Reliability Evaluation of Slopes Using Particle Swarm Optimization

The objective of this research is to develop a numerical procedure to reliability evaluation of earth slope and locating the critical probabilistic slip surface. The performance function is  formulated using simplified Bishop’s limit equilibrium method  to calculate the reliability index. The reliability index defined by Hasofer and Lind is used as an index of safety measure. Searching the critical probabilistic surface that is associated with the lowest reliability index will be formulated as an optimization problem. In this paper, particle swarm optimization is applied to calculate the minimum Hasofer and Lind reliability index and critical probabilistic failure surface. To demonstrate the applicability and to investigate the effectiveness of the algorithm, two numerical examples from literature are illustrated. Results show that the proposed method is capable to achieve better solutions for reliability analysis of slope if compared with those reported in the literature

Probabilistic Slope Stability Evaluation Using Hybrid Metaheuristic Approach

This paper develops an efficient evolutionary hybrid optimization technique based on the adaptive salp swarm algorithm (ASSA) and pattern search (PS) for the reliability evaluation of earth slopes considering spatial variability of soils under the framework of the limit equilibrium method. In the ASSA, to improve the salp swarm approach's exploration ability while also avoiding premature convergence, two new equations for the leaders' and followers' position updating procedure are introduced. The proposed hybrid algorithm (ASSPS) benefits from the effective global search ability of the adaptive salp swarm algorithm as well as the powerful local search capability of the pattern search method. The suggested ASSPS algorithm's efficiency is confirmed using mathematical test functions, and its findings are compared with the standard salp swarm algorithm as well as some efficient optimization techniques. Then, the ASSPS is applied for calculation of the lowest safety factor and reliability index of earth slopes. The safety factor is formulated using the Morgenstern and Price approach and the advanced first-order second-moment (AFOSM) method is implemented for the reliability calculation model. The ASSPS's efficacy for the evaluation of the minimum reliability index of slopes is investigated by considering two literature-based case studies. The numerical experiments demonstrate that the new algorithm could generate better optimal solutions and significantly outperform other methods in the literature

Data and Process Mining Applications on a Multi-Cell Factory Automation Testbed

This paper presents applications of both data mining and process mining in a factory automation testbed. It mainly concentrates on the Manufacturing Execution System (MES) level of production hierarchy. Unexpected failures might lead to vast losses on investment or irrecoverable damages. Predictive maintenance techniques, active/passive, have shown high potential of preventing such detriments. Condition monitoring of target pieces of equipment beside defined thresholds forms basis of the prediction. However, monitored parameters must be independent of environment changes, e.g. vibration of transportation equipments such as conveyor systems is variable to workload. This work aims to propose and demonstrate an approach to identify incipient faults of the transportation systems in discrete manufacturing settings. The method correlates energy consumption of the described devices with the workloads. At runtime, machine learning is used to classify the input energy data into two pattern descriptions. Consecutive mismatches between the output of the classifier and the workloads observed in real time indicate possibility of incipient failure at device level. Currently, as a result of high interaction between information systems and operational processes, and due to increase in the number of embedded heterogeneous resources, information systems generate unstructured and massive amount of events. Organizations have shown difficulties to deal with such an unstructured and huge amount of data. Process mining as a new research area has shown strong capabilities to overcome such problems. It applies both process modelling and data mining techniques to extract knowledge from data by discovering models from the event logs. Although process mining is recognised mostly as a business-oriented technique and recognised as a complementary of Business Process Management (BPM) systems, in this paper, capabilities of process mining are exploited on a factory automation testbed. Multiple perspectives of process mining is employed on the event logs produced by deploying Service Oriented Architecture through Web Services in a real multi-robot factory automation industrial testbed, originally used for assembly of mobile phones

Seismic Analysis of Earth Slope Using a Novel Sequential Hybrid Optimization Algorithm

One of the most important topics in geotechnical engineering is seismic analysis of the earth slope. In this study, a pseudo-static limit equilibrium approach is applied for the slope stability evaluation under earthquake loading based on the Morgenstern–Price method for the general shape of the slip surface. In this approach, the minimum factor of safety corresponding to the critical failure surface should be investigated and it is a complex optimization problem. This paper proposed an effective sequential hybrid optimization algorithm based on the tunicate swarm algorithm (TSA) and pattern search (PS) for seismic slope stability analysis. The proposed method employs the global search ability of TSA and the local search ability of PS. The performance of the new CTSA-PS algorithm is investigated using a set of benchmark test functions and the results are compared with the standard TSA and some other methods from the literature. In addition, two case studies from the literature are considered to evaluate the efficiency of the proposed CTSA-PS for seismic slope stability analysis. The numerical investigations show that the new approach may provide better optimal solutions and outperform previous methods

