Beim Kammer-Pfeiler-Abbauverfahren auf flach einfallende Kaliflöze wird die Stabilität der Kammerfirsten von einer Reihe naturgegebener sowie technischer Faktoren beeinflusst, die im Rahmen dieser Arbeit vorgestellt werden. Ausgewählte Einflussfaktoren wie Lage und Eigenschaften von Löserflächen, die Firstankerung, Stoffgesetze für die Salzgesteine sowie die Firstwölbung werden darüber hinaus mit Hilfe numerischer Parametervariationsrechnungen unter Nutzung eines Finite-Differenzen-Programms und einer vergleichenden Ergebnisauswertung untersucht. Der Schwerpunkt liegt dabei auf der Firstankerung, wobei das tatsächliche elasto-plastisch-verfestigende Materialverhalten heutiger Ankerstähle numerisch umgesetzt wird, Ankervorspannkräfte sowie der Ankersetzzeitpunkt variiert werden und der Einfluss unterschiedlicher Teufen auf das Anker- und Firstverhalten herausgearbeitet wird. Durch die Untersuchungen werden die Modellvorstellungen über die Ankerwirkung bei dünnmächtig ausgebildetem unmittelbarem Kammerhangenden gefestigt, es werden Verformungsreserven bei dem verwendeten Ankerstahl „Bergbau – GEWI – Stahl ST 450 / 700“ aufgedeckt und insgesamt zeigen die numerischen Untersuchungsergebnisse, dass die bisher in der Praxis angewandte Verfahrensweise zur Erzielung stabiler Firstverhältnisse in der Gewinnungsphase als geeignet anzusehen ist.

Beim Kammer-Pfeiler-Abbauverfahren auf flach einfallende Kaliflöze wird die Stabilität der Kammerfirsten von einer Reihe naturgegebener sowie technischer Faktoren beeinflusst, die im Rahmen dieser Arbeit vorgestellt werden. Ausgewählte Einflussfaktoren wie Lage und Eigenschaften von Löserflächen, die Firstankerung, Stoffgesetze für die Salzgesteine sowie die Firstwölbung werden darüber hinaus mit Hilfe numerischer Parametervariationsrechnungen unter Nutzung eines Finite-Differenzen-Programms und einer vergleichenden Ergebnisauswertung untersucht. Der Schwerpunkt liegt dabei auf der Firstankerung, wobei das tatsächliche elasto-plastisch-verfestigende Materialverhalten heutiger Ankerstähle numerisch umgesetzt wird, Ankervorspannkräfte sowie der Ankersetzzeitpunkt variiert werden und der Einfluss unterschiedlicher Teufen auf das Anker- und Firstverhalten herausgearbeitet wird. Durch die Untersuchungen werden die Modellvorstellungen über die Ankerwirkung bei dünnmächtig ausgebildetem unmittelbarem Kammerhangenden gefestigt, es werden Verformungsreserven bei dem verwendeten Ankerstahl „Bergbau – GEWI – Stahl ST 450 / 700“ aufgedeckt und insgesamt zeigen die numerischen Untersuchungsergebnisse, dass die bisher in der Praxis angewandte Verfahrensweise zur Erzielung stabiler Firstverhältnisse in der Gewinnungsphase als geeignet anzusehen ist.

Die Rückstandshalden des Kalibergbaus bestehen überwiegend aus Steinsalz. In Abhängigkeit der Stabilität des Untergrundes sowie der Haldenhöhe neigen diese Halden dazu sich plastisch zu verformen. Diese Deformationen gilt es frühzeitig zu erkennen und zu bewerten. Das neue gestaffelte Monitoringkonzept beruht auf langjährigen Erfahrungen, die ebenso kurz vorgestellt werden. / Tailing heaps of potash mining consist predominantly of rock salt. Depending on the stability of the underground, as well as the heights of the heaps, these heaps tend to deform plastically. These deformations must be recognized and assessed at an early stage. The new graduated monitoring concept is based on many years of experience, which are also briefly presented.

