Civil Engineering

This article examines five common misunderstandings about case-study research: (a) theoretical knowledge is more valuable than practical knowledge; (b) one cannot generalize from a single case, therefore, the single-case study cannot contribute to scientific development; (c) the case study is most useful for generating hypotheses, whereas other methods are more suitable for hypotheses testing and theory building; (d) the case study contains a bias toward verification; and (e) it is often difficult to summarize specific case studies. This article explains and corrects these misunderstandings one by one and concludes with the Kuhnian insight that a scientific discipline without a large number of thoroughly executed case studies is a discipline without systematic production of exemplars, and a discipline without exemplars is an ineffective one. Social science may be strengthened by the execution of a greater number of good case studies.

We use multifractal detrended fluctuation analysis (MF-DFA), to See query 1 study sunspot number fluctuations. The result of the MF-DFA shows that there are three crossover timescales in the fluctuation function. We discuss how the existence of the crossover timescales is related to a sinusoidal trend. Using Fourier detrended fluctuation analysis, the sinusoidal trend is eliminated. The Hurst exponent of the time series without the sinusoidal trend is $0.12\pm 0.01$. Also we find that these fluctuations have multifractal nature. Comparing the MF-DFA results for the remaining data set to those for shuffled and surrogate series, we conclude that its multifractal nature is almost entirely due to long range correlations.

with a power function relating soil moisture and hydraulic conductivitymis used to derive a formula for the wetting front suction required by the Green-Ampt equation. Representative parameters for the moisture charac- teristic, the wetting front suction, and the sorptivity, a parameter in the infiltration equation derived by Philip (1957), are computed by using the desorption data of Holtan et al.

The Shear Strength of Rock Joints in Theory and Practice The paper describes an empirical law of friction for rock joints which can be used both for extrapolating and predicting shear strength data. The equation is based on three index parameters; the joint roughness coefficientJRC, the joint wall compressive strengthJCS, and the residual friction angleφ r . All these index values can be measured in the laboratory. They can also be measured in the field. Index tests and subsequent shear box tests on more than 100 joint samples have demonstrated thatφ r can be estimated to within ± 1° for any one of the eight rock types investigated. The mean value of the peak shear strength angle (arctanτ/σ n ) for the same 100 joints was estimated to within 1/2°. The exceptionally close prediction of peak strength is made possible by performing self-weight (low stress) sliding tests on blocks with throughgoing joints. The total friction angle (arctanτ/σ n ) at which sliding occurs provides an estimate of the joint roughness coefficientJRC. The latter is constant over a range of effective normal stress of at least four orders of magnitude. However, it is found that bothJRC andJCS reduce with increasing joint length. Increasing the length of joint therefore reduces not only the peak shear strength, but also the peak dilation angle and the peak shear stiffness. These important scale effects can be predicted at a fraction of the cost of performing large scale in situ direct shear tests. Die Scherfestigkeit von Kluftflächen in Theorie und Praxis Zur Ermittlung der Reibungswerte in Kluftflächen wird ein empirisches Gesetz beschrieben, das sowohl das Extrapolieren als auch das Voraussagen von Scherfestigkeitszahlen ermöglicht. Die Gleichung ist auf drei Indexzahlen gegründet: Den Rauhigkeitskoeffizienten der KluftJRC (Joint Roughness Coeff.), die Druckfestigkeit des Felses der KluftwändeJCS (Joint Wall Compression Strength) und der residuelle Reibungswinkel der Trennflächeφ r . Die Indexzahlen können alle im Laboratorium bestimmt oder am Ort gemessen werden. Bestimmung von Indexzahlen mit nachfolgenden Prüfungen im Scherapparat von mehr als 100 Kluftproben haben erwiesen, daß für jede beliebige der acht untersuchten Gesteinsarten der Reibungswinkelφ r auf ± 1° genau geschätzt werden kann. Der Durchschnittswert des Reibungswinkels (arc tan (τ/σ n ) der Höchstscherfestigkeit wurde für dieselben 100 Klüfte auf ± 1/2° genau geschätzt. Die besonders genaue Vorausschätzung der Höchstscherfestigkeit ist durch Eigengewicht-Gleitversuche (niedrige Spannungen) auf Gesteinsblöcken mit durchgehenden Trennflächen ermöglicht. Der totale Reibungswinkel (arc tanτ/σ n ), bei dem das Gleiten eintrifft, ergibt eine Abschätzung des Rauhigkeitskoeffizienten der KluftJRC. Der Rauhigkeitskoeffizient bleibt über einen Normal-Spannungsbereich von mindestens vier Größenanordnungen konstant. Die IndexzahlenJRC (Rauhigkeitskoeffizient) undJCS (Druckfestigkeitskoeffizient) reduzieren sich aber bei zunehmenden Kluftlängen. Bei zunehmender Kluftflächengröße nehmen nicht nur die Höchstscherfestigkeit, sondern auch der zugehörige Dilatanzwinkel und die Schubsteifigkeit ab. Diese wichtigen Einflüsse der geometrischen Abmessungen können geschätzt und zahlenmäßig erfaßt werden, und zwar mit Kosten, die nur einen Bruchteil von denen betragen, die für große, direkte Scherversuche in situ erforderlich wären. La résistance au cisaillement des joints de roches en théorie et en pratique Le rapport traite d'une loi empirique du frottement dans les joints de roches, loi pouvant être utilisée tant pour l'extrapolation que pour la prédiction de données relatives à la résistance au cisaillement. L'équation est basée sur trois indices de paramètres: coefficient de rugosité du joint (joint roughness coefficient —JRC), résistance de la paroi à la compression (joint wall compressive strength —JCS), et l'angle de frottement résiduelφ r . Toutes ces valeurs d'indice peuvent être mesurées au laboratoire. Elles peuvent aussi l'être in situ. Des tests d'indice, avec ensuite des tests en boîte de cisaillement, sur plus de 100 échantillons de joints, ont permis de constater que, pour n'importe quel des huit types de roche étudiés, l'angle de frottementφ r peut être évalué avec une précision de ± 1°. La valeur moyenne de l'angle maximum de la résistance au cisaillement (arctanτ/σ n ) pour les mêmes 100 joints fut évaluée avec une précision de 1/2°. La prédiction particulièrement précise de la résistance maximum au cisaillement est rendue possible par la réalisation d'essais de glissement dits “de poids propre” (faible contrainte) sur des blocs à joints passant de part en part. L'angle de frottement total (arctanτ/σ n ) auquel le glissement se manifeste, fournite une estimation du coefficient de rugosité du joint,JRC. Ce dernier est constant à l'intérieur d'une plage de tensions normales effectives d'au moins quatre ordres de grandeur. Cependant, on a trouvé que les indicesJRC (rugosité) comme ceuxJCS (compression) diminuent quand la longueur du joint augmente. Ainsi, si la langueur du joint augmente, cela réduira non seulement la résistance maximum au cisaillement, mais aussi l'angle maximum de dilatation et la rigidité maximum de cisaillement. Ces importants effets d'échelle peuvent être prédits, et ce à des coûts ne représentant qu'une fraction de ceux liés à des tests directs de cisaillement effectués in situ.

