滴水兽的夜晚共19页文档
夜幕下的甘露
夜幕下的甘露夜幕来临，露水慢慢长大变成甘露。
半夜，一个昆虫飞累了停在叶子上面甘露旁边。
甘露很兴奋问：朋友你要到那里去。
昆虫看着美丽的甘露说：我没地方去，只是到处看看现在累了，就在这里歇歇，我没打扰你吧。
甘露甜美的笑了笑：不会，我自己好孤单。
你来了正好陪我聊聊，我们可以做个朋友吗？昆虫说：恩，当然可以了。
甘露又问：你怎么也自己呀，你的伴侣呢。
昆虫：我们分开很久了，各自过自己的生活。
（昆虫看看周围）你怎么也自己在这里。
甘露委屈的说：我一来到这世上就自己在这里，没有其它的伴侣。
在过不久我就要离开这里了，要去很远的地方。
昆虫为了不甘露让伤心说：别怕我在这陪你，直到你离开这里。
甘露很开心说：真的吗？不过我离开时我怕我会伤害你。
昆虫笑了笑说：没事，你离开时我会为你祝福的。
甘露很高兴同时又很怕会伤害昆虫：那好吧。
昆虫看着她在月亮照耀下，那美丽可和爱高贵的甘露。
想着自己平平凡凡的能配的上她吗？它们聊的很开心很幸福可是幸福的日子总是短暂的，夜静静的过去，迎接黎明。
天暂暂的亮了，太阳很快就要出来了。
甘露忧伤的看着昆虫说：我就要离开了我会永远记住你的。
昆虫想掉眼泪但是忍着了，将泪往心里流。
微微的笑着说：恩，祝你找到自己的快乐。
我也会永远记得我们在一起的快乐日子。
天亮了，太阳露出了一点头。
在这世间最伤人的离别时。
昆虫和甘露听到（又惊又喜的）一个打哈欠的声音。
就在树里的一个老人传出来的声音：早，你们的事我都知道了。
这个老人就是月老。
它们那份无私的爱感动了上天。
上天派月老下来解救它们。
月老不想打扰它们竟然睡着了。
月老说：我有一个办法可以让你们永远在一起。
昆虫和甘露高兴的一起说：真的。
月老拉拉胡子说：恩，但不知道你们能不能做到。
昆虫惊喜的抱着甘露说：你说吧，要怎样做。
月老不荒不忙的说：甘露你愿意放下你那高贵的身份，和一个平凡的昆虫在一起永不离弃吗。
甘露看着昆虫说：我愿意，永不离弃。
月老微笑着：恩好，就看你昆虫的了。
你要一直往北飞，期间只能吃饭的时间可以停留一刻，在你飞的过程会有很多难关，能不能做到就看你了。
1996_Kulhawy and Phoon_ENGINEERING JUDGMENT IN THE EVOLUTION FROM DETERMINISTIC TO RBD Foundation De
Proceedings of Uncertainty ’96, Uncertainty in the Geologic Environment - From Theory to Practice (GSP 58), Eds. C. D. Shackelford, P. P. Nelson & M. J. S. Roth, ASCE, New York, 1996 ENGINEERING JUDGMENT IN THE EVOLUTION FROM DETERMINISTIC TO RELIABILITY-BASED FOUNDATION DESIGNFred H. Kulhawy1 and Kok-Kwang Phoon2ABSTRACT: Engineering judgment has always played a predominant role in geotechnical design and construction. Until earlier this century, most of this judgment was based on experience and precedents. The role of judgment in geotechnical practice has undergone significant changes since World War II as a result of theoretical, experimental, and field developments in soil mechanics, and more recently, in reliability theory. A clarification of this latter change particularly is needed to avoid misunderstanding and misuse of the new reliability-based design (RBD) codes. This paper first provides a historical perspective of the traditional factor of safety approach. The fundamental importance of limit state design to RBD then is emphasized. Finally, an overview of RBD is presented, and the proper application of this new design approach is discussed, with an example given of the ultimate limit state design of drilled shafts under undrained uplift loading. Judgment issues from traditional approaches through RBD are interwoven where appropriate.INTRODUCTIONAlmost all engineers would agree that engineering judgment is indispensable to the successful practice of engineering. Since antiquity, engineering judgment has played a predominant role in geotechnical design and construction, although most of the early judgment was based on experience and precedents. A major change in engineering practice took place when scientific principles, such as stress analysis, were incorporated systematically into the design process. In geotechnical engineering in particular, significant advances were made following World War II primarily because of extensive theoretical, experimental, and field research. The advent of powerful and inexpensive computers in the last two decades has helped to provide further impetus to the expansion and adoption of theoretical analyses in geotechnical engineering practice. The role of 1- Professor, School of Civil and Environmental Engineering, Hollister Hall, Cornell University, Ithaca, NY 14853-35012- Lecturer, Department of Civil Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 051130engineering judgment has changed as a result of these developments, but the nature of this change often has been overlooked in the enthusiastic pursuit of more sophisticated analyses. Much has been written by notable engineers to highlight the danger of using theory indiscriminately, particularly in geotechnical engineering (e.g., Dunnicliff & Deere, 1984; Focht, 1994). For example, engineering judgment still is needed (and likely always will be!) in site characterization, selection of appropriate soil/rock parameters and methods of analysis, and critical evaluation of the