂质土、粉质土液化评价方面具有独特的优势.通过编译的CPTU液化案例数据库,在简化应力框架下,通过粒子群优化核极限学...     

Internal governance and member investment behavior in energy cooperatives: An experimental approach

Cooperatives may become increasingly important as suppliers of electricity from renewable resources. Numerous governance models exist for establishing a renewable energy cooperative. Since members self-select into the organization, causal links between methods of internal governance and member characteristics are difficult to identify. We demonstrate how economic experiments can address this problem. In a simple social-dilemma game, we study the impact of heterogeneity in wealth on investment in a jointly owned enterprise under two different governance models. We do not find that member heterogeneity or governance model affect investment levels. A participant's endowment appears to be the most important factor explaining variation in investment. Good knowledge of cooperative governance has a positive impact on investment in the game and good knowledge of game theory has a negative impact on investment in the game. Future research should investigate the effect of the distribution of control rights on the performance of cooperative enterprises.     

砂土液化分析及其防治措施

针对地基处理中的难题之一——砂土液化问题,首先分析了砂土的液化机理,然后给出了砂土的液化判别及影响因素,最后综述了目前国内外所采用的砂土液化治理措施,可供相关技术人员参考借鉴。     

Experimental investigation on energy saving potential of smart HVAC unit

It is well known that ‘the energy can’t be created but could be saved’ and the energy resources are depleting at an alarming rate over the decades. The HVAC unit consumes 1/5th of the World’s electrical power consumption. Over the years several modifications have been made on the HVAC unit to save energy. This study implements the machine-to-machine interaction through Internet of things which reduces the human effort. The proposed system utilises the smart power controlling and modifying feedback systems from mobile phones integrated with human motion detectors. Due to the accurate feedback system, the room temperature can be maintained within the range of ±0.3°C. The experiment shows an interesting result that the power consumption of compressor can be reduced by 45% using the smart control HVAC unit. The client can also control the system through a web server which controls the actuators.     

砂土液化影响因素及其判别方法

砂土液化受动荷条件、埋藏条件和土性条件的制约，在进行砂土液化判别时不能从某一方面或某一统计公式简单评判。综合考虑各影响因索，采用多因子判别分析法加以判别。     

基于颗粒接触状态理论的粗细粒混合料液化强度试验研究

为探讨粗细粒混合料的液化强度CRR,对具有不同细粒含量FC和相对密度Dr的粗细粒混合料开展了一系列不排水循环三轴试验。基于颗粒接触状态理论,将粗细粒混合料分为类粗粒土、中间性态土和类细粒土;用骨架孔隙比esk表征混合料骨架颗粒的接触状态,引入参数b与m描述粗细粒混合料从类细粒土到类粗粒土过度中粗粒与细粒对颗粒接触状态的影响。试验结果表明:随着FC的增加,具有相同Dr粗细粒混合料的CRR先降低后基本保持不变。此外,具有不同FC和Dr的粗细粒混合料CRR都随esk的增大而降低。分析表明:基于颗粒接触状态理论的esk是合理地表征粗细粒混合料CRR的一个物理状态指标,且两者呈现较好的负幂函数关系。     

浅谈液化土的判别

以液化土为例,阐述了液化土的概念及形成机理,提出了工程上液化土判别的常用方法,以使工程技术人员对液化土有一个概括的了解,从而能根据工程的实际情况选择合理的液化土的判别方法。     

Improving prediction of soil liquefaction using hybrid optimization algorithms and a fuzzy support vector machine

The phenomenon of soil liquefaction is one of the most complex and interesting fields in geotechnical earthquakes that has drawn the attention of many researchers in recent years. The present study used hybrid particle swarm optimization and genetic algorithms with a fuzzy support vector machine (FSVM) as the classifier for the soil liquefaction prediction problem. Fuzzy logic is used to decrease the outlier sensitivity of the system by inferring the importance of each sample in the training phase to increase the ability of the classifier’s generalization. Using the appropriate combination of optimization algorithms, we can find the best parameters for the classifier during the training phase without the need for trial and error by the user due to the high accuracy of the classifier. The proposed approach was tested on 109 CPT-based field data from five major earthquakes between 1964 and 1983 recorded in Japan, China, the USA and Romania. Good results have been demonstrated for the proposed algorithm. Classification accuracy is 100% for the combination of the optimization algorithms with the FSVM classifier. The results show that the best kernel used in the training of the FSVM classifier is a radial basis function (RBF). According to the experimental results, the proposed algorithm can improve classification accuracy and that it is a feasible method for predicting soil liquefaction using the optimal parameters of the classifier with no user interface.

     

地基液化评价的模糊不确定性分析

基于世界范围内19次中强震的CPT液化案例,通过构建客观性较强的液化隶属函数映射地基液化模糊性;基于LOGISTIC模型导出地基抗液化强度CRR曲线及其概率密度函数;借用中国华北地区地震动加速度衰减关系,构建CSR的概率密度函数,进而分析地基液化评价的模糊不确定性.结果表明,忽略地基液化评价的模糊不确定性,评价结果可能偏于危险;基于模糊不确定性的地基液化评价工程意义较为明显,可作为规范方法的有益参考.     

Effect of earthquake ground motions on soil liquefaction

During the 2011 off the Pacific Coast of Tohoku Earthquake, which was the largest earthquake in Japanese history, the soil liquefaction phenomenon was observed over a wide area along the Pacific Coast in Tohoku and in Kanto, including the Tokyo Bay area. Extensive damage was caused by the effect of soil liquefaction to residential lands and houses, as well as to infrastructures, such as roads, rivers, ports, and water supply/sewage systems. Since the 2011 off the Pacific Coast of Tohoku Earthquake occurred in a mega-size fault zone, with an area of 500 km×200 km, the duration of the strong shaking was extremely long compared to that in the data recorded for past earthquakes. Clarifying the effect of the characteristics of the ground motion on the soil liquefaction mechanism is one of the essential studies to be conducted, and effective countermeasures for the damaged structures need to be found.This paper presents the strong motion observation data obtained on the liquefied and the non-liquefied grounds and raises preliminary discussions on the mechanism of soil liquefaction based on this data. The effect of the duration and the number of cyclic loadings on the progress of the soil liquefaction phenomenon is also compared with that found in past strong motion data.