Optimal design of structures for earthquake loads by a hybrid RBF-BPSO method     

Eysa Salajegheh  Saeed Gholizadeh  Mohsen Khatibinia 《地震工程与工程振动(英文版)》2008,7(1):13-24

The optimal seismic design of structures requires that time history analyses （THA） be carried out repeatedly. This makes the optimal design process inefficient, in particular, if an evolutionary algorithm is used. To reduce the overall time required for structural optimization, two artificial intelligence strategies are employed. In the first strategy, radial basis function （RBF） neural networks are used to predict the time history responses of structures in the optimization flow. In the second strategy, a binary particle swarm optimization （BPSO） is used to find the optimum design. Combining the RBF and BPSO, a hybrid RBF-BPSO optimization method is proposed in this paper, which achieves fast optimization with high computational performance. Two examples are presented and compared to determine the optimal weight of structures under earthquake loadings using both exact and approximate analyses. The numerical results demonstrate the computational advantages and effectiveness of the proposed hybrid RBF-BPSO optimization method for the seismic design of structures.  相似文献   

Unsteady flow over offshore wind turbine airfoils and aerodynamic loads with computational fluid dynamic simulations     

M. Abbaspour  A. R. Radmanesh  M. R. Soltani 《International Journal of Environmental Science and Technology》2016,13(6):1525-1540

The first notable megawatt class wind turbine, which was the pioneer of improvement in the blade performance in large wind turbines, appeared in Vermont. Nowadays, modern wind turbines are using blades with multi-airfoils at different sections. In this study, in order to indicate the best airfoil profile for the optimum performance in different sections of a blade, five popular airfoils, including S8xx, FFA and AH series, were studied. On the large-scale profile, shear stress transport K–ω model was applied for the simulation of horizontal axis wind turbines for different wind speeds. The aerodynamic simulation was accomplished using computational fluid dynamic method, which in turn is based on the finite volume method, and semi-implicit method for pressure-linked equations algorithm is used for pressure–velocity coupling. The governing equations applied in this simulation are the unsteady Reynolds-averaged Navier–Stokes equations. The aerodynamic coefficients of lift and drag were calculated at different angle of attacks and different wind speeds. The results were validated by EPPLER code, XFOIL and experimental data of the US National Renewable Energy Laboratory. The results showed that S818 profile is the best profile in terms of gaining the highest lift coefficient with the lowest angle of attack at the root of the blades. The findings also indicated that the selected model can predict the exact geometry with a high precision.  相似文献   

Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study     

Najmeh?Khalili  Saeed?Reza?KhodashenasEmail author  Kamran?Davary  Mohammad?Mousavi?Baygi  Fatemeh?Karimaldini 《Arabian Journal of Geosciences》2016,9(13):624

In this paper, we have utilized ANN (artificial neural network) modeling for the prediction of monthly rainfall in Mashhad synoptic station which is located in Iran. To achieve this black-box model, we have used monthly rainfall data from 1953 to 2003 for this synoptic station. First, the Hurst rescaled range statistical (R/S) analysis is used to evaluate the predictability of the collected data. Then, to extract the rainfall dynamic of this station using ANN modeling, a three-layer feed-forward perceptron network with back propagation algorithm is utilized. Using this ANN structure as a black-box model, we have realized the complex dynamics of rainfall through the past information of the system. The approach employs the gradient decent algorithm to train the network. Trying different parameters, two structures, M₅₃₁ and M₇₄₁, have been selected which give the best estimation performance. The performance statistical analysis of the obtained models shows with the best tuning of the developed monthly prediction model the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) are 0.93, 0.99, and 6.02 mm, respectively, which confirms the effectiveness of the developed models.  相似文献   

Role of early warning systems for sustainable agriculture in Iran     

Saeed Sharafi  Mahmud Ramroudi  Mehdi Nasiri  Mohammad Galavi  Gholam Ali Kamali 《Arabian Journal of Geosciences》2016,9(19):734

