Estimation of the ground motion parameters is extremely important for engineers to make the structures safer and more economical, so it is one of the main issues of Earthquake Engineering. Peak values of the ground motions obtained either from existing records or with the help of attenuation relationships, have been used as a useful parameter to estimate the effect of an earthquake on a specific location. Peak Ground Velocities (PGV) of a ground motion is used extensively in the recent years as a measure of intensity and as the primary source of energy-related analysis of structures. Consequently, PGV values are used to construct emergency response systems like Shake Maps or to determine the deformation demands of structures. Despite the importance of the earthquakes for Turkey, there is a lack of suitable attenuation relationships for velocity developed specifically for the country. The aim of this study is to address this deficiency by developing an attenuation relationship for the Peak Ground Velocities of the chosen database based on the strong ground motion records of Turkey. A database is processed with the established techniques and corrected database for the chosen ground motions is formed. Five different forms of equations that were used in the previous studies are selected to be used as models and by using nonlinear regression analysis, best fitted mathematical relation for attenuation is obtained. The result of this study can be used as an effective tool for seismic hazard assessment studies for Turkey. Besides, being a by-product of this study, a corrected database of strong ground motion recordings of Turkey may prone to be a valuable source for the future researchers.

Regional flood frequency techniques are commonly used to estimate flood quantiles when flood data are unavailable or the record length at an individual gauging station is insufficient for reliable analyses. These methods compensate for limited or unavailable data by pooling data from nearby gauged sites. This requires the delineation of hydrologically homogeneous regions in which the flood regime is sufficiently similar to allow the spatial transfer of information. Therefore, several Regional Flood Frequency Analysis (RFFA) methods are applied to the Ceyhan Basin. Dalyrmple (1960) Method is applied as a common RFFA method used in Turkey. Multivariate statistical techniques which are Stepwise and Nonlinear Regression Analysis are also applied to flood statistics and basin characteristics for gauging stations. Rainfall, Perimeter, Length of Main River, Circularity, Relative Relief, Basin Relief, Hmax, Hmin, Hmean and H&Delta / are the simple additional basin characteristics. Moreover, before the analysis started, stations are clustered according to their basin characteristics by using the combination of Ward&rsquo / s and k-means clustering techniques. At the end of the study, the results are compared considering the Root Mean Squared Errors, Nash-Sutcliffe Efficiency Index and % difference of results. Using additional basin characteristics and making an analysis with multivariate statistical techniques have positive effect for getting accurate results compared to Dalyrmple (1960) Method in Ceyhan Basin. Clustered region data give more accurate results than non-clustered region data. Comparison between clustered region and non-clustered region Q100/Q2.33 reduced variate values for whole region is 3.53, for cluster-2 it is 3.43 and for cluster-3 it is 3.65. This show that clustering has positive effect in the results. Nonlinear Regression Analysis with three clusters give less errors which are 29.54 RMSE and 0.735 Nash-Sutcliffe Index, when compared to other methods in Ceyhan Basin.

Most of the reinforced concrete systems are monolithic. During construction, concrete from the bottom of the deepest beam to the top of slab, is placed at once. Therefore the slab serves as the top flange of the beams. Such a beam is referred to as T-beam. In a floor system made of T-beams, the compressive stress is a maximum over the web, dropping between the webs. The distribution of compressive stress on the flange depends on the relative dimensions of the cross section, span length, support and loading conditions. For simplification, the varying distribution of compressive stress can be replaced by an equivalent uniform distribution. This gives us an effective flange width, which is smaller than the real flange width. In various codes there are recommendations for effective flange width formulas. But these formulas are expressed only in terms of span length or flange and web thicknesses and ignore the other important variables. In this thesis, three-dimensional finite element analysis has been carried out on continuous T-beams under different loading conditions to assess the effective flange width based on displacement criterion. The formulation is based on a combination of the elementary bending theory and the finite element method, accommodating partial interaction in between. The beam spacing, beam span length, total depth of the beam, the web and the flange thicknesses are considered as independent variables. Depending on the type of loading, the numerical value of the moment of inertia of the transformed beam crosssection and hence the effective flange width are calculated. The input data and the finite element displacement results are then used in a nonlinear regression analysis and two explicit design formulas for effective flange width have been derived. Comparisons are made between the proposed formulas and the ACI, Eurocode, TS-500 and BS-8110 code recommendations.

