Response of an Arch Dam to Non-Uniform Excitation Generated by a Seismic Wave Scattering Model

Non-uniform ground motions are generated based on a single record available at a site and seismic wave scattering analysis. The Chino Hills 2008 earthquake records at the Pacoima Dam site are used to indicate the accuracy of the method. Dynamic analysis of the Pacoima dam-reservoir-foundation under uniform and non-uniform ground motions is carried out using the EACD-3D2008 software, and the results are compared to recorded responses at different locations on the dam. There is good agreement between computed and recorded displacements of the dam for non-uniform excitation. For uniform excitation, the displacements are underestimated in comparison with those obtained from recorded excitation. Significant intensification of stresses, especially near the foundation, and different patterns of stress distribution are observed for non-uniform excitation in comparison with uniform excitation. For uniform excitation maximum stresses occur in the crown cantilever near the crest, but for non-uniform excitation the maximum stresses occur along the sides and near the foundation

Full-scale Experimental Modal Analysis of an Arch Dam: The First Experience in Iran

Forced vibration field tests and finite-element studies were conducted on the Shahid Rajaee concrete arch dam in Northern Iran to determine the dynamic properties of the dam–reservoir–foundation system. The first forced vibration tests on the dam were performed with two different types of exciter units, with a limited maximum force, bolted on the dam crest for alternative in-phase and out-of-phase sequencing. Because of an insufficient number of recording sensors, two arrangements of sensors were used to cover sufficient points on the dam crest and one gallery during tests. Two kinds of vibration tests, on–off and frequency sweeping, were carried out on the dam. The primary natural frequencies of the coupled system for both symmetric and anti-symmetric vibration modes were approximated during on–off tests in two types of sequencing of exciters, in phase and out-of-phase, with a maximum frequency of 14 Hz. The principal forced vibration tests were performed at precise resonant frequencies based on the results of the on–off tests in which sweeping around the approximated frequencies at 0.1 Hz increments was performed. Baseline correction and suitable bandpass filtering were applied to the test records and then signal processing was carried out to compute the auto power, cross power and coherence spectra. Nine middle modes of vibration of the coupled system and corresponding damping ratios were estimated. The empirical results are compared against the results from calibrated finite-element modeling of the system using former ambient vibration tests, considering the dam–reservoir–foundation interaction effects. Good agreement is obtained between experimental and numerical results for eight middle modes of the dam–reservoir–foundation system

Discharge coefficient of combined rectangular-triangular weirs using soft computing models

This study investigates the potential of Adaptive Neuro-fuzzy inference system (ANFIS), M5P, and Gaussian Process regression (GP) approaches to predict discharge coefficient (Cd) of chimney weir with different apex angles. Out of 110 data points, 77 arbitrarily selected observations were used for training, whereas the remaining 77 data points were used for testing. Input data consisted of h/p, y/p, L/p, and w/z, whereas Cd was an output. Four shapes of membership functions, i.e., triangular, trapezoidal, generalized bell-shaped, and Gaussian, were used for the ANFIS-based model development. Five different goodness-of-fit parameters and various graphical presentations were used to evaluate the performance of the machine-learning models. It was found that the M5P-based model was superior to other implemented models in predicting the Cd with Correlation Coefficient (CC) (0.9532 and 0.9472), Mean Absolute Error (MAE) (0.0024 and 0.0026), (Root Mean Square Error) RMSE (0.0032 and 0.0033), Scattering Index (SI) (0.0048 and 0.0050), and Nash Sutcliffe Efficiency (NSE) (0.9085 and 0.9925) values in the training and testing stages, respectively. Another major outcome of this study was that the ANFIS model was better than GP and other MFs-based ANFIS-ti models. The sensitivity of the Cd variables is also investigated, which showed h/p and L/p as major influencing factors in the Cd

Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples

The successful use of fly ash (FA) and silica fume (SF) materials has been reported in the design of concrete samples in the literature. Due to the benefits of using these materials, they can be utilized in many industrial applications. However, the proper use of them in the right mixes is one of the important factors with respect to the strength and weight of concrete. Therefore, this paper develops relationships based on meta-heuristic (MH) algorithms (artificial bee colony technique) to evaluate the compressive strength of concrete specimens using laboratory experiments. A database comprising silica fume replacement ratio, fly ash replacement ratio, total cementitious material, water content coarse aggregate, high-rate water-reducing agent, fine aggregate, and age of samples, as model inputs, was used to evaluate and predict the compressive strength of concrete samples. Developed models of the MH technique created relationships between the mentioned parameters. In the new models, the influence of each parameter on the compressive strength was determined. Finally, using the developed model, optimum conditions for compressive strength of concrete samples were presented. This paper demonstrated that the MH algorithms are able to develop relationships that can serve as good substitutes for empirical models

Random forest and bayesian network techniques for probabilistic prediction of flyrock induced by blasting in quarry sites

Flyrock is one of the most important environmental and hazardous issues in mine blasting, which can affect equipment and people, and may lead to fatal accidents. Therefore, prediction and minimization of this phenomenon are crucial to many rock removal projects. This study was organized in two sections. The first section is related to evaluation and selection of the most effective parameters of flyrock through the use of random forest technique. This resulted in the exclusion of “maximum charge per delay” from being used as input variable. The remaining input variables, i.e., hole diameter, hole depth, burden-to-spacing ratio, stemming, and powder factor, were utilized to develop the probabilistic prediction model using the Bayesian network (BN) technique. The learning and structure type of the BN model were maximum likelihood and tree augmented naïve Bayes, respectively. Many perfect probabilities were observed in the BN model for flyrock occurrence. The hole diameters between 97.5 and 127.5 mm appeared in four perfect probability conditions, which show that this hole diameter range is the most influential parameter. In addition, the combination of hole diameter and hole depth yielded three perfect probability predictions, suggesting that this factor combination is also influential on flyrock distance prediction. The results of this study can be used for optimum design of blasting pattern parameters for flyrock prediction

Seepage Analysis in Short Embankments Using Developing a Metaheuristic Method Based on Governing Equations

Seepage is one of the most challenging issues in some procedures such as design, construction, and operation of embankment or earth fill dams. The purpose of this research is to develop a new solution based on governing equations to solve the seepage problem in an effective way. Therefore, by implementing the equations in the programming environment, more than 24,000 models were designed to be applicable to different conditions. Input data included different parameters such as slopes in upstream and downstream, embankment width, soil permeability coefficient, height, and freeboard. With the use of this big data, a new process was developed to provide simple mathematical models for the seepage rate analysis. The study first used intelligent models to simulate the seepage behavior. Finally, the accuracy of the models was optimized using a new metaheuristic algorithm. This led to the ultimate flexibility of the final model presented as a new solution capable of evaluating different conditions. Finally, using the best model, new mathematical relationships were developed based on this methodology. This new solution can be used as a proper alternative to the governing equations of seepage rate estimation. Another advantage of the proposed model is its high flexibility that can be well applied to engineering design in this field, which was not possible using the initial equations