results of analyses, measurements, and observations. The importance of engineering judgment clearly has not diminished with the growth of theory and computational tools. However, its role has become more focused on those design aspects that remained outside the scope of theoretical analyses.At present, another significant change in engineering practice is taking place. Much of the impetus for this innovation arose from the widespread rethinking of structural safety concepts that was brought about by the boom in post-World War II construction (e.g., Freudenthal, 1947; Pugsley, 1955). Traditional deterministic design codes gradually are being phased out in favor of reliability-based design (RBD) codes that can provide a more consistent assurance of safety based on probabilistic analyses. Since the mid-1970s, a considerable number of these new design codes have been put into practice for routine structural design, for example, in the United Kingdom in 1972 (BSI-CP110), in Canada in 1974 (CSA-S136), in Denmark in 1978 (NKB-36), and in the U.S. in 1983 for concrete (ACI) and in 1986 for steel (AISC). In geotechnical engineering, a number of RBD codes also have been proposed recently for trial use (e.g., Barker et al. 1991; Berger & Goble 1992; Phoon et al. 1995).The impact of these developments on the role of engineering judgment is analogous to that brought about by the introduction of scientific principles into engineering practice. In this continuing evolution, it must be realized that RBD is just another tool, but it is different from traditional deterministic design, even though the code equations from both methods have the same “look-and-feel”. These differences can lead to misunderstanding and misuse of the new RBD codes. For these reasons, it is necessary to: (a) clarify how engineering judgment can be used properly so that it is compatible with RBD, and (b) identify those geotechnical safety aspects that are not amenable to probabilistic analysis. In this paper, an overview is given first of the traditional geotechnical design approach from the perspective of safety control. The philosophy of limit state design then is presented as the underlying framework for RBD. Finally, the basic principles of RBD are reviewed, and the proper application of this new design approach is discussed with an example of the ultimate limit state design of drilled shafts under undrained uplift loading. As described in the paper title, engineering judgment is interwoven throughout.TRADITIONAL GEOTECHNICAL DESIGN PRACTICEThe presence of uncertainties and their significance in relation to design has long been appreciated (e.g., Casagrande 1965). The engineer recognizes, explicitly or otherwise, that there is always a chance of not achieving the design objective, which is to ensure that the31 system performs satisfactorily within a specified period of time. Traditionally, the geotechnical engineer relies primarily on factors of safety at the design stage to reduce the risk of potential adverse performance (collapse, excessive deformations, etc.). Factors of safety between 2 to 3 generally are considered to be adequate in foundation design (e.g., Focht & O’Neill 1985). However, these values can be misleading because, too often, factors of safety are recommended without reference to any other aspects of the design computational process, such as the loads and their evaluation, method of analysis (i.e., design equation), method of property evaluation (i.e., how do you select the undrained shear strength?), and so on. Other important considerations that affect the factor of safety include variations in the loads and material strengths, inaccuracies in the design equations, errors arising from poorly supervised construction, possible changes in the function of the structure from the original intent, unrecognized loads, and unforeseen in-situ conditions. The manner in which these background factors are listed should not be construed as suggesting that the engineer actually goes through