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The stability of natural slopes is goverened by many factors, one of which is the porewater pressure. In this study, a sensitivity analysis has been conducted in GeostudioSLOPE/W to investigate the impact of near-surface pore water pressure on thefactor of safety i silt slopes. The study includes five slopes along Ångermanälven,Sweden, which previously have been investigated within the framework of a slopefailure risk mapping of the area conducted by the Swedish Geotechnical Institute,SGI. The near-surface pore water pressure in the slopes has not successfullybeen measured in this area as the slopes are very high and steep. Calculations ofslope stability done previously by Tyréns instead assumed 1) that the pore waterpressure is zero 1m in from the slope face and 2) that it decreases hydrostatically(10 kPa/m) towards the slope face, and these are the parameters studied in thesensitivity analysis. When the pore water pressure is set to zero at the surface, thefactor of safety is reduced by an average of 7 %. Setting the pore water pressure tozero 2mfrom the surace increases the safety factor by 3%on average. A lower thanhydrostatic (7 kPa/m) pore pressure gradient increases the safety factor by on average2 %. A higher than hydrostatic pore water gradient decreases the safety factorby 16% on average. The results verifies that an increase in near-surface pore waterpressure gives a lower factor of safety and decrease in near-surface pore waterpressure leads to a higher factor of safety. The slopes are generally more sensitiveto destabilizing changes of the near-surface pore water pressure than of those stabilizing.Other factors such as vegetation, cohesion, dilatancy and erosion are notconsidered in this study but likely have a considerable effect on the stability. Whenmodelling the influence of near-surface pore water pressure and other parameters,it is recommended to use a FEM program.

Duvbacken wastewater treatment plant in Gävle, Sweden, currently designed for 100,000 person equivalent (P.E.) is looking for a new permit for 120,000 P.E. due to the expected increase of the population in the community. Moreover, the recipient of the plant’s effluent water was characterized as eutrophic in 2009. The plant emissions are regulated regarding seven days biological oxygen demand (BOD7) and total phosphorus (Ptot) emissions. Yet, there is no available computer model to simulate the plant operations and investigate the emissions of the requested permit. However, it was uncertain if the available data would be sufficient for the development of a new model. A model of the plant was eventually developed in BioWin® software under a number of assumptions and simplifications. A sensitivity analysis was conducted and used conversely than in other studies. The sensitivity analysis was conducted for the uncalibrated model in order to indicate its sensitive parameters. The parameters of substrate half saturation constant for ordinary heterotrophic organisms (KS) and phosphorus/acetate release ratio for polyphosphate accumulating organisms (YP/acetic) were finally used for model calibration. Following, the model validation confirmed the correctness of the calibrated model and the ability to develop a basic model under data deficiency. The new model was used to investigate a loading scenario corresponding to 120,000 P.E. where plant emissions that meet the current permits were predicted. Some suggestions proposed were the installation of disc filters in order to further reduce the effluent phosphorus and BOD precipitation in cases of high influent concentrations. In case of the application of a nitrogen (N) permit, the installation of membrane bioreactors and a full-scale chemical P removal was proposed as an alternative that will require a smaller footprint expansion of the plant.

Neural networks are regarded as state of the art within many areas of machine learning, however due to their growing complexity and size, a question regarding their trustability and understandability has been raised. Thus, neural networks are often being considered a "black-box". This has lead to the emersion of evaluation methods trying to decipher these complex networks. Two of these methods, layer-wise relevance propagation (LRP) and sensitivity analysis (SA), are used to generate heatmaps, which presents pixels in the input image that have an impact on the classification. In this report, the aim is to do a usability-analysis by evaluating and comparing these methods to see how they can be used in order to understand a particular classification. The method used in this report is to iteratively distort image regions that were highlighted as important by the two heatmapping-methods. This lead to the findings that distorting essential features of an image according to the LRP heatmaps lead to a decrease in classification score, while distorting inessential features of an image according to the combination of SA and LRP heatmaps lead to an increase in classification score. The results corresponded well with the theory of the heatmapping-methods and lead to the conclusion that a combination of the two evaluation methods is advocated for, to fully understand a particular classification. / Neurala nätverk betraktas som den senaste tekniken i många områden inom maskininlärning, dock har deras pålitlighet och förståelse ifrågasatts på grund av deras växande komplexitet och storlek. Således, blir neurala nätverk ofta sedda som en "svart låda". Detta har lett till utvecklingen  av evalueringsmetoder som ämnar att tolka dessa komplexa nätverk. Två av dessa metoder, layer-wise relevance propagation (LRP) och sensitivity analysis (SA), används för att generera färgdiagram som visar pixlar i indata-bilden som har en påverkan på klassificeringen. I den här rapporten, är målet att göra en användarbarhets-analys genom att utvärdera och jämföra dessa metoder för att se hur de kan användas för att förstå en specifik klassificering. Metoden som används i denna rapport är att iterativt förvränga bilder genom att följa de två färgdiagrams-metoderna. Detta ledde till insikterna att förvrängning av väsentliga delar av bilden, vilket framgick ur LRP färgdiagrammen, tydligt minskade sannolikheten för klassen. Det framkom även att förvrängning av oväsentliga delar, som framgick genom att kombinera SA och LRP färgdiagrammen, ökade sannolikheten för klassen. Resultaten stämde väl överens med teorin och detta ledde till slutsatsen att en kombination av metoderna rekommenderas för att förstå en specifik klassificering.

