Journal of Engineering Geology

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A new approach is suggested to determine the permanent deformation of slope under seismic loading based on the horizontal slices method and limit equilibrium analysis. A comparison of the analytical results obtained from the proposed method for 3 sample slopes with those of previous research results is performed. The analytical method presented can be used to calculate yield acceleration, seismic coefficient of horizontal acceleration, permanent deformation and angle of failure wedge for slopes. Also, the stability analysis can be performed by proposed method. It was concluded that the horizontal slices method by analytical procedure proposed reliably calculates the permanent deformation of slopes.

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With respect to development of underground structures and their high construction costs in intra- and inter-municipal transportation, it is necessary to study the stability of such structures. In this research, tunnel stability of Mashhad Metro line 2 with 17 km length is studied. First, the type of sediments and geotechnical properties in Mashhad Plain are investigated. The SPT profiles were prepared using Rock Work 2006 software. The soil classification tests and XRD results show that the soils in this line are mostly clay such as Illite and Kaolinite types. Afterwards, because most of the soils in this line are characterized as fine grained, the ground settlement using PLAXIS V8 software was performed. According to the numerical modeling and the depth of tunnel, the optimum depth for tunnel was determined.

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In tunnelling in soil mass, in groundwater existing mode, liquefaction, elastic displacements and settlement in soils upon the tunnel, are the risks may attack the excavated underground space stability. In this case study that were performed on second line of Mashhad city subway route, information catched from Standard Penetration Test, in situ and laboratorial tests, were used to optimum numerical values search for soil engineering parameters that could optimize the TBM stationing level. In order to this goal attaining, intelligent, numerical and probabilistic methods were used and the reliability of intelligent and numerical methods with the Safety Factors of tunnel stability, investigated simultaneously. The results were denoting the accordance of intelligent models such as Genetic Algorithm (GA) and Multi objective Genetic Algorithm with Finite Element model's output. So these models could be complement of each others in planning and designing of tunnels and using of them advised in tunneling and excavations.