Drought is a complex phenomenon in meteorology and can affect agriculture. Its impacts vary greatly since they depend not only on the magnitude, timing, duration, and frequency of rainfall deficits but also on the differing responses of various plants to water stress. The essence of good drought management is to use this range of responses to the best advantage. Iran is one of the world’s largest and most productive suppliers of food and fiber. The objective of this study was, therefore, to gather and analyze standardized information on the Role of Early Warning Systems for Sustainable Agriculture for cereals and leguminous and industrial crops in Iran environmental zones. Annual average rainfall (mm year^?1) and ET_O (mm year^?1) are 76.56 and 3001, respectively, in stations with very dry climate; 195.41 and 2249, respectively, in stations with dry climate; 343.9 and 1351, respectively, in stations with semi-dry climate; 583.8 and 1153, respectively, in stations with semi-humid climate; and 1272 and 949, respectively, in stations with humid climate. The maximum and minimum of annual average rainfall happened in Rasht (1337 mm year^?1) and Zabol (57 mm year^?1) stations, and the maximum and minimum for annual average ET_O happened in Chabahar (3909.15 mm year^?1) and Anzali harbor (890.6 mm year^?1), respectively. Therefore, 13.63 % of stations have suitable conditions for crop productions and 86.37 % are in critical conditions.  相似文献   

Storage dam’s locality placing by MCDM techniques (case study: three dams in Iran)     

Saeed Reza Khodashenas  Niloufar Yarahmadi 《Arabian Journal of Geosciences》2016,9(13):612

MCDM (multi-criteria decision making) techniques are used to choose the best alternative among the relevant ones and rank the others. In this research, MCDM techniques were used to choose and rank the best places for constructing storage dams in Iran. To achieve this goal, the relevant information and data about dam construction situations were collected. Then, the recommended places for constructing storage dams were ranked by using MCDM techniques and according to the most suitable criteria which have been chosen based on experts’ opinions. To rank dam constructing project, three MCDM techniques, i.e., TOPSIS, AHP, and DEMATEL, were used. The results showed that the application of MCDM models by putting emphasis on economic, social, political, and technical criteria enables government and watershed managers to choose the best alternative(s) for constructing a storage dam.  相似文献   

Erratum to: The interannual precipitation variability in the southern part of Iran as linked to large-scale climate modes     

Farnaz Pourasghar  Tomoki Tozuka  Saeed Jahanbakhsh  Behrooz Sari Sarraf  Hooshang Ghaemi  Toshio Yamagata 《Climate Dynamics》2012,39(12):3121-3121

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Modeling the exceptional south Foehn event (Garmij) over the Alborz Mountains during the extreme forest fire of December 2005     

Abbas Mofidi  Iman Soltanzadeh  Yadollah Yousefi  Azar Zarrin  Mohsen Soltani  Jafar Masoompour Samakosh  Ghasem Azizi  Samuel T. K. Miller 《Natural Hazards》2015,75(3):2489-2518

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Comparison of classification and clustering methods in spatial rainfall pattern recognition at Northern Iran   总被引：1，自引：1，他引：0  

Saeed Golian  Bahram Saghafian  Sara Sheshangosht  Hossein Ghalkhani 《Theoretical and Applied Climatology》2010,102(3-4):319-329

Pattern recognition is the science of data structure and its classification. There are many classification and clustering methods prevalent in pattern recognition area. In this research, rainfall data in a region in Northern Iran are classified with natural breaks classification method and with a revised fuzzy c-means (FCM) algorithm as a clustering approach. To compare these two methods, the results of the FCM method are hardened. Comparison proved overall coincidence of natural breaks classification and FCM clustering methods. The differences arise from nature of these two methods. In the FCM, the boundaries between adjacent clusters are not sharp while they are abrupt in natural breaks method. The sensitivity of both methods with respect to rain gauge density was also analyzed. For each rain gauge density, percentage of boundary region and hardening error are at a minimum in the first cluster while the second cluster has the maximum error. Moreover, the number of clusters was sensitive to the number of stations. Since the optimum number of classes is not apparent in the classification methods and the boundary between adjacent classes is abrupt, use of clustering methods such as the FCM method, overcome such deficiencies. The methods were also applied for mapping an aridity index in the study region where the results revealed good coincidence between the FCM clustering and natural breaks classification methods.  相似文献   