Ausbildung mit moderner Software für Festigkeitsberechnungen ist im Ingenieursstudium unabdingbar. Die Studierenden sollten hierbei bevorzugt Methodenkompetenz erwerben, um auf spätere Umgebungs- und Generationswechsel bestmöglich vorbereitet zu sein. Einfach zu erlernende und zu bedienende Programme sind hierfür von Vorteil. Langfristiges Ziel ist es, Wissen zu aktuellen Berechnungs- und Nachweismethoden in die Industrie zu transferieren. Es werden zwei SimSolid-Berechnungsprojekte - Kranhilfsrahmen und Lokomotiv-Dampfkessel – aus Branchen vorgestellt, denen bisher praktikable Möglichkeiten für Festigkeitsberechnungen an großen Baugruppen fehlten. / Training with modern software for strength calculation is essential in engineering studies. The students should preferably acquire methodological competence. In this way, they are best prepared for changes in software version or program environment. Programs that are easy to learn and use are advantageous for this. The goal is to transfer knowledge about current calculation and verification methods to industry. Two SimSolid calculation projects will be presented - Crane Subframe and Locomotive Boiler. Strength calculations on large assemblies have not been common in these industrie sectors up to now.

In geotechnical engineering, the uncertainties such as the variability and uncertainty inherent in the geotechnical properties have caught more and more attentions from researchers and engineers. They have found that a single “Factor of Safety” calculated by traditional deterministic analyses methods can not represent the slope stability exactly. Recently in order to provide a more rational mathematical framework to incorporate different types of uncertainties in the slope stability estimation, reliability analyses and non-deterministic methods, which include probabilistic and non probabilistic (imprecise methods) methods, have been applied widely. In short, the slope non-deterministic analysis is to combine the probabilistic analysis or non probabilistic analysis with the deterministic slope stability analysis. It cannot be regarded as a completely new slope stability analysis method, but just an extension of the slope deterministic analysis. The slope failure probability calculated by slope non-deterministic analysis is a kind of complement of safety factor. Therefore, the accuracy of non deterministic analysis is not only depended on a suitable probabilistic or non probabilistic analysis method selected, but also on a more rigorous deterministic analysis method or geological model adopted. In this thesis, reliability concepts have been reviewed first, and some typical non-deterministic methods, including Monte Carlo Simulation (MCS), First Order Reliability Method (FORM), Point Estimate Method (PEM) and Random Set Theory (RSM), have been described and successfully applied to the slope stability analysis based on a numerical simulation method-Strength Reduction Method (SRM). All of the processes have been performed in a commercial finite difference code FLAC and a distinct element code UDEC. First of all, as the fundamental of slope reliability analysis, the deterministic numerical simulation method has been improved. This method has a higher accuracy than the conventional limit equilibrium methods, because of the reason that the constitutive relationship of soil is considered, and fewer assumptions on boundary conditions of slope model are necessary. However, the construction of slope numerical models, particularly for the large and complicated models has always been very difficult and it has become an obstacle for application of numerical simulation method. In this study, the excellent spatial analysis function of Geographic Information System (GIS) technique has been introduced to help numerical modeling of the slope. In the process of modeling, the topographic map of slope has been gridded using GIS software, and then the GIS data was transformed into FLAC smoothly through the program built-in language FISH. At last, the feasibility and high efficiency of this technique has been illustrated through a case study-Xuecheng slope, and both 2D and 3D models have been investigated. Subsequently, three most widely used probabilistic analyses methods, Monte Carlo Simulation, First Order Reliability Method and Point Estimate Method applied with Strength Reduction Method have been studied. Monte Carlo Simulation which needs to repeat thousands of deterministic analysis is the most accurate probabilistic method. However it is too time consuming for practical applications, especially when it is combined with numerical simulation method. For reducing the computation effort, a simplified Monte Carlo Simulation-Strength Reduction Method (MCS-SRM) has been developed in this study. This method has estimated the probable failure of slope and calculated the mean value of safety factor by means of soil parameters first, and then calculated the variance of safety factor and reliability of slope according to the assumed probability density function of safety factor. Case studies have confirmed that this method can reduce about 4/5 of time compared with traditional MCS-SRM, and maintain almost the same accuracy. First Order Reliability Method is an approximate method