Biostratigraphy of Asmari Formation in Ghare Agha seyed of Farsan region,Chaharmahale Bakhtiari Province,Iran

The Asmari Formation consists of shallow marine sedimentary rocks deposited on ramp setting .Lar-ger benthic foraminifera collected from Asmri Formation are dominated by hyaline and porcelanouse forms , in-cluding Amphistegina, Nummulites, Archaias, Astrotrillina, Miogypsinella, Miogypsinoides, Lepidocyclina, Operculina, Spiroclypeous and Miliolids.The presence of Nummulites cf.vascus in the lower part of the forma-tion allows the age to be determined as Rupelian .The occurrence of Borelis pygmae is an index taxon of the Ru-pelian-Chattian and indicates Early Chattian of SBZ 21-22 in the study section .The first appearance of Mio-gypsinella akadagensis shows Late Chattian ( SBZ 23) and defines the upper boundary of the SBZ 21-22.The new data are the first evidences showing that the shallow marine Asmari Formation is attributed to Oligocene (Rupelian-Chattian) age for this region.     

Analysis of the stress field and strain rate in Zagros-Makran transition zone

Transition boundary between Zagros continental collision and Makran oceanic-continental subduction can be specified by two wide limits: (a) Oman Line is the seismicity boundary with a sizeable reduction in seismicity rate from Zagros in the west to Makran in the east; and (b) the Zendan-Minab-Palami (ZMP) fault system is believed to be a prominent tectonic boundary. The purpose of this paper is to analyze the stress field in the Zagros-Makran transition zone by the iterative joint inversion method developed by Vavrycuk (Geophysical Journal International 199:69-77, 2014). The results suggest a rather uniform pattern of the stress field around these two boundaries. We compare the results with the strain rates obtained from the Global Positioning System (GPS) network stations. In most cases, the velocity vectors show a relatively good agreement with the stress field except for the Bandar Abbas (BABS) station which displays a relatively large deviation between the stress field and the strain vector. This deviation probably reflects a specific location of the BABS station being in the transition zone between Zagros continental collision and Makran subduction zones.     

Implementation of viscoelastic mud-induced energy attenuation in the third-generation wave model,SWAN

The interaction of waves with fluid mud can dissipate the wave energy significantly over few wavelengths. In this study, the third-generation wave model, SWAN, was advanced to include attenuation of wave energy due to interaction with a viscoelastic fluid mud layer. The performances of implemented viscoelastic models were verified against an analytical solution and viscous formulations for simple one-dimensional propagation cases. Stationary and non-stationary test cases in the Surinam coast and the Atchafalaya Shelf showed that the inclusion of the mud-wave interaction term in the third-generation wave model enhances the model performance in real applications. A high value of mud viscosity (of the order of 0.1 m²/s) was required in both field cases to remedy model overestimation at high frequency ranges of the wave spectrum. The use of frequency-dependent mud viscosity value improved the performance of model, especially in the frequency range of 0.2–0.35 Hz in the wave spectrum. In addition, the mud-wave interaction might affect the high frequency part of the spectrum, and this part of the wave spectrum is also affected by energy transfer from wind to waves, even for the fetch lengths of the order of 10 km. It is shown that exclusion of the wind input term in such cases might result in different values for parameters of mud layer when inverse modeling procedure was employed. Unlike viscous models for wave-mud interaction, the inverse modeling results to a set of mud parameters with the same performance when the viscoelastic model is used. It provides an opportunity to select realistic mud parameters which are in more agreement with in situ measurements.     

Regional flood frequency analysis using support vector regression in arid and semi-arid regions of Iran

Regional flood frequency analysis (RFFA) was carried out on data for 55 hydrometric stations in Namak Lake basin, Iran, for the period 1992–2012. Flood discharge of specific return periods was computed based on the log Pearson Type III distribution, selected as the best regional distribution. Independent variables, including physiographic, meteorological, geological and land-use variables, were derived and, using three strategies – gamma test (GT), GT plus classification and expert opinion – the best input combination was selected. To select the best technique for regionalization, support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and nonlinear regression (NLR) techniques were applied to predict peak flood discharge for 2-, 5-, 10-, 25-, 50- and 100-year return periods. The GT + ANFIS and GT + SVR models gave better performance than the ANN and NLR models in the RFFA. The results of the input variable selection showed that the GT technique improved the model performance.     

Seismic reliability assessment of a steel moment-resisting frame with two different ductility levels using a cloud analysis approach

A cloud method for generating percentile engineering demand parameter versus intensity measure(EDP-IM) curves of a structure subjected to a set of synthetic ground motions is presented. To this end, an ensemble of synthetic ground motions based on available real ones is generated. This is done by using attenuation relationships, duration and suitable Gutenberg-Richter relations attributed to the considered seismic hazard at a given site by estimating a suitable distribution of magnitude and site to source distance. The study aims to clarify the significance of the duration and frequency content on the seismic performance of structures, which were not considered in developing percentile incremental dynamic analysis(IDA) curves. The collapse probabilities of two steel moment-resisting frames with different ductility levels generated by IDA and the proposed cloud method are compared at different intensity levels. When compared with conventional IDA, the suggested cloud analysis(SCA) methodology with the same run number of dynamic analyses was able to develop response hazard curves that were more consistent with site-specific seismic hazards. Eliminating the need to find many real records by generating synthetic records consistent with site-specific seismic hazards from a few available recorded ground motions is another advantage of using this scheme over the IDA method..     

