• International Journal of Concrete Structures and Materials
• /
• v.9 no.2
• /
• pp.159-172
• /
• 2015

Estimation of the residual strength of corroded reinforced concrete beams has been studied from experimental and theoretical perspectives. The former is arduous as it involves casting beams of various sizes, which are then subjected to various degrees of corrosion damage. The latter are static; hence cannot be generalized as new coefficients need to be re-generated for new cases. This calls for dynamic models that are adaptive to new cases and offer efficient generalization capability. Computational intelligence techniques have been applied in Construction Engineering modeling problems. However, these techniques have not been adequately applied to the problem addressed in this paper. This study extends the empirical model proposed by Azad et al. (Mag Concr Res 62(6):405-414, 2010), which considered all the adverse effects of corrosion on steel. We proposed four artificial neural networks (ANN) models to predict the residual flexural strength of corroded RC beams using the same data from Azad et al. (2010). We employed two modes of prediction: through the correction factor ($C_f$) and through the residual strength ($M_{res}$). For each mode, we studied the effect of fixed and random data stratification on the performance of the models. The results of the ANN models were found to be in good agreement with experimental values. When compared with the results of Azad et al. (2010), the ANN model with randomized data stratification gave a $C_f$-based prediction with up to 49 % improvement in correlation coefficient and 92 % error reduction. This confirms the reliability of ANN over the empirical models.

• 제어로봇시스템학회:학술대회논문집
• /
• 1998.10a
• /
• pp.260-264
• /
• 1998

In this paper, we implemented the neuro-computer called MY-NEUPOWER in our research to carry out the artificial neural networks (ANN) calculating. An application software was developed based on a neural network using back-propagation (BP) algorithm under the UNIX platform by the specified computer language named MYPARAL. This neural network model was used as an auxiliary controller in the temperature control of sinter cooler system in steel plant which is a nonlinear system. The neural controller was trained off-line using the real input-output data as training pairs. We also made the system description of adaptive neural controller on the same temperature control system. We will carry out the whole system simulation to verify the suitability of neural controller in improving the system features.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1997.10a
• /
• pp.109-112
• /
• 1997

In this study, we attempt to improve analytical methods in auditing by applying Artificial Intelligence(AI) methods including Artificial Neural Networks(ANN) and Case-Based Reasoning(CBR), and to perform pattern recognition of the investigation signals generated by analytical procedures. Five years of audited financial data from a large-sized firm were used to calculate four commonly applied financial ratios. This exploratory study shows that the use of AI methods to analyze patterns of related fluctuations across numerous financial ratios provides improved performance in recognizing material misstatements within the financial accounts.

• Journal of Navigation and Port Research
• /
• v.40 no.5
• /
• pp.337-343
• /
• 2016

The core decisions of bulk shipping businesses can be summarized as the timing and the choice of period for which carrying capacity is traded. In particular, frequent decisions to trade freight either with repeated spot transactions or with a one-off long-term deal critically impact business performance. Even though a variety of freight trading strategies can be employed to facilitate the decisions, chartering practitioners have not been active in utilizing these strategies, and academic research has rarely proposed applicable solutions. The specific properties of freight as a tradable commodity are not properly reflected in existing studies, and limitations have been reported in their application to the real world. This research focused on the establishment of applicable freight trading strategies by taking into account two properties of freight: time perishability and term-dependant pricing. In addition to traditional trading strategies, artificial neural networks were applied for the first time to the test of freight trading strategies. The performances of the trading strategies were measured and compared to produce a remarkable outperformance of the ANN. This research is expected to make a significant contribution to chartering practices by enhancing the quality of chartering decisions and eventually enabling the effective management of freight rate risk. In addition to methodological expansion, the result will propose a way to approach the controversial issue of freight market efficiency.

• Structural Engineering and Mechanics
• /
• v.60 no.4
• /
• pp.655-665
• /
• 2016

This paper aims to develop models to accurately predict the behavior of fresh concrete exposed to vibration using artificial neural networks (ANNs) model and regression model (RM). For this purpose, behavior of a full scale precast concrete mold was investigated experimentally and numerically. Experiment was performed under vibration with the use of a computer-based data acquisition system. Transducers were used to measure time-dependent lateral displacements at some points on mold while both mold is empty and full of fresh concrete. Modeling of empty and full mold was made using both ANNs and RM. For the modeling of ANNs: Experimental data were divided randomly into two parts. One of them was used for training of the ANNs and the remaining part was used for testing the ANNs. For the modeling of RM: Sinusoidal regression model equation was determined and the predicted data was compared with measured data. Finally, both models were compared with each other. The comparisons of both models show that the measured and testing results are compatible. Regression analysis is a traditional method that can be used for modeling with simple methods. However, this study also showed that ANN modeling can be used as an alternative method for behavior of fresh concrete exposed to vibration in precast concrete structures.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2008.11a
• /
• pp.801-804
• /
• 2008

