Seongkyu Chang

Estimation of Concrete Strength Using Improved Probabilistic Neural Network Method

The compressive strength of concrete is commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time-consuming. More importantly, it is too late to make improvement even if the test result does not satisfy the required strength, since the test is usually performed at the 28th day after the placement of concrete at the construction site. Therefore, accurate and realistic strength estimation before the placement of concrete is being highly required. In this study, the estimation of the compressive strength of concrete was performed by probabilistic neural network(PNN) on the basis of concrete mix proportions. The estimation performance of PNN was improved by considering the correlation between input data and targeted output value. Improved probabilistic neural network was proposed to automatically calculate the smoothing parameter in the conventional PNN by using the scheme of dynamic decay adjustment (DDA) algorithm. The conventional PNN and the PNN with DDA algorithm(IPNN) were applied to predict the compressive strength of concrete using actual test data of two concrete companies. IPNN showed better results than the conventional PNN in predicting the compressive strength of concrete.

Vibration Control of Nuclear Power Plant Piping System Using Stockbridge Damper under Earthquakes

Generally the piping system of a nuclear power plant (NPP) has to be designed for normal loads such as dead weight, internal pressure, temperature, and accidental loads such as earthquake. In the proposed paper, effect of Stockbridge damper to mitigate the response of piping system of NPP subjected to earthquake is studied. Finite element analysis of piping system with and without Stockbridge damper using commercial software SAP2000 is performed. Vertical and horizontal components of earthquakes such as El Centro, California, and Northridge are used in the piping analysis. A sine sweep wave is also used to investigate the control effects on the piping system under wide frequency range. It is found that the proposed Stockbridge damper can reduce the seismic response of piping system subjected to earthquake loading.

Earthquake Response Reduction of Building Structures Using Learning-Based Lattice Pattern Active Controller

An active vibration control technique for building structures using a learning-based lattice pattern controller (LBLPC) is proposed in this article. The training pattern of the LBLPC is composed of a lattice form for the control force and a state vector. The training pattern was trained by a learning rule using the gradient descent method (GDM) in earthquakes. The LBLPC calculates the control force using only the adjacent input information, thus making the corresponding calculation process much faster. A three-story building in the El Centro earthquake was used to train the LBLPC. And the California and Northridge earthquakes were used to verify the performance of the proposed method. In order to prove the control capability of the LBLPC, the control results of the LBLPC were compared with those of a lattice type probabilistic neural network (LPNN) in a numerical example. The results demonstrated that the proposed LBLPC algorithm reduces the response of the building structure during earthquakes more effectively than the LPNN.

Active control of building structure using lattice probabilistic neural network based on learning algorithm

Active control of building structure using lattice probabilistic neural network (LPNN) employing the gradient descent method (GDM) for learning to increase control capability is proposed. With the lattice pattern of the state vector used as the training data, LPNN calculates the control force using only the adjacent information of input, thus, response is greatly faster. Three story building under El Centro earthquake is used to train the LPNN. Northridge earthquake is used to verify the proposed method. In the numerical simulation of the building structure control, the control results of the LPNN are compared with the uncontrolled results. The proposed LPNN algorithm can effectively reduce the response of the building structure under earthquakes.

Adaptive multiple tuned mass dampers based on modal parameters for earthquake response reduction in multi-story buildings

The primary objective of this research is to find the effectiveness of an adaptive multiple tuned mass damper distributed along with the story height to control the seismic response of the structure. The seismic performance of a 10-story building was investigated, which proved the efficiency of the adaptive multiple tuned mass damper. Structures with single tuned mass damper and multiple tuned mass dampers were also modeled considering the location of the dampers at the top of the structure, whereas adaptive multiple tuned mass damper of the structure was modeled based on the story height. Selection of the location of the adaptive multiple tuned mass damper along with the story height was dominated by the modal parameters. Participation of modal mass directly controlled the number of the modes to be considered. To set the stage, a comparative study on the displacements and modal energies of the structures under the El-Centro, California, and North-Ridge earthquakes was conducted with and without various types of tuned mass dampers. The result shows a significant capability of the proposed adaptive multiple tuned mass damper as an alternative tool to reduce the earthquake responses of multi-story buildings.

Active Control of Structures Using Lattice Probabilistic Neural Network

A new neuro-control scheme for active control of structures is proposed. It utilizes lattice pattern of state vector as training data of probabilistic neural network(PNN). Therefore. it is the so-called lattice probabilistic neural network(LPNN). PNN makes control forces by using all the training patterns. Therefore, it takes much time to obtain a control force in application. This inevitably may delay the control action. However. control force of LPNN is calculated by using only the adjacent information of LPNN input. So, the response of LPNN is greatly faster than PNN. The proposed control algorithm is applied for three story building under California and El Centro earthquakes. Also, control results of the LPNN are compared with those of the conventional PNN. The structural responses have been suppressed effectively by the proposed algorithm.

Modified Probabilistic Neural Network of Heterogeneous Probabilistic Density Functions for the Estimation of Concrete Strength

Recently, probabilistic neural network (PNN) has been proposed to predict the compressive strength of concrete for the known effect of improvement on PNN by the iteration method. However, an empirical method has been incorporated in the PNN technique to specify its smoothing parameter, which causes significant uncertainty in predicting the compressive strength of concrete. In this study, a modified probabilistic neural network (MPNN) approach is hence proposed. The global probability density function (PDF) of variables is reflected by summing the heterogeneous local PDFs which are automatically determined by the individual standard deviation of each variable. The proposed MPNN is applied to predict the compressive strength of concrete using actual test data from a concrete company. The estimated results of MPNN are compared with those of the conventional PNN. MPNN showed better results than the conventional PNN in predicting the compressive strength of concrete and provided promising results for the probabilistic approach to predict the concrete strength by using the individual standard deviation of a variable.