Assessment of seismic amplification factor of excavation with support system

Retaining walls have been used in many construction projects such as for road and inclined surfaces protection. The damage caused by an earthquake depends on the fundamental frequency, amplitude and the duration of the seismic motion. These parameters strongly depend on the seismic properties of the layers that are near the surface. In the study of retaining walls, in addition to the influence of soil, the influence of topography is also important. In the present study, site response analysis is performed by using finite element software PLAXIS to obtain the effect of various factors such as embedded length of the sheet pile, underground water table, length and angle of the nail, shear wave velocity of soil on site effect and dynamic response. Moreover, for better understanding of the effect of the above parameters, the stability analysis was performed by using shear reduction method. The results show that an increase in the embedded length of the sheet pile and the length of nailing causes an increase in the amplification factor. Moreover, for shear-wave velocity in the range of 200-600 m/s, the amplification factor increases with increase of the shear-wave velocity due to the decrease of nonlinear behavior.     

Climate change impact on precipitation and cardinal temperatures in different climatic zones in Iran: analyzing the probable effects on cereal water-use efficiency

Global greenhouse gases increase could be a threat to sustainable agriculture since it might affect both green water and air temperature. Using the outputs of 15 general circulation models (GCMs) under three SRES scenarios of A1B, A2 and B1, the projected annual and seasonal precipitation (P) and cardinal temperatures (T) were analyzed for five climatic zones in Iran. In addition, the probable effects of climate change on cereal production were studied using AquaCrop model. Data obtained from the GCMs were downscaled using LARS-WG for 52 synoptic stations up to 2100. An uncertainty analysis was done for the projected P and T associated to GCMs and SRES scenarios. Based on station observations, LARS-WG was capable enough for simulating both P and T for all the climatic zones. The majority of GCMs as well as the median of the ensemble for each scenario project positive P and T changes. In all the climatic zones, wet seasons have a higher P increase than dry seasons, with the highest increase (27.9–83.3%) corresponding to hyper-arid and arid regions. A few GCMs project a P reduction mainly in Mediterranean and hyper-humid climatic regions. The highest increase (11.2–44.5%) in minimum T occurred in Mediterranean climatic regions followed by semi-arid regions in which a concurrent increase in maximum T (2.9–14.6%) occurred. The largest uncertainty in P and cardinal T projection occurred in rainy seasons as well as in hyper-humid regions. The AquaCrop simulation results revealed that the increased cardinal T under global warming will cause 0–28.5% increase in cereal water requirement as well as 0–15% reduction in crop yield leading to 0–30% reduction in water use efficiency in 95% of the country.     

Use of Intelligent Methods to Design Effective Pattern Parameters of Mine Blasting to Minimize Flyrock Distance

Flyrock is one of the most important environmental issues in mine blasting, which can affect equipment, people and could cause fatal accidents. Therefore, minimization of this environmental issue of blasting must be considered as the ultimate objective of many rock removal projects. This paper describes a new minimization procedure of flyrock using intelligent approaches, i.e., artificial neural network (ANN) and particle swarm optimization (PSO) algorithms. The most effective factors of flyrock were used as model inputs while the output of the system was set as flyrock distance. In the initial stage, an ANN model was constructed and proposed with high degree of accuracy. Then, two different strategies according to ideal and engineering condition designs were considered and implemented using PSO algorithm. The two main parameters of PSO algorithm for optimal design were obtained as 50 for number of particle and 1000 for number of iteration. Flyrock values were reduced in ideal condition to 34 m; while in engineering condition, this value was reduced to 109 m. In addition, an appropriate blasting pattern was proposed. It can be concluded that using the proposed techniques and patterns, flyrock risks in the studied mine can be significantly minimized and controlled.

     

Optimisation of deep mixing technique by artificial neural network based on laboratory and field experiments

ABSTRACT

Ground improvement techniques are inevitable for weak soils that cannot endure the design load imposed by superstructures. Deep mixing technique (DMT) as one of these methods is promising and effective when a deep soil layer with low bearing capacity is encountered. Such deposits are quite common in the South-west of Iran where the studied site is located. In order to validate the influence of DMT on the enhancement of strength, both in-situ and laboratory tests were conducted. Afterwards, a parametric study was carried out to investigate the influence of key factors including cement content, water–cement ratio, curing time and plasticity index (PI) on the performance of DMT. In summary, a total of 192 different conditions were examined in this study by using two methods of 3D plotting and artificial neural networks (ANNs) as the optimisation tool. Results proved the importance of water–cement ratio as a key parameter in DMT. Based on the trained networks, ANN was revealed to give satisfactory predictions on the strength of an improved soil with different admixture conditions. More important, the optimisation made by ANN could determine the specific values for selected key admixture factors to reach a desired strength level with the coefficient of determination higher than 0.85.     

Experimental and numerical investigation for energy dissipation of supercritical flow in sudden contractions

Dealing with kinetic energy is one of the most important problems in hydraulic structures, and this energy can damage downstream structures. This study aims to study energy dissipation of supercritical water flow passing through a sudden contraction. The experiments were conducted on a sudden contraction with 15 cm width. A 30 cm wide flume was installed. The relative contraction ranged from 8.9 to 9.7, where relative contraction refers to the ratio of contraction width to initial flow depth. The Froude value in the investigation varied from 2 to 7. The contraction width of numerical simulation was 5~15 cm, the relative contraction was 8.9~12.42, and the Froude value ranged from 8.9~12.42. In order to simulate turbulence, the k-ε RNG model was harnessed. The experimental and numerical results demonstrate that the energy dissipation increases with the increase of Froude value. Also, with the sudden contraction, the rate of relative depreciation of energy is increased due to the increase in backwater profile and downstream flow depth. The experimentation verifies the numerical results with a correlation coefficient of 0.99 and the root mean square error is 0.02.