the process of considering each of these factors separately and in explicit detail. The assessment of the traditional factor of safety is essentially subjective, requiring only global appreciation of the above factors against the backdrop of previous experience.The sole reliance on engineering judgment to assess the factor of safety can lead to numerous inconsistencies. First, the traditional factor of safety suffers from a major flaw in that it is not unique. Depending on its definition, the factor of safety can vary significantly over a wide range, as shown in Table 1 for illustrative purposes. The problem examined in Table 1 is to compute the design capacity of a straight-sided drilled shaft in clay, 1.5 m in diameter and 1.5 m deep, with an average side resistance along the shaft equal to 36 kN/m2 and a potential tip suction of 1/2 atmosphere operating during undrained transient live loading. Five possible design assumptions are included. The first applies the factor of safety (FS) uniformly to the sum of the side, tip, and weight components; the second applies the FS only to the side and tip components; the third is like the first, but disregarding the tip; the fourth is like the second, but disregarding the tip; and the fifth is ultra-conservative, considering only the weight. It is clear from Table 1 that a particular factor of safety is meaningful only with respect to a given design assumption and equation.Another significant source of ambiguity lies in the relationship between the factor of safety and the underlying level of risk. A larger factor of safety does not necessarily imply a smaller level of risk, because its effect can be negated by the presence of larger uncertainties in the design environment. In addition, the effect of the factor of safety on the underlying risk level also is dependent on how conservative the selected design models and design parameters are.In a broad sense, these issues generally are appreciated by most engineers. They can exert additional influences on the engineer’s choice of the factor of safety but, in the absence of a theoretical framework, it is not likely that the risk of adverse performance can be reduced to a desired level consistently. Therefore, the main weakness in traditional practice, where assurance of safety is concerned, can be attributed to the lack of clarity in the relationship32TABLE 1. Design Capacity Example (Kulhawy 1984, p. 395)Equation Q ud (kN) for Q u/ Q udDesign DesignAssumption FS = 3 (“actual” FS)1 Q ud= (Q su + Q tu + W)/FS 170.7 3.02 Q ud - W = (Q su + Q tu) /FS 214.2 2.43 Q ud= (Q su + W)/FS 108.9 4.74 Q ud - W = Q su /FS 152.4 3.45 Q ud= W/FS 21.8 23.5 Note: Q su = side resistance = 261.8 kN, Q tu = tip resistance = 184.4 kN, W = weight of shaft = 65.3 kN, Q u = available capacity = Q su + Q tu + W = 511.6 kN, Q ud =design uplift capacity, FS = factor of safetybetween the method (factor of safety) and the objective (reduce design risk). To address this problem properly, an essential first step is to establish the design process on a more logical basis, known as limit state design.PHILOSOPHY OF LIMIT STATE DESIGNThe original concept of limit state design refers to a design philosophy that entails the following three basic requirements: (a) identify all potential failure modes or limit states, (b) apply separate checks on each limit state, and (c) show that the occurrence of each limit state is sufficiently improbable. Conceptually, limit state design is not new. It is merely a logical formalization of the traditional design approach that would help facilitate the explicit recognition and treatment of engineering risks. In recent years, the rapid development of RBD has tended to overshadow the fundamental role of limit state design. Much attention has been focused on the consistent evaluation of safety margins using advanced probabilistic techniques (e.g., MacGregor 1989). Although the achievement of consistent safety margins is a highly desirable goal, it should not be overemphasized to