which is based on the Taylor\'s series expansion of performance function. The closed form solution of the partial derivatives of the performance function is necessary to calculate the mean and standard deviation of safety factor. However, there is no explicit performance function in numerical simulation method, so the derivative expressions have been replaced with equivalent difference quotients to solve the differential quotients approximately in this study. Point Estimate Method is also an approximate method involved even fewer calculations than FORM. In the present study, it has been integrated with Strength Reduction Method directly. Another important observation referred to the correlation between the soil parameters cohesion and friction angle. Some authors have found a negative correlation between cohesion and friction angle of soil on the basis of experimental data. However, few slope probabilistic studies are found to consider this negative correlation between soil parameters in literatures. In this thesis, the influence of this correlation on slope probability of failure has been investigated based on numerical simulation method. It was found that a negative correlation considered in the cohesion and friction angle of soil can reduce the variability of safety factor and failure probability of slope, thus increasing the reliability of results. Besides inter-correlation of soil parameters, these are always auto-correlated in space, which is described as spatial variability. For the reason that knowledge on this character is rather limited in literature, it is ignored in geotechnical engineering by most researchers and engineers. In this thesis, the random field method has been introduced in slope numerical simulation to simulate the spatial variability structure, and a numerical procedure for a probabilistic slope stability analysis based on Monte Carlo simulation was presented. The soil properties such as cohesion and friction angle were discretized to continuous random fields based on local averaging method. In the case study, both stationary and non-stationary random fields have been investigated, and the influence of spatial variability and averaging domain on the convergence of numerical simulation and probability of failure was studied. In rock medium, the structure faces have very important influence on the slope stability, and the rock material can be modeled as the combination of rigid or deformable blocks with joints in distinct element method. Therefore, much more input parameters like strength of joints are required to input the rock slope model, which increase the uncertainty of the results of numerical model. Furthermore, because of the limitations of the current laboratory and in-site testes, there is always lack of exact values of geotechnical parameters from rock material, even the probability distribution of these variables. Most of time, engineers can only estimate the interval of these variables from the limit testes or the expertise’s experience. In this study, to assess the reliability of the rock slope, a Random Set Distinct Element Method (RS-DEM) has been developed through coupling of Random Set Theory and Distinct Element Method, and applied in a rock slope in Sichuan province China.

In geotechnical engineering, the uncertainties such as the variability and uncertainty inherent in the geotechnical properties have caught more and more attentions from researchers and engineers. They have found that a single “Factor of Safety” calculated by traditional deterministic analyses methods can not represent the slope stability exactly. Recently in order to provide a more rational mathematical framework to incorporate different types of uncertainties in the slope stability estimation, reliability analyses and non-deterministic methods, which include probabilistic and non probabilistic (imprecise methods) methods, have been applied widely. In short, the slope non-deterministic analysis is to combine the probabilistic analysis or non probabilistic analysis with the deterministic slope stability analysis. It cannot be regarded as a completely new slope stability analysis method, but just an extension of the slope deterministic analysis. The slope failure probability calculated by slope non-deterministic analysis is a kind of complement of safety factor. Therefore, the accuracy of non deterministic analysis is not only depended on a suitable probabilistic or non probabilistic analysis method selected, but also on a more rigorous deterministic analysis method or geological model adopted. In this thesis, reliability concepts have been reviewed first, and some typical non-deterministic methods, including Monte Carlo Simulation (MCS), First Order Reliability Method (FORM), Point Estimate Method (PEM) and Random Set Theory (RSM), have been described and successfully applied to the slope stability analysis based on a numerical simulation method-Strength Reduction Method (SRM). All of the processes have been performed in a commercial finite difference code FLAC and a distinct element code UDEC. First of all, as the fundamental of slope reliability analysis, the deterministic numerical simulation method has been improved. This method has a higher accuracy than the conventional