Res-IRF is an energy-economy model of heating consumption in French dwellings developed at CIRED and calibrated against 2012. It is meant to project the evolution of the building stock and the heating demand by 2050 in response to socio-economic parameters, such as energy price and population, and public policies, such as thermal regulations or incentives for renovation. Res-IRF captures the relevant determinants of household decisions related to energy efficiency improvements and energy demand (sufficiency). The aim of the work presented in this report is to calibrate the model against a past year, to run it from this start date to 2012, and to compare the simulation results with observed data on this period. After an overview of the French residential sector in the last 40 years, this report aims at presenting the model and how it was calibrated against year 1984 and adjusted to the past situation of the building stock. Then, the results of a sensitivity analysis on key parameters of the model are compared to reality and it is discussed how the model can be improved to fit the data better. The main results show that the model accurately replicates the evolution of the building stock until 2012. However, the results do not fit well the data of repartition of heating fuels, especially for fuel oil and natural gas. This may be due to the structure of the model which allows fuel switch only for renovating dwellings; then it could miss possible fuel switches from fuel oil to natural gas without renovation due to the expansion of the natural gas network in France between 1980 and 2000. Furthermore, the actual unit consumption, which is a key output of the model, is well replicated by the model, with an error of 5 to 10%. / Res-IRF är en energi-ekonomi modell av värmebehovet i franska byggnader utvecklad av CIRED och kalibrerad mot data för 2012. Det är avsett att förutsäga utvecklingen för byggnadsbeståndet och värmebehovet fram till 2050 med utgångspunkt från socio-ekonomiska parametrar såsom energipriser och befolkningsmängd, politiska beslut som regleringar rörande uppvärmningssektorn och incitament för renoveringar. Res-IRF fångar upp de relevanta faktorer som påverkar hushållens beslut relaterade till förbättringar av energieffektiviteten och energibehoven. Målet med arbetet som presenteras i denna rapport är att kalibrera modellen mot ett redan passerat år, att köra modellen från startåret till 2012, och att jämföra simuleringsresultaten med verkliga observationer för denna period. Efter en översikt över den franska bostadssektorn de senaste 40 åren, följer i rapporten en presentation av modellen och hur den kalibrerades mot året 1984 och sedan anpassats till det dåvarande läget i byggnadsbeståndet. Därefter jämförs resultaten av en känslighetsanalys av nyckelparametrar i modellen med verkligt utfall och en diskussion följer om hur modellen kan förbättras för att bättre passa verkliga data. Huvudresultaten visar att modellen på ett korrekt sätt avbildar utvecklingen av byggnadsbeståndet fram till 2012. Däremot ger resultaten inte god överensstämmelse vad gäller fördelning av bränslen, speciellt inte fördelningen mellan olja och naturgas. Detta kan bero på modellens struktur, som tillåter bränslebyte bara vid renovering; därmed missar den bränslebyten som görs utan samtidig renovering, som tillkommit på grund av utbyggnaden av naturgasnäten i Frankrike mellan 1980 och 2000. Vidare visar modellen god överensstämmelse vad gäller energitillförsel per enhet, vilket är en nyckelparameter bland resultaten från modellen. Denna parameter predikteras med ett fel av 5 till 10%.