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The increasing rate of construction activities in urban areas is accompanied by excavation in the vicinity of existing structures and urban utilities. This issue has highlighted the importance of constructing protecting structures in order to control displacements and prevent damage to structures and their neighboring area. Among the important widely used wall stabilization techniques, one can name nailing and grouted anchors. However, these methods suffer some drawbacks such as annoying noise and vibration during the drilling, implementation difficulties below the water table, grouting problem, installation of strands and bars in the borehole in porous and collapse soils, and long curing time for the grout of post-tension anchors. Since the helical anchor method lacks many of the mentioned problems, it is now widely used in many applications.
In the present work, a laboratory model of helical anchor stabilized wall is presented and evaluated. For this purpose, four types of anchors at 20° back slope are designed in a sandy soil and the effect of helix configuration (in term of its diameter and number of blades) is investigated. Considering the laboratory scale of the designed model, the results obtained using helical anchor were compared with numerical results of soil nailing wall by applying the particle image velocimetry (PIV) analyses.
Material and methods
The test box designed in this work is made of a metal plate with a thickness, length, width, and depth of 1.5 mm, 100 cm, 60 cm, and 30 cm, respectively, and a Plexiglas in its opposing side with a thickness of 50 mm. The soil used in the experiments was the dry sand of Soufian region in east Azerbaijan province of Iran. The soil is classified as SP according to USCS classification. The helical anchors were fabricated by welding the helical pitches to a metal shaft. The end part of the shafts is screw threaded such that to fasten a bolt to them.
To start the experiment, the empty box was completely cleaned using the detergents to remove any pollution or soil on the Plexiglas and metal surface. Afterward, the sandy soil was poured on the wall floor and the facing was placed inside the box vertically. Again, the sandy soil was poured from both sides of the facing up to the installation height of the helices. Helices were installed in the assigned holes and their angle was adjusted through the pre-fabricated stencils. The soil height was increased up to the next row assigned for helices installation. These steps were repeated until reach the wall crest. After preparation of the physical model, its behavior during the preparation must be modeled. We first filled both sides of the model and then modeled the stability behavior of the helical anchor wall through excavating its facing opposed side. Overall, the wall was built through eight excavation steps.
Results and discussion
The maximum displacement is related to the anchor type 1, which does not have enough bearing capacity under surcharge conditions. By changing the anchor type and increasing the number of helices, shear strains and their expansion in the wall back decline. The decrease in displacement rate by changing the anchor from type 1 to type 2 is 18%, which is due to the low bearing capacity of type 2 anchor compared to the type 1 anchor. Increasing the number of pitches from one to two (changing the type 1 anchor to type 3 anchor) showed a considerable decrease (i.e., 43%) in displacement rate. Increasing the number of pitches from 1 to 3 (changing the anchor from type 1 to type 3) resulted in a 62% decrease in wall crest displacement. This displacement decrease rate seems to decline with an increase in the number of helixes.
The displacement rate for all four anchors is almost similar in two excavation steps, which probably is because of the need for displacement for activation of the anchors. One strategy to deal this issue in the sensitive projects and control the displacement is to apply post-tension helical anchors. Then, in stages 4 to 6, the displacement was almost constant due to four main reasons including wall rigidity, the presence of reinforcements, formation of pre-step displacement-induced tension force, and enough capacity of anchors to face with more displacement. In stages 6 to 8, type 1 and 2 anchors showed growing displacements due to the reduction and ending the wall rigidity and lower bearing capacity. In type 3 and 4 anchors, the maximum displacement was related to 4 initial stages. In type 1 and 2 anchors, which have two helical plates, almost a similar behavior was observed until stage 6 of excavation, but eventually type 3 anchors showed better performance because of higher bearing capacity to overall displacement.
Conclusion
In the present study, a physical model was designed to investigate the effect of helical anchors’ geometry on displacement rate of helical anchor wall and compare it with a nail wall. Overall, comparing the results obtained by conducting these experiments on a helical anchor stabilized wall and a nail wall revealed that:
- Wall crest displacement is affected by the diameter and number of helices and decreases by an increase in bearing capacity.
- The increase in the number of pitches from one to two (single-pitch to double-pitch anchor) has a higher effect on displacement control compared to the case of changing the double-pitch to triple-pitch anchor. So, it can be stated that a further increase in the number of anchor pitches results in a declined performance of the anchors.
- All anchors need a slight displacement for activation. This issue cannot be resolved by changing the type of helical anchors. Hence, when the displacement required for activation of the anchors exceeds the allowable wall crest displacement, use of post-tensioned helical anchors is recommended.
- A comparison between nailing and helical anchor results revealed that the relative density of the wall stabilized with the helical anchor is less than that of the nail wall; and wall crest displacement in the helical anchor wall was very lower than that of nail wall. Thus, the helical anchor wall stabilization is preferred when other economic and technical requirements are met.