limit equilibrium methods, because of the reason that the constitutive relationship of soil is considered, and fewer assumptions on boundary conditions of slope model are necessary. However, the construction of slope numerical models, particularly for the large and complicated models has always been very difficult and it has become an obstacle for application of numerical simulation method. In this study, the excellent spatial analysis function of Geographic Information System (GIS) technique has been introduced to help numerical modeling of the slope. In the process of modeling, the topographic map of slope has been gridded using GIS software, and then the GIS data was transformed into FLAC smoothly through the program built-in language FISH. At last, the feasibility and high efficiency of this technique has been illustrated through a case study-Xuecheng slope, and both 2D and 3D models have been investigated. Subsequently, three most widely used probabilistic analyses methods, Monte Carlo Simulation, First Order Reliability Method and Point Estimate Method applied with Strength Reduction Method have been studied. Monte Carlo Simulation which needs to repeat thousands of deterministic analysis is the most accurate probabilistic method. However it is too time consuming for practical applications, especially when it is combined with numerical simulation method. For reducing the computation effort, a simplified Monte Carlo Simulation-Strength Reduction Method (MCS-SRM) has been developed in this study. This method has estimated the probable failure of slope and calculated the mean value of safety factor by means of soil parameters first, and then calculated the variance of safety factor and reliability of slope according to the assumed probability density function of safety factor. Case studies have confirmed that this method can reduce about 4/5 of time compared with traditional MCS-SRM, and maintain almost the same accuracy. First Order Reliability Method is an approximate method which is based on the Taylor\'s series expansion of performance function. The closed form solution of the partial derivatives of the performance function is necessary to calculate the mean and standard deviation of safety factor. However, there is no explicit performance function in numerical simulation method, so the derivative expressions have been replaced with equivalent difference quotients to solve the differential quotients approximately in this study. Point Estimate Method is also an approximate method involved even fewer calculations than FORM. In the present study, it has been integrated with Strength Reduction Method directly. Another important observation referred to the correlation between the soil parameters cohesion and friction angle. Some authors have found a negative correlation between cohesion and friction angle of soil on the basis of experimental data. However, few slope probabilistic studies are found to consider this negative correlation between soil parameters in literatures. In this thesis, the influence of this correlation on slope probability of failure has been investigated based on numerical simulation method. It was found that a negative correlation considered in the cohesion and friction angle of soil can reduce the variability of safety factor and failure probability of slope, thus increasing the reliability of results. Besides inter-correlation of soil parameters, these are always auto-correlated in space, which is described as spatial variability. For the reason that knowledge on this character is rather limited in literature, it is ignored in geotechnical engineering by most researchers and engineers. In this thesis, the random field method has been introduced in slope numerical simulation to simulate the spatial variability structure, and a numerical procedure for a probabilistic slope stability analysis based on Monte Carlo simulation was presented. The soil properties such as cohesion and friction angle were discretized to continuous random fields based on local averaging method. In the case study, both stationary and non-stationary random fields have been investigated, and the influence of spatial variability and averaging domain on the convergence of numerical simulation and probability of failure was studied. In rock medium, the structure faces have very important influence on the slope stability, and the rock material can be modeled as the combination of rigid or deformable blocks with joints in distinct element method. Therefore, much more input parameters like strength of joints are required to input the rock slope model, which increase the uncertainty of the results of numerical model. Furthermore, because of the limitations of the current laboratory and in-site testes, there is always lack of exact values of geotechnical parameters from rock material, even the probability distribution of these variables. Most of time, engineers can only estimate the interval of these variables from the limit testes or the expertise’s experience. In this study, to assess the reliability of the rock slope, a Random Set Distinct Element Method (RS-DEM) has been developed through coupling of Random Set Theory and Distinct Element Method, and applied in a rock slope in Sichuan province China.