the extent that the importance of the principles underlying limit state design become diminished.This fundamental role of limit state design is particularly true for geotechnical engineering. The first step in limit state design, which involves the proper identification of potential foundation failure modes, is not always a trivial task (Mortensen 1983). This effort generally requires an appreciation of the interaction between the geologic environment, loading characteristics, and foundation response. Useful generalizations on which limit states are likely to dominate in typical foundation design situations are certainly possible, as in the case of structural design. The role of the geotechnical engineer in making adjustments to these generalizations on the basis of site-specific information is, however, indispensable as well. The need for engineering judgment in the selection of potential limit states is greater in foundation design than in structural design because in-situ conditions must be dealt with “as is” and might contain geologic “surprises”. The danger of downplaying this aspect of limit state design in the fervor33 toward improving the computation and evaluation of safety margins in design can not be overemphasized.The second step in limit state design is to check if any of the selected limit states has been violated. To accomplish this step, it is necessary to use a model that can predict the performance of the system from some measured parameters. In geotechnical engineering, this is not a straightforward task. Consider Figure 1, which is the essence of any type of prediction, geotechnical or otherwise. At one end of the process is the forcing function, which normally consists of loads in conventional foundation engineering. At the other end is the system response, which would be the prediction in an analysis or design situation. Between the forcing function (load) and the system response (prediction) is the model invoked to describe the system behavior, coupled with the properties needed for this particular model. Contrary to popular belief, the quality of geotechnical prediction does not necessarily increase with the level of sophistication in the model (Kulhawy 1992). A more important criterion for the quality of geotechnical prediction is whether the model and property are calibrated together for a specific load and subsequent prediction (Kulhawy 1992, 1994). Reasonable predictions often can be achieved using simple models, even though the type of behavior to be predicted is nominally beyond the capability of the models, as long as there are sufficient data to calibrate these models empirically. However, these models then would be restricted to the specific range of conditions in the calibration process. Extrapolation beyond these conditions can potentially result in erroneous predictions. Ideally, empirical calibration of this type should be applied judiciously by avoiding the use of overly simplistic models. Common examples of such an oversimplification are the sets of extensive correlations between the standard penetration test N-value and practically all types of geotechnical design parameters, as well as several design conditions such as footing settlement and bearing capacity. Although they lack generality, simple models will remain in use for quite some time because of our professional heritage that is replete with, and built upon, empirical correlations. The role of the geotechnical engineer in appreciating the complexities of soil behavior and recognizing the inherent limitations in the simplified models is clearly of considerable importance. The amount of attention paid to the evaluation of safety margins is essentially of little consequence if the engineer were to assess the soil properties incorrectly or to select an inappropriate model for design.Third, the occurrence of each limit state must be shown to be sufficiently improbable. The philosophy of limit state design does not entail a preferred method ofFIG. 