Reliability and Human Factors in Geotechnical Engineering

Reliability methods have been applied successfully to two primary categories of uncertainties encountered in geotechnical engineering: Natural variabilities and modeling uncertainties. There are several additional categories of uncertainties that have not been addressed, primarily those due to human, organizational, and knowledge related factors. This paper proposes reliability based engineering approaches and strategies that can be used to help address these additional sources of uncertainties. The approaches and strategies have proven to provide useful insights regarding the potential effectiveness of proposed programs to improve quality and reliability of geotechnical engineered systems; facilitating development of rational decisions and judgements concerning trade-offs, costs, and benefits.     

Bayesian Framework for Characterizing Geotechnical Model Uncertainty

As any model is only an abstraction of the real world, model uncertainty always exists. The magnitude of model uncertainty is important for geotechnical decision making. If model uncertainty is not considered, the geotechnical predictions and hence the decisions based on the geotechnical predictions might be biased. In this study, a framework for characterizing geotechnical model uncertainty using observation data is proposed. The framework is based on the concept of multivariable Bayesian updating, in which the statistics of model uncertainty are updated using observed performance data. Uncertainties in both input parameters and observed data can be considered in the proposed framework. To bypass complex computational works involved in the proposed framework, a practical approximate solution is presented. The proposed framework is illustrated by characterizing the model uncertainty of four limit equilibrium methods for slope stability analysis using quality centrifuge test data. Parametric study in the illustrative example shows that both quality and quantity of the performance data could affect the determination of the model uncertainty, and that such effects can be systematically quantified with the proposed method.     

First-Order Rate Equations in Geotechnical Engineering

First-order rate equations model physical or chemical processes in which the rate of approach to an end condition is proportional to the departure from that condition. Relationships were defined and tested with geotechnical problems including the prediction of end values for consolidation and for settlement from limited data. Solutions were obtained by substituting trial end values to obtain linearity of a prescribed relationship. The method does not require a thorough understanding of the mechanisms involved, but only that they be consistent and asymptotic to an end value. The mandatory linear relationship cannot be obtained if these criteria are not met, for example, for linear elastic behavior. First-order rate equations are discontinuous and overlap at behavioral boundaries such as that between primary and secondary consolidation. They also suggest a separate stage involving the loss of structure during consolidation of a quick clay. Settlement predictions are in close agreement with data from a grain elevator and from the Kansai International Airport, Japan. Other applications include defining minimum void ratios in fine-grained soils and determining a zero-flow liquid limit value.     

Factors of Safety and Reliability in Geotechnical Engineering

Simple reliability analyses, involving neither complex theory nor unfamiliar terms, can be used in routine geotechnical engineering practice. These simple reliability analyses require little effort beyond that involved in conventional geotechnical analyses. They provide a means of evaluating the combined effects of uncertainties in the parameters involved in the calculations, and they offer a useful supplement to conventional analyses. The additional parameters needed for the reliability analyses—standard deviations of the parameters—can be evaluated using the same amount of data and types of correlations that are widely used in geotechnical engineering practice. Example applications to stability and settlement problems illustrate the simplicity and practical usefulness of the method.     

Digital Image Analysis in Geotechnical Engineering Education

Existing research has shown that visual input significantly contributes to learning, therefore, it is paramount to use visual tools to help demonstrate engineering concepts. One of these tools, digital image analysis, can help effectively communicate complex concepts to students in a simple and understandable format as a supplement to traditional lecturing, while simultaneously enabling students to have hands-on experience. This note describes a series of activities to incorporate digital image analysis into engineering education. The undergraduate students worked in research projects that involved image-based analysis of geomaterials. Based on these activities and the students’ response to a questionnaire, it was recognized that digital image analysis can enhance the understanding of engineering phenomena for undergraduate students. The hands-on experience and visual demonstration improved the students’ grasp of fundamental concepts in research projects. The research experience allowed the students to build a connection between the classroom and the solution of state-of-the-art engineering scientific problems. It also taught them about cooperation and teamwork, as well as academic independence.     

岩土工程专业方向课程改革探索

岩土工程是土木工程专业的专业方向之一.针对现在岩土工程在教学中存在的问题,对课程的教学内容、教学方法和专业实习时间等进行改革探索.通过调整证明:这些改进可有效地提高学生的学习兴趣,有助于学生巩固专业知识和提高专业学习能力.     

