A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted. 相似文献
Flash floods are responsible for loss of life and considerable property damage in many countries.Flood susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by landuse planners and emergency managers.The main objective of this study is to prepare an accurate flood susceptibility map for the Haraz watershed in Iran using a novel modeling approach(DBPGA) based on Deep Belief Network(DBN) with Back Propagation(BP) algorithm optimized by the Genetic Algorithm(GA).For this task, a database comprising ten conditioning factors and 194 flood locations was created using the One-R Attribute Evaluation(ORAE) technique.Various well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed model.Statistical metrics include sensitivity,specificity accuracy, root mean square error(RMSE), and area under the receiver operatic characteristic curve(AUC) were used to assess the validity of the proposed model.The result shows that the proposed model has the highest goodness-of-fit(AUC = 0.989) and prediction accuracy(AUC = 0.985), and based on the validation dataset it outperforms benchmark models including LR(0.885), LMT(0.934), BLR(0.936), ADT(0.976), NBT(0.974), REPTree(0.811), ANFIS-BAT(0.944), ANFIS-CA(0.921), ANFIS-IWO(0.939), ANFIS-ICA(0.947), and ANFIS-FA(0.917).We conclude that the DBPGA model is an excellent alternative tool for predicting flash flood susceptibility for other regions prone to flash floods. 相似文献
Piles are frequently used to transfer the heavy compressive loads to strong soil layers located in the depth of bed. In addition, such piles may be subjected to combination of repeated compressive and tensile loads due to earthquake, wind, etc. This paper describes a series of laboratory model tests, at unit gravity, performed on belled pile, embedded in unreinforced and geocell-reinforced beds. The tests were performed to evaluate the beneficial effect of geocell in decreasing the downward and upward displacements and performance improvement of the uplift response of belled pile under repeated compressive and tensile loads. Pile displacements due to fifty load repetitions were recorded. The influence of the height of geocell above the bell of pile, an additional geocell layer at the base of belled pile, and the number of load cycles on pile displacements were investigated. The test results show that the geocell reinforcement reduces the magnitude of the final upward displacement. It also acts as a displacement retardant, and changes the behaviour of belled pile from unstable response condition due to excessive upward pile displacement in unreinforced bed to approximately steady response condition. Therefore, the geocell reinforcement permits higher tensile loads or increased cycling. The efficiency of reinforcement in reducing the maximum upward displacement of the pile (i.e. pull-out resistance) was increased by increasing the height of geocell above the bell of the pile. Furthermore, the comparison showed that a specific improvement in upward and downward displacement and the stability against uplift can be achieved using an additional geocell layer at the base. The geocell reinforcement may reduce the required length of pile shaft, consequently reducing required excavation, backfill, and pile’s material. Simple dimensional analysis showed the need for an increased stiffness of the geosynthetic components in order to match prototype-scale performance similitude. 相似文献
The investigation of the impact of different forms of nitrogen fertilizer (NO3-N and NH4-N) on microbial parameters, enzyme activities and phytotoxicity in a petroleum-contaminated soil was evaluated by an incubation study. The tested enzymes, microbial activity and seed germination index showed different patterns in response to both petroleum and nitrogen fertilizer addition and time of incubation. The results apparently showed that the contamination of soil with petroleum has a negative effect on soil ecosystem. Nitrogen fertilizer could improve inhibition of petroleum hydrocarbons in soil. Nevertheless, nitrogen fertilizer had no significant effect on urease activity in the petroleum-contaminated soil. As compared to NO3-N, the addition of NH4-N to the soil resulted in a greater impact on soil performance as attested by the recovery of the soil germination capability and higher values of the respiration. The application of nitrogen fertilizer may be suggested as a good strategy for restoring soils in regions affected by the same problem. 相似文献
Recently, groundwater vulnerability assessment of coastal aquifers using the GALDIT framework has been widely used to investigate the process of groundwater contamination. This study proposes multi-attribute decision-making (MADM) entropy and Wilcoxon non-parametric statistical test methods to improve the vulnerability index of coastal aquifers. The rates and weights of this framework were modified using Wilcoxon non-parametric and entropy methods, respectively, and a combined framework of GALDIT-entropy, Wilcoxon-GALDIT, and Wilcoxon-entropy was obtained. Pearson correlation coefficients between the mentioned vulnerability indices and total-dissolved solids (TDS) of 0.51, 0.66 and 0.75, respectively, were obtained. According to the results, the Wilcoxon-entropy index had the highest correlation with TDS. Generally, it can be concluded that the proposed frameworks provide a more accurate estimation of vulnerability distribution in coastal aquifers. 相似文献
In this research, the spatial and temporal distribution of Mesoscale Convective Systems was assessed in the southwest of Iran using Global merged satellite IR brightness temperature (acquired from Meteosat, GOES, and GMS geostationary satellites) and synoptic station data. Event days were selected using a set of storm reports and precipitation criteria. The following criteria are used to determine the days with occurrence of convective systems: (1) at least one station reported 6-h precipitation exceeding 10 mm and (2) at least three stations reported phenomena related to convection (thunderstorm, lightning, and shower). MCSs were detected based on brightness temperature, maximum areal extent, and duration thresholds (228 K, 10,000 km2, and 3 h, respectively). An MCS occurrence classification system is developed based on mean sea level, 850 and 500 hPa pressure patterns.
The results indicated that the highest frequency of MCSs occurred in December and April. Assessment of MCSs spatial frequency showed that MCS occurrence is strongly correlated with topography in April and May unlike the cold months. In other words, the role of Zagros Mountains in developing MCSs varies based on the season so that its impact increases with enhancement of mean monthly temperature. In addition, the occurrence of MCSs depends closely on the configuration of the Sudan Low in the southwest of Iran.