Composite indicators, also called indices, are widely used synthetic measures for ranking and benchmarking alternatives across complex concepts. The aim of constructing a composite indicator is, among other things, to simplify and condense the information of a plurality of underlying indicators. However, to avoid misleading results, it is important to ensure that the construction is performed in a transparent and representative manner. To this end, this thesis aims to aid the construction of the Electricity Supply Resilience Index (ESRI) – which is a novel energy index, developed within the Future Resilient Systems (FRS) programme at the Singapore-ETH Centre (SEC) – by looking at the complementary and fundamental component of index aggregation, namely the weighting of the indicators. Normally, weights are assigned to reflect the relative importance of each indicator, based on stakeholders’ or decision-makers’ preferences. Consequently, the weights are often perceived to be importance coefficients, independent from the dataset under analysis. However, it has recently been shown that the structure of the dataset and correlations between the indicators often have a decisive effect on each indicator’s importance in the index. In fact, their importance rarely coincides with the assigned weights. This phenomenon is sometimes referred to as implicit weights. The aim of this thesis is to assess the implicit weights in the aggregation of ESRI.  For this purpose, a six-step analytical framework, based on a novel variance-based sensitivity analysis approach, is presented and applied to ESRI. The resulting analysis shows that statistical dependencies between ESRI’s underlying indicators have direct implications on the outcome values – the equal weights assigned a-priori do not correspond to an equal influence from each indicator. Furthermore, when attempting to optimise the weights to balance the contribution of each indicator, it is found that this would require a highly unbalanced set of weights and come at the expense of representing the indicators in an effective manner. Thereby, it can be concluded that there are significant dependencies between the indicators and that their correlations need to be accounted for to achieve a balanced and representative index construction. Guided by these findings, this thesis provides three recommendations for improving the statistical representation and conceptual coherence of ESRI. These include: (1) avoid aggregating a negatively correlated indicator – keep it aside, (2) remove a conceptually problematic indicator – revise its construction or conceptual contribution, and (3) aggregate three collinear and conceptually intersecting indicators into a sub-index, prior to aggregation – limit their overrepresentation. By revising the index according to these three recommendations, it is found that ESRI showcases a greater conceptual and statistical coherence. It can thus be concluded that the analytical framework, proposed in this thesis, can aid the development of representative indices. / Kompositindikatorer (eller index) är populära verktyg som ofta används vid rankning och benchmarking av olika alternativ utifrån komplexa koncept. Syftet med att konstruera ett index är, bland annat, att förenkla och sammanfatta informationen från ett flertal underliggande indikatorer. För att undvika missvisande resultat är det därmed viktigt att konstruera index på ett transparent och representativt sätt. Med detta i åtanke, avser denna uppsats att stödja konstruktionen av Electricity Supply Resilience Index (ESRI) – vilket är ett nyutvecklat energiindex, framtaget inom Future Resilient Systems (FRS) programmet på Singapore-ETH Centre (SEC). Detta görs genom att studera ett vanligt fenomen (s.k. implicita vikter) som gör sig gällande i ett av konstruktionsstegen, då de underliggande indikatorerna ska viktas och aggregeras till ett index. I detta steg tilldelas vanligtvis vikter till de enskilda indikatorerna som ska spegla deras relativa betydelse i indexet. Det har dock nyligen visats att datastrukturen och korrelationer mellan indikatorerna har en avgörande påverkan på varje indikators betydelse i indexet, vilket ibland kan vara helt oberoende av vikten de tilldelats. Detta fenomen kallas ibland för implicita vikter, då de ej är explicit tilldelade utan uppkommer från datastrukturen. Syftet med denna uppsatts är således att undersöka de implicita vikterna i aggregationen av ESRI.  För detta ändamål sker en tillämpning och utökning av en nyutvecklad variansbaserad känslighetsanalys, baserad på olinjär regression, för bedömning av implicita vikter i kompositindikatorer. Resultaten från denna analys visar att statistiska beroenden mellan ESRIs underliggande indikatorer har direkt inverkan på varje indikators betydelse i indexet. Detta medför att vikterna ej överensstämmer med indikatorernas betydelse. Följaktligen utförs en vikt-optimering, för att balansera bidraget från varje indikator. Utifrån resultaten av denna vikt-optimering kan det konstateras att det inte är tänkbart att balansera bidraget från varje indikator genom att justera vikterna. Om så görs, skulle det ske på bekostnad av att kunna representera varje indikator på ett effektivt sätt. Därmed kan slutsatsen dras att det finns tydliga beroenden mellan indikatorer och att deras korrelationerna måste tas i hänsyn för att uppnå en balanserad och representativ indexkonstruktion. Utifrån dessa insikter presenteras tre rekommendationer för att förbättra den statistiska representationen och konceptuella samstämmigheten i ESRI. Dessa innefattar: (1) Undvik att aggregera en negativt korrelerad indikator - behåll den vid sidan av, (2) ta bort en konceptuellt problematisk indikator - revidera dess konstruktion eller konceptuella bidrag, och (3) sammanställ tre kollinära och konceptuellt överlappande indikatorer i ett sub-index, före aggregering - begränsa deras överrepresentation. När dessa rekommendationer implementerats står det klart att den reviderade ESRI påvisar en förbättrad konceptuell och statistisks samstämmighet. Därmed kan det fastställas att det analytiska verktyg som presenteras i denna uppsats kan bidra till utvecklingen av representativa index.