Prediction of Rock Tensile Strength Using Soft Computing and Statistical Methods

The tensile strength of the rocks is one of the effective factors in the rupture of structure foundations and underground spaces, the stability of rocky slopes, and the ability to drill and explode in rocks. This research was conducted to estimate tensile strength using methods such as simple regression (SR), multivariate linear regression (MVLR), support vector regression (SVR) with radial basis kernel function, multilayer feed-forward artificial neural network (MFF-ANN), Gaussian process regression (GPR) using squared exponential kernel (SEK) function, and adaptive neuro-fuzzy inference system (ANFIS) based on Gaussian membership function. For this purpose, petrography, and engineering features of the limestone, sandstone, and argillaceous limestone samples in the south of Iran, were assessed. The results obtained from this study were compared with those of previous research, revealing a strong correlation (R2=0.95 to 1.00) between our findings and the published works. To estimate Brazilian tensile strength (BTS), the index properties including water absorption by weight, point load index (PLI), porosity%, P-wave velocity (Vp), and density were considered as inputs. Methods were compared using various criteria. The SVR precision (R=0.96) was higher than MFF-ANN (R=0.92), ANFIS (R=0.95), GPR (R=0.945), and MVLR (R=0.89) to estimate the tensile strength. The average BTS measured in the laboratory and predicted by all 5 methods is 6.62 and 6.71 MPa, respectively, which shows the very high precision of the investigated methods. Analysis of model criteria using statistical analysis for developed relationships revealed that there is sufficient accuracy to use the empirical equations

Effect of mycorrhiza symbiosis on the Nacl salinity in Sorghum bicolor

In order to determine mycorrhizal symbiosis on the Nacl salinity tolerance in Sorghum bicolor (aspydfyd cultivar), an experiment with two factors was done in Damghan Islamic Azad University laboratory (Iran) in 2007. The first factor with two levels (mycorihizal and non-mycorihizal) and second factor with six levels Nacl concentration of (0, 50, 100, 150, 200 and 250 Mmol) were examined in a random design with three replication in sand environment for 15 weeks. The measurements were the absorption of K, Na, P, N, plant growth, tolerance for different salinity concentrations and traits such as stem and root dry weight and the length of stem. The results showed that the dry weight and stem height in M plants were higher than NM plants. The increase in Nacl concentration decreased the stem height in both groups. However, there was no significant different in root dry weight. The measurement of elements in different organs showed that with increase in Nacl concentration, there would be a significant decrease in N, P, K absorption. But Na absorption increase is more in lower Nacl concentration. Generally, the amount of N, P, K in M plant organs is more than NM plant organs. The result of the experiment showed that mycorrhizal symbiosis is not only effective on element absorption, but also in plant growth and to some extent on salinity tolerance of the plant. So it will be suggested that mycorrhizal be used in salty soils with high Nacl for Sorghum bicolor.Key words: Sorghum, mycorrhizal, salinity, symbiosis, tolerance

A Novel Hybrid Particle Swarm Optimization and Sine Cosine Algorithm for Seismic Optimization of Retaining Structures

This study introduces an effective hybrid optimization algorithm, namely Particle Swarm Sine Cosine Algorithm (PSSCA) for numerical function optimization and automating optimum design of retaining structures under seismic loads. The new algorithm employs the dynamic behavior of sine and cosine functions in the velocity updating operation of particle swarm optimization (PSO) to achieve faster convergence and better accuracy of final solution without getting trapped in local minima. The proposed algorithm is tested over a set of 16 benchmark functions and the results are compared with other well-known algorithms in the field of optimization. For seismic optimization of retaining structure, Mononobe-Okabe method is employed for dynamic loading condition and total construction cost of the structure is considered as the objective function. Finally, optimization of two retaining structures under static and seismic loading are considered from the literature. As results demonstrate, the PSSCA is superior and it could generate better optimal solutions compared with other competitive algorithms

Determination of economic injury level of Sitobion avenae (Hom.: Aphididae) on wheat of Chameran variety in Ahwaz

The drought phenomenon in recent years has increased the oat aphid (Sitobion avenae Fab.) population in khuzestan wheat fields. Considering the existence of different natural enemies of this pest in cereal fields, the best control method would be based on selective pesticide application which is less harmful to environment and natural enemies. Thus determination of pest Economic injury level (EIL) became a necessity to prevent immethodical usage of pesticides in wheat vulnerable agroecosystem. Hence, an experiment was conducted in complete randomized block design based on split plot with two factors and three replications including growing stages and aphid density in Chamran cultivar in agricultural research station of Ahwaz during 2010 to 2011. Growing stages (Early, middle and late of milky stage) and aphid density (0, 5-10, 11-15, 16-20, 21-25, 26-30 and 31-35 aphid on spike) were considered as mainplot and subplot, respectively. All treatments applied in a net cage. Averages of yield in different aphid density treatments were grouped by Duncan- multiple range test. Regression equations and curves were obtained between aphid density and seed weight in each spike. Grain threshold method was used to EIL measurement. The results showed that EIL was 17, 22, 42 aphids/spike in early, middle and late of milky stage of wheat with no calculating of natural enemies but ET was 12, 17, 31 aphids/spike in the first year and EIL was 9, 18, 49 aphids/spike and ET was 7, 14, 37 aphids/spike in the second year. Also, EIL was 8, 16, 44 aphids/spike and ET was 6, 12, 33 aphids/spike in 2014-2015, respectively

Economic Design of Retaining Wall Using Particle Swarm Optimization with Passive Congregation

Abstract: This paper presents an effective optimization method for nonlinear constrained optimization of retaining structures. The proposed algorithm is based on the particle swarm optimization with passive congregation. The optimization procedure controls all geotechnical and structural design constraints while reducing the overall cost of the retaining wall. To applying the constraints, the algorithm employs penalty function method. To verify the efficiency of the proposed method, two design examples of retaining structures are illustrated. Comparison analysis between the results of the presented methodology, standard particle swarm optimization and nonlinear programming optimization method show the ability of the proposed algorithm to find better optimal solutions for retaining wall tasks than the others