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فهرست مطالب نویسنده:

mohammad mohammadizadeh

  • Mohammad Mohammadizadeh *, Farnaz Esfandnia, Mohsen Khatibinia
    It is generally accepted that the shear strength of Reinforced Concrete (RC) deep beams depends on the mechanical and geometrical parameters of the beam. The accurate estimation of shear strength is a substantial problem in engineering design. However, the prediction of shear strength in this type of beams is not very accurate. One of the relatively accurate methods for estimating shear strength of beams is Artificial Intelligence (AI) methods. Adaptive Neuro-Fuzzy Inference System (ANFIS) was presented as an AI method. In this study, the efficiency of ANFIS incorporating meta-heuristic algorithms for predicting shear strength of RC beams was investigated. Meta-heuristic algorithms were used to determine the optimum parameters of ANFIS for providing the efficient models of the prediction of the RC beam shear strength. To evaluate the accuracy of the proposed method, its results were compared with those of other methods. For this purpose, the parameters of concrete compressive strength, cross-section width, effective depth, beam length, shear span-to-depth beam ratio (a/d), as well as percentage of longitudinal and transverse reinforcement were selected as input data, and the shear strength of reinforced concrete deep beam as the output data. Here, K-fold validation method with k = 10 was used to train and test the algorithms. The results showed that the proposed model with second root mean square error of 25.968 and correlation coefficient of 0.914 is more accurate than other methods. Therefore, neural fuzzy inference system with meta-heuristic algorithms can be adopted as an efficient tool in the prediction of the shear strength of deep beams.
    Keywords: Meta-heuristic algorithms, Neuro-fuzzy inference system, Reinforced concrete deep beam, shear strength
  • Mohammad Mohammadizadeh *, Farnaz Esfandnia
    There are several methods to predict the compression strength of reinforced concrete columns confined by FRP, such as experimental methods, theory of elasticity and plasticity. Meanwhile, due to its good potential and high accuracy in predicting different problems, the soft computing techniques has attracted considerable attentions. Soft computing includes methods and programs to deal with complex computational problems. The objective of this study is to evaluate and compare the performance of four methods of Least Squares Support Vector Machine (LS-SVM), the Weight Least Squares Support Vector Machine (WLS-SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Particle Swarm Optimization - Adaptive Network based Fuzzy Inference System (PSO-ANFIS) for predicting the compression strength of reinforced concrete columns confined by FRP. A total of 95 laboratory data are selected for use in these methods. The Root Mean Square Error (RMSE) and the correlation coefficient of the results are used to validate and compare the performance of the methods. The results of the study show that the PSO-ANFIS method with the RMSE of 4.610 and the coefficient of determination of R2 = 0.9677 predicts compression strength of reinforced concrete columns confined by FRP with high accuracy and therefore, it can be a good alternative to time-consuming and costly laboratory methods.
    Keywords: ANFIS, Compression Strength, FRP-Confined Columns, LS-SVM, PSO-ANFIS, WLS-SVM
  • Mohammad Mohammadizadeh *, Ahmad Jafarzadeh
    In this study, the seismic response of tall concrete structures with a special dual frame-wall concrete system is investigated using the endurance time method, and the results are compared with nonlinear time history analysis results. For this purpose, first, appropriate analytical models including buildings with concrete framed-wall system and 20, 30, and 40 stories are modeled non-linearly in PERFORM 3D software, and then, main nonlinear time history analyses are carried out for seven ground motions (accelerogram) further from the fault based on the FEMA P695 code and the endurance time accelerogram of (in) series. The results of the analysis are compared using indices (shear, relative displacement, and acceleration). The results indicate that the endurance time method is accurate in two indices of shear and acceleration, but the accuracy of the relative displacement index of the floor decreases as the number of stories of the structure increases.
    Keywords: Dual Concrete Lateral Resistant System, Endurance Time Method, Nonlinear Time History Analysis, Tall Structure
  • Mohammad Mohammadizadeh *, Babak Yasi
    One of the numerous methods recently employed to study the health of structures is the identification of anomaly in data obtained for the condition of the structure, e.g. the frequencies for the structural modes, stress, strain, displacement, speed, and acceleration) which are obtained and stored by various sensors. The methods of identification applied for anomalies attempt to discover and recognize patterns governing data which run in sharp contrast to the statistical population. In the case of data obtained from sensors, data appearing in contrast to others, i.e. outliers, may signal the occurrence of damage in the structure. The present research aims to employ computer algorithms to identify structural defects based on data gathered by sensors indicating structural conditions. The present research investigates the performance of various methods including Artificial Neural Networks (ANN), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Manhattan Distance, Curve Fitting, and Box Plot in the identification of samples from damages in a case study using frequency values related to a cable-support bridge. Subsequent to the implementation of the methods in the datasets, it was shown that the ANN provided the optimal performance.
    Keywords: Artificial Neural Networks, Damage Identification, Frequency, Manhattan Distance, Structures
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