Chemical stabilization of sabkha soils at high moisture contents     

Omar Saeed Baghabra Al-Amoudi 《Engineering Geology》1994,36(3-4):279-291

Vast expanses of arid, saline soils that occur along the Arabian Gulf seaboard and elsewhere possess a very low density and strength that necessitate improvement before any actual construction takesplace. For large-scale constructions, several field improvement techniques have recently been implemented with various degrees of success. In surficial, small-scale applications, chemical stabilization provides a potential technique to improve the inferior properties of these soils, known locally as sabkha. A literature search indicates that chemical stabilization of soils usingasphalt, lime and cement is usually conducted at lower moisture contents than the optimum. Such moisture contents are also much lower than the natural moisture content of sabkha, and if applied to sabkha in the field, this wouldrequire lowering the moisture content before any stabilization commenced; whichwould be neither feasible nor economical.

In this investigation, an eastern Saudi sabkha soil was chemically stabilized at its natural moisture level, which varies from 16% to 22%. In addition to the characterization of the soil and standard compaction tests, cement and lime sabkha mixtures were prepared at five additions and cured for up to 90 days in plastic wrap. Results indicate that cement-stabilized sabkha gained high strength with time and proved to have a potential use in construction.  相似文献   

Evaluation of sediment contamination in the Red Sea coastal area combining multiple pollution indices and multivariate statistical techniques     

Yousef Saeed Saleh 《国际泥沙研究》2021,36(2):243-254

In the recent years,the Red Sea coast of Yemen has been severely affected by intensive anthropogenic activities.The current study constitutes a thorough inquiry to evaluate the extent of heavy metals pollution in Yemen's Red Sea coast sediment and identifies the possible sources of pollution.The concentrations of five metals(copper(Cu),zinc(Zn),cadmium(Cd),lead(Pb),and nickel(Ni))collected from nine sites along the Red Sea coast of Yemen were assessed using an atomic absorption spectrophotometer(ASS).Sediment quality indices,such as the sediment quality guidelines(SQGs),potential ecological risk(RI),contamination factor(CF),pollution load index(PLI),geoaccumulation index(Igeo),and modified degree of contamination(mCd)were computed.In addition,multivariate statistical techniques(principal component analysis(PCA),hierarchical cluster analysis,and Pearson's correlation analysis)were applied to identify the potential sources of metals.The mean concentrations of Cu,Zn,Cd,Pb,and Ni were 51.3,61.9,4.02,9.9,and 33.4 mg/kg dry wt,respectively.The spatial distribution revealed that the metals concentrations were high at the middle zone and low southward of Hodeida city.According to the SQGs,the adverse biological effects of metals were occasionally associated with Cu and Cd,frequently associated with Ni,and not expected to occur with Zn and Pb.The RI indicated that the sediment of the studied sites pose low(RI<50)to considerable(100≤RI<200)ecological risk.The mean effect range-median quotient(M-ERM-Q)indicated that the combination of the studied metals had the toxicity probability of 21%at all studied sites.Igeo and CF indicated that the metals concentrations were in the descending order of:Zn>Ni>Pb>Cd>Cu,whereas the PLI and mCd indicated that Ras Isa(Site 5)and Urj village(Site 6)were the most polluted sites.PCA,cluster analysis,and correlation analysis found that Cd,Pb,and Ni mostly originated from anthropogenic sources while Cu and Zn were mainly derived from natural sources.Thus,it is evident that the intensive anthropogenic activities had negative influence on metals accumulation in the sediment of the Red Sea coast of Yemen leading to detrimental effects to the whole ecosystem.These comprehensive findings provide valuable information and data for future monitoring studies regarding heavy metals pollution and sediment quality at the Red Sea coast of Yemen.  相似文献