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Introduction
Drill-bit selection is one of the most important aspects of well planning due to the bearing it can have on the overall cost of the well. Bit selection in conventional and slightly inclined wells is a very delicate and complex process. In high angle and horizontal wells it is even more difficult. Historically, drilling engineers have selected bits on the basis of what has been worked well in the area and what has been determined to have the lowest cost run from offset bit records. Often the best bit records were not available for evaluation, because the best bit may not yet have been run, may have been run by a competitor or the engineer was new to the area. As a result the bit program was generally developed by trial and error and at significant additional costs for a large number of wells. In most cases the optimum program was never reached because there was nothing to predict that a bit selection change could further reduce the cost of the well. In this study, an alternative solution approaches using the concept of the power of data mining algorithms to solve the optimum bit program for a given field is proposed.
Material and methods
It has been considered an offset well to be drilled outside the known boundaries of a known field. For this purpose, the seventh well (X-7) of the same field was used as a verification point. The data was trained using the well log and rock bit data of six wells located in the field and the real well log data of well 7 was input as unknown data. These depths are selected based on reported rock bit program. When compared to the real data, it could be observed that the models (adaptive neuro fuzzy inference system, K-nearest neighbors, decision tree, Bayesian classification theory and association rules) estimates the formation hardness accurately. This minor discrepancy was also present with the company’s suggested rock bit program, which was based on the previous wells’ rock bit data.
Results and discussion
In this paper, data mining algorithms for optimum rock bit program estimation is proposed. The accuracy and efficiency of the developed data mining algorithms (adaptive neuro fuzzy inference system, K-nearest neighbors, decision tree, Bayesian classification theory and association rules) that requires sonic and neutron log data input was tested for several real and synthetic cases. In the case of a development? well to be drilled outside the known boundaries of a field the model estimated rock bits with properties that consider the formation hardness correctly but slightly underestimated further rock bit details. The models also produced reasonable rock bit programs for an advance well to be drilled within the known boundaries of a field and a wildcat well drilled in a nearby field with similar rock properties to the training field. Thus it was concluded that the developed adaptive neuro fuzzy inference system is suitable as a front-end system for rock bit selection that could help engineers in decision-making analysis.
Conclusion
Optimum bit selection is one of the important issues in drilling engineering. Usually, optimum bit selection is determined by the lowest cost per foot and is a function of bit cost and performance as well as penetration rate. Conventional optimum rock bit selection program involves development of computer programs created from mathematical models along with information from previously drilled wells in the same area. Based on the data gathered on a daily basis for each well drilled, the optimum drilling program may be modified and revised as unexpected problems arose. The approaches in this study uses the power of data mining algorithms to solve the optimum bit selection problem. In order to achieve this goal, adaptive neuro fuzzy inference system, K-nearest neighbors, decision tree, Bayesian classification theory and association rules were developed by training the models using real rock bit data for several wells in a carbonated field. The training of the basic models involved use of both gamma ray and sonic log data. After that the models were tested using various drilling scenarios in different lithologic units. It was observed that the adaptive neuro fuzzy inference system model has provided satisfactory results.
 
 