1. Components of Geotechnical Prediction (Kulhawy 1994, p. 210)34ensuring safety. Since all engineering quantities (e.g., loads, strengths) are inherentlyuncertain to some extent, a logical approach is to formulate the above problem in thelanguage of probability. The mathematical formalization of this aspect of limit statedesign using probabilistic methods constitutes the main thrust of RBD. Aside fromprobabilistic methods, less formal methods of ensuring safety, such as the partial factorsof safety method (e.g., Danish Geotechnical Institute 1985; Technical Committee onFoundations 1992), have also been used within the framework of limit state design.In summary, the control of safety in geotechnical design is distributed amongmore than one aspect of the design process. Although it is important to consider theeffect of uncertainties in loads and strengths on the safety margins, it is nonetheless onlyone aspect of the problem of ensuring sufficient safety in the design. The other twoaspects, identification of potential failure modes and the methodology of makinggeotechnical predictions, can be of paramount importance, although they may be lessamenable to theoretical analyses.RELIABILITY-BASED DESIGNOverview of Reliability TheoryThe principal difference between RBD and the traditional design approach lies inthe application of reliability theory, which allows uncertainties to be quantified andmanipulated consistently in a manner that is free from self-contradiction. A simpleapplication of reliability theory is shown in Figure 2. Uncertain design quantities, such asthe load (F) and the capacity (Q), are modeled as random variables, while design risk isquantified by the probability of failure (p f). The basic reliability problem is to evaluate p ffrom some pertinent statistics of F and Q, which typically include the mean (m F or m Q)and the standard deviation (s F or s Q). Note that the standard deviation provides aquantitative measure of the magnitude of uncertainty about the mean value.A simple closed-form solution for p f is available if Q and F are both normallydistributed. For this condition, the safety margin (Q - F = M) also is normally distributedwith the following mean (m M) and standard deviation (s M) (e.g., Melchers 1987):m M= m Q - m F (1a) s M2= s Q2 + s F2 (1b)Once the probability distribution of M is known, the probability of failure (p f) can beevaluated as (e.g., Melchers 1987):p f= Prob(Q < F) = Prob(Q - F < 0) = Prob(M < 0) = Φ(- m M/s M) (2)in which Prob(⋅) = probability of an event and Φ(⋅) = standard normal cumulativefunction. Numerical values for Φ(⋅) are tabulated in many standard texts on reliability35FIG. 2. Reliability Assessment for Two Normal Random Variables, Q and Ftheory (e.g., Melchers, 1987). The probability of failure is cumbersome to use when itsvalue becomes very small, and it carries the negative connotation of “failure”. A moreconvenient (and perhaps more palatable) measure of design risk is the reliability index(β), which is defined as:β = - Φ-1(p f) (3) in which Φ-1(⋅) = inverse standard normal cumulative function. Note that β is not a newmeasure of design risk. It simply represents an alternative method for presenting p f on a36more convenient scale. A comparison of Equations 2 and 3 shows that the reliabilityindex for the special case of two normal random variables is given by:β = m M/ s M = (m Q - m F) / (s Q2 + s F2)0.5 (4) The reliability indices for most structural and geotechnical components and systems liebetween 1 and 4, corresponding to probabilities of failure ranging from about 16 to0.003%, as shown in Table 2. Note that p f decreases as β increases, but the variation isnot linear. A proper understanding of these two terms and their interrelationship isessential, because they play a fundamental role in RBD.Simplified RBD for FoundationsOnce a reliability assessment technique is available, the process of RBD wouldinvolve evaluating the probabilities of failure of trial designs until an acceptable targetvalue is achieved. While the approach