As a latest developed computational code, TRACE is expected to be useful and effective for analyzing the thermal-hydraulic behaviors in design, licensing and safety analysis of nuclear power plant. However, its validity and correctness have to be verified and qualified before its application into industry. Loss-of-coolant accident (LOCA) is a kind of transient thermal hydraulic event which has been emphasized a lot as a most important threat to the safety of the nuclear power plant. In the present study, based on FIX- II LOCA tests, simulation models for the tests of No. 3025, No. 3061 and No. 5052 were developed to validate the TRACE code (version 5.0 patch 2). The simulated transient thermal-hydraulic behaviors during the LOCA tests including the pressure in the primary system, the mass flow rate in certain key parts, and the temperature in the core are compared with experimental data. The simulation results show that TRACE model can well reproduce the transient thermal-hydraulic behaviors under different LOCA situations. In addition, sensitivity analysis are also performed to investigate the influence of particular models and parameters, including counter current flow limitation (CCFL) model, choked flow model , insulator in the steam dome, K-factor in the test section, and pump trip, on the results. The sensitivity analyses show that both the models and parameters have significant influence on the outcome of the model.

Thermal systems often feature composite regions that are mechanically mated. In general, there exists a significant temperature drop across the interface between such regions which may be composed of similar or different materials. The parameter characterizing this temperature drop is the thermal contact resistance, which is defined as the ratio of the temperature drop to the heat flux normal to the interface. The thermal contact resistance is due to roughness effects between mating surfaces which cause certain regions of the mating surfaces to loose contact thereby creating gaps. In these gap regions, the principal modes of heat transfer are conduction across the contacting regions of the interface, conduction or natural convection in the fluid filling the gap regions of the interface, and radiation across the gap surfaces. Moreover, the contact resistance is a function of contact pressure as this can significantly alter the topology of the contact region. The thermal contact resistance is a phenomenologically complex function and can significantly alter prediction of thermal models of complex multi-component structures. Accurate estimates of thermal contact resistances are important in engineering calculations and find application in thermal analysis ranging from relatively simple layered and composite materials to more complex biomaterials. There have been many studies devoted to the theoretical predictions of thermal contact resistance and although general theories have been somewhat successful in predicting thermal contact resistances, most reliable results have been obtained experimentally. This is due to the fact that the nature of thermal contact resistance is quite complex and depends on many parameters including types of mating materials, surface characteristics of the interfacial region such as roughness and hardness, and contact pressure distribution. In experiments, temperatures are measured at a certain number of locations, usually close to the contact surface, and these measurements are used as inputs to a parameter estimation procedure to arrive at the sought-after thermal contact resistance. Most studies seek a single value for the contact resistance, while the resistance may in fact also vary spatially. In this thesis, an inverse problem (IP) is formulated to estimate the spatial variation of the thermal contact resistance along an interface in a two-dimensional configuration. Temperatures measured at discrete locations using embedded sensors appropriately placed in proximity to the interface provide the additional information required to solve the inverse problem. A superposition method serves to determine sensitivity coefficients and provides guidance in the location of the measuring points. Temperature measurements are then used to define a regularized quadratic functional that is minimized to yield the contact resistance between the two mating surfaces. A boundary element method analysis (BEM) provides the temperature field under current estimates of the contact resistance in the solution of the inverse problem when the geometry of interest is not regular, while an analytical solution can be used for regular geometries. Minimization of the IP functional is carried out by the Levenberg-Marquadt method or by a Genetic Algorithm depending on the problem under consideration. The L-curve method of Hansen is used to choose the optimal regularization parameter. A series of numerical examples are provided to demonstrate and validate the approach.

No / The Job Shop Scheduling Problem (JSSP) is a hard combinatorial optimisation problem. This paper presents a heuristic-based Genetic Algorithm (GA) or Hybrid Genetic Algorithm (HGA) with the aim of overcoming the GA deficiency of fine tuning of solution around the optimum, and to achieve optimal or near optimal solutions for benchmark JSSP. The paper also presents a detail GA parameter analysis (also called sensitivity analysis) for a wide range of benchmark problems from JSSP. The findings from the sensitivity analysis or best possible parameter combination are then used in the proposed HGA for optimal or near optimal solutions. The experimental results of the HGA for several benchmark problems are encouraging and show that HGA has achieved optimal solutions for more than 90% of the benchmark problems considered in this paper. The presented results will provide a reference for selection of GA parameters for heuristic-based GAs for JSSP.