Fiber reinforced plastics (FRP) are light in weight, non-corrosive and exhibits high tensile strength. FRPs having superior material properties to corrosive steels have been widely replacing steel bars or tendons used in concrete structures as flexural reinforcements. Although current design guidelines for estimating shear strength of FRP concrete beam follow the format of conventional reinforced concrete design method, there are noticeable differences among the existing formulas in calculating the contributions of concrete to shear resistance. In this paper, the artificial neural network (ANN) technique is employed as an analytical alternative to existing methods for predicting shear capacity of FRP concrete beams. Influential factors on shear strength were identified through literature review and input in ANN and the ANN was trained for the target ultimate shear obtained from database. The results from ANN were compared with existing formulas for its accuracy. It was found that the developed ANN were more closely predicting the test data than those of the currently available predictive equations.

• 제어로봇시스템학회:학술대회논문집
• /
• 1998.10a
• /
• pp.356-361
• /
• 1998

Neural Networks, modeled succinctly from the real nervous system of a living body, can be categorized into two folds; artificial neural network(ANN) and biological neural network(BNN). While the former has been developed to solve practical problems using function approximation capability, pattern classification) clustering algorithm, etc, the latter has been focused on verifying the information processing capability to which brain research gives an impetus, by mimicking real biological systems. However, BNN suffers Iron severe nonlinearities dealt with. A bridge between two neural networks is chaotic neural network(CNN), which simply delineate the real nor-vous system and comprises almost all the ANN structures by selecting parameters. Main research theme of this area is to develop an explanation tool to clarify the information processing mechanism in biological systems and its extension to engineering applications. The CNN has a Gaussian-shaped refractory function with hysteresis effect and the chaotic responses of it have been observed fur a wide range of parameter space. Through the examination of the coupling effects of excitatory and inhibitory connections, the secrets of information processing and memory structure will appear.

• Water Engineering Research
• /
• v.3 no.2
• /
• pp.143-153
• /
• 2002

As the natural flows in rivers dramatically decrease during drought season in Korea, a deterioration of river water quality is accelerated. Thus, consideration of downstream water quality responding to changes in reservoir release is essential for an integrated watershed management with regards to water quantity and quality. In this study, water quality models based on artificial neural networks (ANNs) method were developed using historical downstream water quality (rm $\NH_3$-N) data obtained from a water treatment plant in Geum river and reservoir release data from Daechung dam. A nonlinear multiple regression model was developed and compared with the ANN models. In the models, the rm NH$_3$-N concentration for next time step is dependent on dam outflow, river water quality data such as pH, alkalinity, temperature, and rm $\NH_3$-N of previous time step. The model parameters were estimated using monthly data from Jan. 1993 to Dec. 1998, then another set of monthly data between Jan. 1999 and Dec. 2000 were used for verification. The predictive performance of the models was evaluated by comparing the statistical characteristics of predicted data with those of observed data. According to the results, the ANN models showed a better performance than the regression model in the applied cases.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.12
• /
• pp.2084-2093
• /
• 2016

It is crucial to predict water temperature for aquatic ecosystem studies and management. In this paper, we first address challenging issues in predicting water temperature in a real time manner and propose a distributed computing model to address such issues. Then, we present an Artificial Neural Network (ANN)-based water temperature prediction model developed for the Soyang River and a cyberinfrastructure system called WT-Agabus to run such prediction models in an automated and real time manner. The ANN model is designed to use only weather forecast data (air temperature and rainfall) that can be obtained by invoking the weather forecasting system at Korea Meteorological Administration (KMA) and therefore can facilitate the automated and real time water temperature prediction. This paper also demonstrates how easily and efficiently the real time prediction can be implemented with the WT-Agabus prototype system.

• Computers and Concrete
• /
• v.15 no.4
• /
• pp.503-514
• /
• 2015

Fibre-added concretes are frequently used in large site applications such as slab and airports as well as in bearing system elements or prefabricated elements. It is very difficult to determine the mechanical properties of the fibre-added concretes by experimental methods in situ. The purpose of this study is to develop an artificial neural network (ANN) model in order to predict the compressive and bending strengths of hybrid fibre-added and non-added concretes. The strengths have been predicted by means of the data that has been obtained from destructive (DT) and non-destructive tests (NDT) on the samples. NDTs are ultrasonic pulse velocity (UPV) and Rebound Hammer Tests (RH). 105 pieces of cylinder samples with a dimension of $150{\times}300mm$, 105 pieces of bending samples with a dimension of $100{\times}100{\times}400mm$ have been manufactured. The first set has been manufactured without fibre addition, the second set with the addition of %0.5 polypropylene and %0.5 steel fibre in terms of volume, and the third set with the addition of %0.5 polypropylene, %1 steel fibre. The water/cement (w/c) ratio of samples parametrically varies between 0.3-0.9. The experimentally measured compressive and bending strengths have been compared with predicted results by use of ANN method.