岩土工程地质灾害的防治与实践探讨

分析了引发岩土工程地质灾害的原因,阐述了灾害类型及防治施工技术标准并提出实践措施。旨在不断提升岩土工程地质灾害的防治水平,为防治工程实际提供参考。     

Probability-Based Diagnosis of Defective Geotechnical Engineering Structures

Many earth structures, including foundations, retaining walls, and road embankments, exhibit signs of distress, such as excessive vertical or lateral movements and cracking. Rotation or tilting and cracking of the structural components in retaining walls are quite common and constitute the majority of cases of concern in built-up areas. This paper describes a general iterative methodology for the diagnosis of existing civil infrastructure, with emphasis on geotechnical problems. The term diagnosis, as used in this paper, refers to the process of determining a set of technical explanations for the occurrence of the observed defects. Using probability theory, the investigating engineer is in a better position to make informed decisions rather than simply relying on experience and intuition. Engineering decisions include the correctness of a given hypothesis; whether further investigations are necessary or a terminal decision can be made; and whether appropriate corrective action can be undertaken. A case study is used to demonstrate the application of the methodology.     

Evolutionary Fuzzy Neural Inference System for Decision Making in Geotechnical Engineering

Problems in geotechnical engineering are full of uncertain, vague, and incomplete information. In most instances, successfully solving such problems depends on experts’ knowledge and experience. The primary object of this research was to develop an evolutionary fuzzy neural inference system (EFNIS) to imitate the decision-making processes in the human brain in order to facilitate geotechnical expert decision making. First, an evolutionary fuzzy neural inference model (EFNIM) was constructed by combining the genetic algorithm (GA), fuzzy logic (FL), and neural network (NN). In the proposed model, GA is primarily concerned with optimizing parameters required in the fuzzy neural network; FL with imprecision and approximate reasoning; and NN with learning and curve fitting. This research then integrates the EFNIM with an object-oriented computer technique to develop an EFNIS. Finally, the potential to apply the proposed system to practical geotechnical decision making is validated using two real problems, namely estimating slurry wall duration and selecting retaining wall construction methods.     

Integrated Lecture and Laboratory Modules for Contaminant Transport Studies in Undergraduate Geotechnical Engineering

The engineering concepts necessary for fully describing contaminant transport processes in soil and groundwater have been largely neglected by traditional civil engineering curricula in the United States. New lecture and laboratory modules have been developed and integrated into the undergraduate soil mechanics course for civil engineering students at the Colorado School of Mines. The lecture module includes theoretical development of the advection-dispersion equation and a set of practical example problems designed to clarify the relative importance of advection, dispersion, and molecular diffusion in realistic contaminant transport scenarios. The laboratory experiment is developed using relatively inexpensive soil permeability testing equipment common to most academic and industrial geotechnical engineering laboratories. Unlike conventional chemical transport testing, which requires relatively complicated, time consuming, and expensive analytical instrumentation, the new laboratory exercise relies on the use of simple colored dye for simulating the movement of a contaminant through a one-dimensional soil column. The key soil parameters governing the transport of the dye, including the diffusion coefficient, hydraulic conductivity, dispersion coefficient, and retardation coefficient, are quantified by observing the location and color intensity of the dye as it moves through the column. Emphasis is placed on maximizing the simplicity and visual impact of the experimental exercise. The effectiveness of the lecture and laboratory modules is assessed through a student survey.     

Data Division for Developing Neural Networks Applied to Geotechnical Engineering

In recent years, artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. In the majority of these applications, data division is carried out on an arbitrary basis. However, the way the data are divided can have a significant effect on model performance. In this paper, the issue of data division and its impact on ANN model performance is investigated for a case study of predicting the settlement of shallow foundations on granular soils. Four data division methods are investigated: (1) random data division; (2) data division to ensure statistical consistency of the subsets needed for ANN model development; (3) data division using self-organizing maps (SOMs); and (4) a new data division method using fuzzy clustering. The results indicate that the statistical properties of the data in the training, testing, and validation sets need to be taken into account to ensure that optimal model performance is achieved. It is also apparent from the results that the SOM and fuzzy clustering methods are suitable approaches for data division.     

湿陷性黄土地区建(构)筑物岩土工程治理实例

黄土区岩土工程治理难度较大。从目前工程实践来看,介绍了几种治理效果良好的施工方法,指出了其中应注意的问题。     

甘肃省某道路项目岩土工程地质评价浅析

简单阐述了某道路工程的基本概况,完成的主要工作量,分析了场地气象、水文、地层岩性以及不良地质作用等,进而对勘察区岩土工程地质进行了评价,最后提出了该勘察区不同土类做为地基持力层时应采取的处理措施及建议,对后续道路工程的施工起到了指引导作用.