This study quantifies the green, blue and grey water footprint of global crop production in a spatially-explicit way for the period 1996-2005. The assessment improves upon earlier research by taking a high-resolution approach, estimating the water footprint of 126 crops at a 5 by 5 arc minute grid. We have used a grid-based dynamic water balance model to calculate crop water use over time, with a time step of one day. The model takes into account the daily soil water balance and climatic conditions for each grid cell. In addition, the water pollution associated with the use of nitrogen fertilizer in crop production is estimated for each grid cell. The crop evapotranspiration of additional 20 minor crops is calculated with the CROPWAT model. In addition, we have calculated the water footprint of more than two hundred derived crop products, including various flours, beverages, fibres and biofuels. We have used the water footprint assessment framework as in the guideline of the Water Footprint Network. Considering the water footprints of primary crops, we see that the global average water footprint per ton of crop increases from sugar crops (roughly 200 m3 ton-1), vegetables (300 m3 ton-1), roots and tubers (400 m3 ton-1), fruits (1000 m3 ton-1), cereals (1600 m3 ton-1), oil crops (2400 m3 ton-1) to pulses (4000 m3 ton-1). The water footprint varies, however, across different crops per crop category and per production region as well. Besides, if one considers the water footprint per kcal, the picture changes as well. When considered per ton of product, commodities with relatively large water footprints are: coffee, tea, cocoa, tobacco, spices, nuts, rubber and fibres. The analysis of water footprints of different biofuels shows that bio-ethanol has a lower water footprint (in m3 GJ-1) than biodiesel, which supports earlier analyses. The crop used matters significantly as well: the global average water footprint of bio-ethanol based on sugar beet amounts to 51 m3 GJ-1, while this is 121 m3 GJ-1 for maize. The global water footprint related to crop production in the period 1996-2005 was 7404 billion cubic meters per year (78 % green, 12 % blue, 10 % grey). A large total water footprint was calculated for wheat (1087 Gm3 yr-1), rice (992 Gm3 yr-1) and maize (770 Gm3 yr-1). Wheat and rice have the largest blue water footprints, together accounting for 45 % of the global blue water footprint. At country level, the total water footprint was largest for India (1047 Gm3 yr-1), China (967 Gm3 yr-1) and the USA (826 Gm3 yr-1). A relatively large total blue water footprint as a result of crop production is observed in the Indus river basin (117 Gm3 yr-1) and the Ganges river basin (108 Gm3 yr-1). The two basins together account for 25 % of the blue water footprint related to global crop production. Globally, rain-fed agriculture has a water footprint of 5173 Gm3 yr-1 (91 % green, 9 % grey); irrigated agriculture has a water footprint of 2230 Gm3 yr-1 (48 % green, 40 % blue, 12 % grey).

The accuracy of the predictions of distributed hydrological models must depend in part on the proper specification of flow pathways. This paper examines some of the problems of deriving flow pathways from raster digital terrain data in the context of hydrological predictions using TOPMODEL. Distributed moisture status is predicted in TOPMODEL on the basis of spatial indices that depend on flow path definition. The sensitivity of this index to flow path algorithm and grid size is examined for the case where the surface topography is a good indicator of local hydraulic gradients. A strategy for the case where downslope subsurface flow pathways may deviate from those indicated by the surface topography is described with an example application.

This article presents results from the first statistically significant study of cost escalation in transportation infrastructure projects. Based on a sample of 258 transportation infrastructure projects worth US$90 billion and representing different project types, geographical regions, and historical periods, it is found with overwhelming statistical significance that the cost estimates used to decide whether such projects should be built are highly and systematically misleading. Underestimation cannot be explained by error and is best explained by strategic misrepresentation, that is, lying. The policy implications are clear: legislators, administrators, investors, media representatives, and members of the public who value honest numbers should not trust cost estimates and cost-benefit analyses produced by project promoters and their analysts.