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Introduction
Stone column installation method is one of the popular methods of ground improvement. Several studies have been performed to investigate the behavior of stone columns under vertical loads. However, limited research, mostly focused on numerical investigations, has been performed to evaluate the shear strength of soil reinforced with stone column. The stress concentration ratio (n) is one of the important parameters that uses in soil improvement by stone column method. Stress concentration ratio is the ratio of the stress carried by stone column to that carried by the surrounding soil. In this paper, the results of a laboratory study were used to examine the changes in the stress concentration ratio when normal and shear stress applied. Direct shear tests were carried out on specimens of sand bed material, stone column material and sand bed reinforced with stone column, using a direct shear device with in-plane dimensions of 305*305 mm and height of 152.4 mm. Experiments were performed under normal stresses of 55, 77 and 100 kPa. In this study, three different area replacement ratios (8.4%, 12%, 16.4%), and three different stone column arrangements (single, square and triangular) were considered for investigation. Loose sand and crushed gravel were used to make the bed and stone columns, respectively. In this study, the equivalent shear strength and equivalent shear parameters measured from experiments were also compared with those predicted by analytical relationships at stress concentration value of 1 and stress concentration value obtained from experiments.
Material Properties
Fine-grained sand with particle size ranging from 0.425 to 1.18 mm was used to prepare loose sand bed, and crushed gravel with particle size ranging from 2 to 8 mm was used as stone column material. The sand material used as bed material had a unit weight of 16 kN/m³ and a relative density of 32.5%, and the crushed stone material used in stone columns had a unit weight of 16.5 kN/m³ and a relative density of 80%. The required standard tests were performed to obtain the mechanical parameters of bed material and stone column material. As the diameters of model scale stone columns were smaller than the diameters of stone columns installed in the field, the particle dimensions of stone column material were reduced by an appropriate scale factor to allow an accurate simulation of stone columns behavior.
Testing Procedure
In this study, large direct shear device was used to evaluate the shear strength and equivalent shear strength parameters of loose sand bed reinforced with stone column. Experiments were performed under normal stresses of 55, 75 and 100 kPa. Two class C load cells with capacity of 2 tons were used to measure and record vertical forces and the developed shear forces during the experiments, and a Linear Variable Differential Transformer (LVDT) was used to measure horizontal displacement. The main objectives of this study was to calculate the stress concentration ratio of stone columns in different arrangement. Stress concentration ratio is the ratio of the stress carried by stone column to that carried by the surrounding soil, and can be calculated using Equation 1. For this purpose, the direct shear device was modified. Two miniature load cells with capacity of 5 kN were employed. The load cells were mounted on the rigid loading plate with dimensions of 305*305 mm² and thickness of 30 mm, as shown in Figure 1, All achieved data from the experiments including data on vertical forces, shear forces and horizontal displacements were collected and recorded using a data logger, and an especial software was used to transfer data between the computer and the direct shear device. All specimens were sheared under a horizontal displacement rate of 1 mm/min.
Experiments were performed on single stone columns and group stone columns arranged in square and triangular patterns. The selected area replacement ratios were 8.4, 12 and 16.4% for single, square and triangular stone column arrangements. To eliminate boundary effects, the distance between stone columns and the inner walls of the shear box was kept as high as 42.5 mm. In total, 11 direct shear tests were carried out, including two tests on loose sand bed material and stone column material, and 9 tests on stone columns with different arrangements. From the tests performed on group stone columns, 3 tests were performed on single stone columns, 3 tests on stone columns with square arrangement and 3 tests on stone columns with triangular arrangement. Hollow pipes with wall thickness of 2 mm and inner diameters equal to stone column diameters were used to construct stone columns. To prepare the specimens, first, the hollow pipes were installed in the shear box according to the desired arrangement. Then, bed material with unit weight of 16.5 kN/m³ was placed and compacted in the box in 5 layers, each 3 cm thick. Stone material was uniformly compacted to construct stone columns with uniform unit weight.
Results and discussion

1. The SCR value increases for settlement up to 3 mm and then decreases with increasing the horizontal displacement and then approaches almost a constant value. Results also show that stress concentration ratio decreases with increase of stone column diameter. Results show that the value of stress concentration ratio in square pattern is higher than that in single and triangular pattern. Moreover, results show that stress concentration ratio decreases with increase of normal stress.
2. The value of the internal friction angle in (peak) state, for loose bed increases from 33 to 40 degrees in square arrangement and in the corresponding state of displacement of 10 % from 30 degrees in a loose bed increase to 32 degrees, for loose sand reinforced with stone column. Shear strength increases with the increase of modified area ratio in all stone column installation patterns in both the peak and the corresponding state of the horizontal displacement of 10%.
3. For stone columns with the same modified area ratio, the installation pattern has an effective role in defining the shear strength. Group stone columns mobilize higher shear strength compared to single stone columns. Among the installation patterns investigated in this study, stone columns with square arrangement experienced the highest increase in shear strength value while single stone columns experienced the lowest.
4. The equivalent shear strength values measured from experiments are higher than those obtained from analytical relationships. Accordingly, it is conservative to use analytical relationships to calculate shear strength parameters. It is worth explaining that these relationships assume that the value of stress concentration ratio is equal to 1. Results from this study show that the value of stress concentration ratio should be accurately calculated and used in the relationships.
5. Comparison between shear strength parameters obtained from experiments and those predicted by analytical relationships shows that in single stone columns, the value of stress concentration ratio should be 3 to 4.5, and in square and triangular patterns, this value should be 6 to 7 and in triangular patterns 4.5 to 5, respectively, to achieve good agreement between experimental and analytical results in peak condition. In horizontal displacement 10% the value of stress concentration ratio should be 2.5 to 3, in single, square and triangular patterns, to achieve good agreement between experimental and analytical results../files/site1/files/151/2.pdf