is rigorous, it is not suitable for designs that areconducted on a routine basis. One of the main reasons for this limitation is that the reliabilityassessment of realistic geotechnical systems is more involved than that shown in Figure 2.The simple closed-form solution given by Equation 2 only is applicable to cases wherein thesafety margin can be expressed as a linear sum of normal random variables. However, thecapacity of most geotechnical systems is more suitably expressed as a nonlinear function ofrandom design soil parameters (e.g., effective stress friction angle, in-situ horizontal stresscoefficient, etc.) that generally are non-normal in nature. To evaluate p f for this generalcase, fairly elaborate numerical procedures, such as the First-Order Reliability Method(FORM), are needed. A description of FORM for geotechnical engineering is givenelsewhere (e.g., Phoon et al. 1995) and is beyond the scope of this paper. At the presenttime, it is safe to say that most geotechnical engineers would feel uncomfortable performingsuch elaborate calculations because of their lack of proficiency in probability theory (Whitman1984).All the existing implementations of RBD are based on a simplified approach thatinvolves the use of multiple-factor formats for checking designs. The three main types ofTABLE 2. Relationship Between Reliability Index (β) and Probability of Failure (p f)Reliability Index, Probability of Failure,βp f = Φ(-β)1.0 0.1591.5 0.06682.0 0.02282.5 0.006213.0 0.001353.5 0.0002334.0 0.0000316Φ(⋅) = standard normal probability distributionNote:37multiple-factor formats are: (a) partial factors of safety, (b) load and resistance factor design(LRFD), and (c) multiple resistance factor design (MRFD). Examples of these designformats are given below for uplift loading of a drilled shaft:ηF n= Q u(c n/γc, φn/γφ) (5a)ηF n =Ψu Q un (5b) ηF n =Ψsu Q sun + Ψtu Q tun + Ψw W (5c)in which η = load factor, F n = nominal design load, Q u = uplift capacity, c n = nominalcohesion, φn = nominal friction angle, γc and γφ = partial factors of safety, Q un = nominaluplift capacity, Q sun = nominal uplift side resistance, Q tun = nominal uplift tip resistance,W = shaft weight, and Ψu, Ψsu, Ψtu, and Ψw = resistance factors. The multiple factors inthe simplified RBD equations are calibrated rigorously using reliability theory to producedesigns that achieve a known level of reliability consistently. Details of the geotechnicalcalibration process are given elsewhere (e.g., Phoon et al. 1995).In principle, any of the above formats or some combinations thereof can be used forcalibration. The selection of an appropriate format is unrelated to reliability analysis.Practical issues, such as simplicity and compatibility with the existing design approach, areimportant considerations that will determine if the simplified RBD approach can gain readyacceptance among practicing engineers. At present, the partial factors of safety format(Equation 5a) has not been used for RBD because of three main shortcomings. First, aunique partial factor of safety can not be assigned to each soil property, because the effect ofits uncertainty on the foundation capacity depends on the specific mathematical function inwhich it is embedded. Second, indiscriminate use of the partial factors of safety can producefactored soil property values that are unrealistic or physically unrealizable. Third, manygeotechnical engineers prefer to assess foundation behavior using realistic parameters, so thatthey would have a physical feel for the problem, rather than perform a hypotheticalcomputation using factored parameters (Duncan et al. 1989; Green 1993; Been et al. 1993).This preference clearly is reflected in the traditional design approach, wherein themodification for uncertainty often is applied to the overall capacity using a global factor ofsafety (FS) as follows:F n = Q un/FS (6)A comparison between Equation 6 and Equations 5b and 5c clearly shows that the LRFDand MRFD formats are compatible with the preferred