Metal foam is a relatively new and potentially revolutionary material that allows for components to be replaced with elements capable of large energy dissipation, or components to be stiffened with elements which will generate significant supplementary energy dissipation when buckling occurs. Metal foams provide a means to explore reconfiguring steel structures to mitigate cross-section buckling in many cases and dramatically increase energy dissipation in all cases. The microstructure of metal foams consists of solid and void phases. These voids have random shape and size. Therefore, randomness ,which is introduced into metal foams during the manufacturing processes, creating more uncertainty in the behavior of metal foams compared to solid steel. Therefore, studying uncertainty in the performance metrics of structures which have metal foams is more crucial than for conventional structures. Therefore, in this study, structural application of metal foams considering material and geometrical uncertainty is presented. This study applies the Sobol' decomposition of a function of many random variables to different problem in structural mechanics. First, the Sobol' decomposition itself is reviewed and extended to cover the case in which the input random variables have Gaussian distribution. Then two examples are given for a polynomial function of 3 random variables and the collapse load of a two story frame. In the structural example, the Sobol' decomposition is used to decompose the variance of the response, the collapse load, into contributions from the individual input variables. This decomposition reveals the relative importance of the individual member yield stresses in determining the collapse load of the frame. In applying the Sobol' decomposition to this structural problem the following issues are addressed: calculation of the components of the Sobol' decomposition by Monte Carlo simulation; the effect of input distribution on the Sobol' decomposition; convergence of estimates of the Sobol' decomposition with sample size using various sampling schemes; the possibility of model reduction guided by the results of the Sobol' decomposition. For the rest of the study the different structural applications of metal foam is investigated. In the first application, it is shown that metal foams have the potential to serve as hysteric dampers in the braces of braced building frames. Using metal foams in the structural braces decreases different dynamic responses such as roof drift, base shear and maximum moment in the columns. Optimum metal foam strengths are different for different earthquakes. In order to use metal foam in the structural braces, metal foams need to have stable cyclic response which might be achievable for metal foams with high relative density. The second application is to improve strength and ductility of a steel tube by filling it with steel foam. Steel tube beams and columns are able to provide significant strength for structures. They have an efficient shape with large second moment of inertia which leads to light elements with high bending strength. Steel foams with high strength to weight ratio are used to fill the steel tube to improves its mechanical behavior. The linear eigenvalue and plastic collapse finite element (FE) analysis are performed on steel foam filled tube under pure compression and three point bending simulation. It is shown that foam improves the maximum strength and the ability of energy absorption of the steel tubes significantly. Different configurations with different volume of steel foam and composite behavior are investigated. It is demonstrated that there are some optimum configurations with more efficient behavior. If composite action between steel foam and steel increases, the strength of the element will improve due to the change of the failure mode from local buckling to yielding. Moreover, the Sobol' decomposition is used to investigate uncertainty in the strength and ductility of the composite tube, including the sensitivity of the strength to input parameters such as the foam density, tube wall thickness, steel properties etc. Monte Carlo simulation is performed on aluminum foam filled tubes under three point bending conditions. The simulation method is nonlinear finite element analysis. Results show that the steel foam properties have a greater effect on ductility of the steel foam filled tube than its strength. Moreover, flexural strength is more sensitive to steel properties than to aluminum foam properties. Finally, the properties of hypothetical structural steel foam C-channels foamed are investigated via simulations. In thin-walled structural members, stability of the walls is the primary driver of structural limit states. Moreover, having a light weight is one of the main advantages of the thin-walled structural members. Therefore, thin-walled structural members made of steel foam exhibit improved strength while maintaining their low weight. Linear eigenvalue, finite strip method (FSM) and plastic collapse FE analysis is used to evaluate the strength and ductility of steel foam C-channels under uniform compression and bending. It is found that replacing steel walls of the C-channel with steel foam walls increases the local buckling resistance and decreases the global buckling resistance of the C-channel. By using the Sobol' decomposition, an optimum configuration for the variable density steel foam C-channel can be found. For high relative density, replacing solid steel of the lips and flange elements with steel foam increases the buckling strength. On the other hand, for low relative density replacing solid steel of the lips and flange elements with steel foam deceases the buckling strength. Moreover, it is shown that buckling strength of the steel foam C-channel is sensitive to the second order Sobol' indices. In summary, it is shown in this research that the metal foams have a great potential to improve different types of structural responses, and there are many promising application for metal foam in civil structures.

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