method of applying safety factors. Infact, the load and resistance factors in the LRFD format can be related easily to the familiarglobal factor of safety as follows:38 FS = η/Ψu (7)The corresponding relationship for the MRFD format is:FS = η/(Ψsu Q sun /Q un + Ψtu Q tun /Q un + Ψw W/Q un ) (8)Although Equation 8 is slightly more complicated, it still is readily amenable to simple calculations. These relationships are very important, because they provide the design engineer with a simple direct means of checking the new design formats against their traditional design experience.RBD EXAMPLEThe development of a rigorous and robust RBD approach for geotechnical design, which also is simple to use, is no trivial task. Since the early 1980s, an extensive research study of this type has been in progress at Cornell University under the sponsorship of the Electric Power Research Institute and has focused on the needs of the electric utility industry. Extensive background information on site characterization, property evaluation, in-situ test correlations, etc. had to be developed as a prelude to the RBD methodology. This work is summarized elsewhere (Spry et al. 1988, Orchant et al. 1988, Filippas et al. 1988, Kulhawy et al. 1992). Building on these and other studies, ultimate and serviceability limit state RBD equations were developed for drilled shafts and spread foundations subjected to a variety of loading modes under both drained and undrained conditions (Phoon et al. 1995). The results of an extensive reliability calibration study for ultimate limit state design of drilled shafts under undrained uplift loading are presented in Tables 3 and 4 and are to be used with Equations 5b (LRFD) and 5c (MRFD). All other limit states, foundation types, loading modes, and drainage conditions addressed have similar types of results, with simple LRFD and MRFDTABLE 3. Undrained Ultimate Uplift Resistance Factors For Drilled ShaftsDesigned Using F 50 = Ψu Q un (Phoon et al. 1995, p. 6-7)Clay COV of s u , (%)Ψu Medium 10 - 30 0.44 mean s u = 25 to 50 kN/m 2 30 - 500.43 50 - 700.42 Stiff 10 - 30 0.43 mean s u = 50 to 100 kN/m 2 30 - 500.41 50 - 700.39 Very Stiff 10 - 30 0.40 mean s u = 100 to 200 kN/m 2 30 - 500.37 50 - 70 0.34 Note: Target reliability index = 3.2TABLE 4. Undrained Uplift Resistance Factors For Drilled Shafts Designed Using F50 = Ψsu Q sun + Ψtu Q tun + Ψw W (Phoon et al. 1995, p. 6-7)Clay COV of s u,ΨsuΨtuΨw(%)Medium 10 - 30 0.44 0.28 0.50 mean s u = 25 to 50 kN/m230 - 50 0.41 0.31 0.5250 - 70 0.38 0.33 0.53Stiff 10 - 30 0.40 0.35 0.56 mean s u = 50 to 100 kN/m230 - 50 0.36 0.37 0.5950 - 70 0.32 0.40 0.62Very Stiff 10 - 30 0.35 0.42 0.66 mean s u = 100 to 200 kN/m230 - 50 0.31 0.48 0.6850 - 70 0.26 0.51 0.72 Note: Target reliability index = 3.2equations and corresponding tables of resistance factors. In these equations, the load factor is taken as unity, while the nominal load is defined as the 50-year return period load (F50), which is typical for electrical transmission line structures. Note that the resistance factors depend on the clay consistency and the coefficient of variation (COV) of the undrained shear strength (s u). The COV is an alternative measure of uncertainty that is defined as the ratio of the standard deviation to the mean. The clay consistency is classified broadly as medium, stiff, and very stiff, with corresponding mean s u values of 25 to 50 kN/m2, 50 to 100 kN/m2, and 100 to 200 kN/m2, respectively. Foundations are designed using these new RBD formats in the same way as in the traditional approach, with the exception that the rigorously-determined resistance factors shown in Tables 3 and 4 are used in place of an empirically-determined factor of safety.Target Reliability IndexBefore applying these resistance factors blindly in design, it is important to examine the target reliability index for which these resistance factors are calibrated. At the present time, there are no simple or straightforward procedures available to produce the “correct” or “true” target reliability index. However, important data that can be used to guide the selection of the target reliability index are the reliability indices implicit in existing designs (Ellingwood et al. 1980). An example of such data for ultimate limit state design of drilled shafts in undrained uplift is shown in Figure 3, in which a typical range of COV s u, mean s u normalized by atmospheric pressure (p a), and global factor of safety are examined for a specific geometry. It can be seen that the reliability indices implicit in existing global factor of safety designs lie in the approximate range of 2.6 to 3.7. A target reliability index of 3.2 is representative of this range. Similar ultimate limit state。
滴水兽题目
滴水兽题目滴水兽是一种设在屋檐的排水管，外观通常以人类或动物的雕像作为装饰。
滴水兽蹲坐高高的屋角，凝视着天空，空洞的眼睛眨也不眨，直到黑夜降临。
到了夜晚，滴水兽聚到一起，聊天，嬉戏，还和人类开玩笑，看着他们落荒而逃……滴水兽蹲坐在高高的屋檐，凝视着天空，空洞的眼睛眨也不眨。
直到黑夜降临。
这时，阴暗的角落有了动静。
滴水兽轻轻移动粗短的双腿，沿着屋檐，慢慢地爬，双眼窥视屋内。
就在眼前，木乃伊躺在又长又窄，像是棺材的盒子里，盒盖上画着船只和人像，颜色暗沉，犹如黑夜。
滴水兽接着爬，惊讶地看着一套套闪亮、坚硬的盔甲。
盔甲露出的眼睛和他们的一样，毫无血色。
如果看累了，飞吧！有翅膀的滴水兽鼓起双翼直飞上天，舔舔星星，用他们布满青苔长长的石舌头。
或是落入沉睡的树林里，在枝丫间摇摆晃荡，荡——呀——荡，凉凉的空气，在他们布满坑洞的石头身躯之间游移。
接着他们飞扑而下，冲到喷泉池底下的暗处，泉水从小天使的嘴里汨汨涌出。
滴水兽弓起背，挤在池边，咕噜咕噜，呼噜呼噜，和从其他屋檐来的朋友一起聊天。
他们聊见到的事情和去过的地方。
他们说太阳高照时，屋角好炎热、好炎热，特别是靠近时钟的地方，还分外吵闹呢！他们抱怨盛夏走得太急；抱怨倾盆而下的大雨流过他们张开的嘴，让秋天的落叶堵了他们的喉咙！还有那些鸟儿，想来就来，想走就走，只留下斑斑点点的印记。
他们用长满苔藓的舌头吸吮雨水，用爪子互相拍打嬉戏。
隆隆的笑声低沉又厚重，因为厚实的石头里没有地方能让笑声翻滚扩散。
一名守卫急忙过来，抬头巡视天空，看看会不会打雷。
他曾经看到滴水兽聚在这儿，也告诉过冰冷大门后的主管，只是主管哼了一声并不相信：“滴水兽？真的？你见鬼了吧！”所以，现在他只看看天空，把恐惧藏在心中！滴水兽鼓动翅膀，用拇指按住易碎的耳朵，露出不屑的样子。
他们不爱人类，是人类把他们造成这样，还把他们放在只有鸽子才到得了的高高突出的屋檐。
他们用力跺脚，他们尖声笑闹，为了要看守卫紧闭双眼，落荒而逃！“嗷——呜！”滴水兽尖叫，接着又叫，“嗷——呜！”他们咧开嘴大声，淘气地笑。
一个晚上的童话故事
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对另一种生灵的呵护与尊重——《滴水兽的夜晚》导读
对另一种生灵的呵护与尊重——《滴水兽的夜晚》导读【经典绘本导读】系列-第463号对另一种生灵的呵护与尊重——《滴水兽的夜晚》导读阅读推广人石头说明：1、这是一个对经典绘本深度解读的公众号，已累计发文在三百篇以上。
2、查找是否有您想阅读的文章，可以从历史消息中输入您想要的书名搜索即可。
3、如果您喜欢本公众号，先点击上边蓝色的“经典绘本导读”再点击“关注”。
4、本导读的宗旨是为亲子阅读而努力、为父母深度掌握绘本精髓而奉献。
一、内容简介滴水兽是一种设在屋檐的排水管，外观通常以人类或动物的雕像作为装饰。
滴水兽蹲坐高高的屋角，凝视着天空，空洞的眼睛眨也不眨，直到黑夜降临。
到了夜晚，滴水兽聚到一起，聊天，嬉戏，还和人类开玩笑，看着他们落荒而逃……快要天亮了，他们又各自回到原来蹲坐的角落，默默地凝视着，空洞的眼睛眨也不眨，直到夜晚来临。
二、绘本信息书名：滴水兽的夜晚文：[美]伊夫·邦廷图：[美]大卫·威斯纳译者：陈鸿铭适读年龄：4-10岁（仅供参考，并非绝对)选题策划：启发绘本馆出版社：河北教育出版社出版日期：2013年06月三、绘本导读【封面】解析：滴水兽是一种设在屋檐的排水管，外观通常以人类或动物的雕像作为装饰。
《滴水兽的夜晚》故事这样说：滴水兽蹲坐高高的屋角，凝视着天空，空洞的眼睛眨也不眨，直到黑夜降临。
到了夜晚，滴水兽聚到一起，聊天，嬉戏，还和人类开玩笑，看着他们落荒而逃……快要天亮了，他们又各自回到原来蹲坐的角落，默默地凝视着，空洞的眼睛眨也不眨，直到夜晚来临。
一个荒诞的故事，却保有人性的关怀。
故事用滴水兽这种怪兽当做封面，就像绘本中提到的那句“盔甲露出的眼睛，和他们一样，毫无血色”，有些恐怖，但这恐怖的感觉却给我一种神秘的吸引力，引领我一直读下去。
我读完的第一感受是有些怪诞，无非就是一个带有科幻色彩的叙事故事。
但孩子眼中的滴水兽又是什么样子的呢？也许是可爱的，也许是诙谐的，儿童天马行空的好奇心是大人无法去揣测的，每个人心中都有一只“滴水兽”，我们又何必执着他到底是什么样子的呢？【环衬】解析：环衬为暗红色，而内页都是黑白色。
五年级语文下册《夜晚的实验》课件3 北京版
终于揭开
秘密蝙的蝠经过
( jīngguò)。这一实验的(b结iā果n 促fú) 使了人们对 超的声研波究飞，行并给人
类带来了巨大的恩惠。
第十页，共34页。
意大利科学家帕斯拉捷习惯晚饭后 到附近的街道上散步。他常常看常到常，(chán 很多蝙蝠(biān fú)灵活的在空中飞来 飞去，却从不会撞到墙壁上。这个现 象引起了他的好奇好：奇蝙(h蝠à(obqiāí)n fú)凭 什么特殊本领在夜空中自由自在的飞 行呢？
序 号
实验目的
实验内容
实验结果
实验结论
1 靠眼睛 蒙住眼睛
2
(yǎn jing)？
靠鼻子？
堵住鼻子
轻盈敏捷
敏捷轻松
不是靠眼睛
不是靠鼻子
3 靠翅膀？ 涂满油漆 没有影响 不是靠翅膀
4 靠耳朵？ 堵住耳朵 东碰西撞 原来是靠听觉
第十四页，共34页。
默读课文(kèwén)2~6自然段，思 考：
2.斯帕拉捷为什么能够揭 开蝙蝠夜行的秘密？
——史迈尔
观察对于儿童(ér tóng)之必不可少， 正如阳光、空气水分对于植物之必不可少 一样。在这里，观察是智慧的最重要能源。
——苏霍姆林斯基
观察与经验和谐地应用(yìngyòng)到生 活上就是智慧。
——冈察洛夫
第三十四页，共34页。
第十九页，共34页。
默读课文8~9小节： 1.在草稿纸上画一画蝙蝠夜行示意图，
同桌间说说蝙蝠夜行的奥秘(àomì)。 2.“巨大的恩惠”指什么？用直线在文中
画出相关语句。 3.读了这篇文章，你受到什么启发？
第二十页，共34页。
原来，蝙蝠靠喉咙发出人耳听不 到的“超声波”，这种声音沿着直线 传播，一碰到物体就像光照到镜子上 那样反射回来。蝙蝠用耳朵接收到这 种“超声波”，就能迅速作出判断， 灵巧地自由飞翔，捕捉(bǔzhuō)食 物。
《狗狗的奇妙夜晚》
《狗狗的奇妙夜晚》夜晚降临，整个世界都安静了下来，可对于我家那调皮的狗狗来说，这才是它奇妙之旅的开始。
我吃完晚饭，像往常一样带着狗狗出去散步。
一路上，它这儿闻闻，那儿嗅嗅，兴奋得不得了。
路过小区花园的时候，它突然停住了，眼睛直勾勾地盯着前方。
我顺着它的目光看去，原来是一只小花猫。
“汪汪汪！”狗狗冲着小花猫叫了起来。
小花猫也不甘示弱，“喵喵喵”地回应着，身上的毛都竖了起来。
我赶紧拉住狗狗，说：“别叫啦，别吓着小猫。
”狗狗哪里肯听，一个劲儿地往前冲，我差点都拉不住它。
这时候，旁边乘凉的大爷大妈们都笑了起来。
王大爷说：“这小狗可真有劲儿，看把你拉得。
”李大妈也跟着说：“可不是嘛，这小猫小狗一见面就掐。
”好不容易把狗狗拉走，它还一步三回头，恋恋不舍的。
走着走着，我们来到了一个小广场。
这里有好多小朋友在玩耍，狗狗一下子就被吸引过去了。
它在小朋友们中间跑来跑去，尾巴摇得像个螺旋桨。
一个小女孩跑过来，摸摸狗狗的头，说：“小狗狗，你真可爱。
”狗狗像是听懂了似的，舔了舔小女孩的手。
这时候，一个小男孩拿着一个飞盘跑过来，说：“我们和狗狗一起玩飞盘吧。
”于是，大家就开始扔飞盘，狗狗欢快地跑来跑去，接住飞盘，然后得意地跑回来，把飞盘放到我们脚边。
玩了好一会儿，我看时间不早了，就带着狗狗回家。
回到家，狗狗累得直接趴在地上，大口大口地喘着气。
我笑着对它说：“今天晚上玩得开心不？”它抬起头，看了我一眼，然后又闭上眼睛，好像在说：“太开心啦，我要睡个好觉。
”这就是狗狗的奇妙夜晚，充满了惊喜和欢乐。