• Smart Structures and Systems
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• v.26 no.5
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• pp.591-603
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• 2020

Dynamic deflection monitoring is an essential and critical part of structural health monitoring for high-speed railway bridges. Two critical problems need to be addressed when using inclinometer sensors for such applications. These include constructing a general representation model of inclination-deflection and addressing the ill-posed inverse problem to obtain the accurate dynamic deflection. This paper provides a dynamic deflection monitoring method with the placement of optimal inclinometer sensors for high-speed railway bridges. The deflection shapes are reconstructed using the inclination-deflection transformation model based on the differential relationship between the inclination and displacement mode shape matrix. The proposed optimal sensor configuration can be used to select inclination-deflection transformation models that meet the required accuracy and stability from all possible sensor locations. In this study, the condition number and information entropy are employed to measure the ill-condition of the selected mode shape matrix and evaluate the prediction performance of different sensor configurations. The particle swarm optimization algorithm, genetic algorithm, and artificial fish swarm algorithm are used to optimize the sensor position placement. Numerical simulation and experimental validation results of a 5-span high-speed railway bridge show that the reconstructed deflection shapes agree well with those of the real bridge.

• Journal of Korean Association for Spatial Structures
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• v.4 no.4 s.14
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• pp.91-98
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• 2004

Genetic algorithm is one of the best ways to solve a discrete variable optimization problem. This method is an unconstrained optimization technique, so the constraints are handled in an implicit manner. The most popular way of handling constraints is to transform the original constrained problem into an unconstrained problem, using the concept of penalty function. I present the 3 fitness functions which represent the reject strategy, the penalty strategy, and the combined strategy. I make the design program using the 3 fitness Auctions and it is applied to the design problem of a gable frame and a 2 story 3 span frame.

• Computers and Concrete
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• v.24 no.4
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• pp.313-327
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• 2019

The purpose of the present study is to present a new approach to designing and selecting the details of multidimensional continuous RC beam by applying all strength, serviceability, ductility and other constraints based on ACI318-14 using Teaching Learning Based Optimization (TLBO) algorithm. The optimum reinforcement detailing of longitudinal bars is done in two steps. in the first stage, only the dimensions of the beam in each span are considered as the variables of the optimization algorithm. in the second stage, the optimal design of the longitudinal bars of the beam is made according to the first step inputs. In the optimum shear reinforcement, using gradient-based methods, the most optimal possible mode is selected based on the existing assumptions. The objective function in this study is a cost function that includes the cost of concrete, formwork and reinforcing steel bars. The steel used in the objective function is the sum of longitudinal and shear bars. The use of a catalog list consisting of all existing patterns of longitudinal bars based on the minimum rules of the regulation in the second stage, leads to a sharp reduction in the volume of calculations and the achievement of the best solution. Three example with varying degrees of complexity, have been selected in order to investigate the optimal design of the longitudinal and shear reinforcement of continuous beam.

• Smart Structures and Systems
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• v.16 no.2
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• pp.243-262
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• 2015

Optimal sensor placement (OSP) is a critical issue in construction and implementation of a sophisticated structural health monitoring (SHM) system. The uncertainties in the identified structural parameters based on the measured data may dramatically reduce the reliability of the condition evaluation results. In this paper, the information entropy, which provides an uncertainty metric for the identified structural parameters, is adopted as the performance measure for a sensor configuration, and the OSP problem is formulated as the multi-objective optimization problem of extracting the Pareto optimal sensor configurations that simultaneously minimize the appropriately defined information entropy indices. The nondirective movement glowworm swarm optimization (NMGSO) algorithm (based on the basic glowworm swarm optimization (GSO) algorithm) is proposed for identifying the effective Pareto optimal sensor configurations. The one-dimensional binary coding system is introduced to code the glowworms instead of the real vector coding method. The Hamming distance is employed to describe the divergence of different glowworms. The luciferin level of the glowworm is defined as a function of the rank value (RV) and the crowding distance (CD), which are deduced by non-dominated sorting. In addition, nondirective movement is developed to relocate the glowworms. A numerical simulation of a long-span suspension bridge is performed to demonstrate the effectiveness of the NMGSO algorithm. The results indicate that the NMGSO algorithm is capable of capturing the Pareto optimal sensor configurations with high accuracy and efficiency.

• Journal of Advanced Navigation Technology
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• v.16 no.6
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• pp.960-967
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• 2012

Wireless sensor network is used at the environment to obtain nearby information and since such information is transferred through wireless link, it causes unnecessary re-sending and disadvantage of big energy consumption at node. Because of this to select reliable, energy effective link, method of estimating quality on wireless link using RSSI(received signal strength indication), LQI(link quality indication), etc is needed on wireless link. To set up path extending survival time by reducing energy consumption of nodes at the wireless sensor network, the thesis selects with small standard deviation of LQI after obtaining LQI within each path. Additionally, LQI standard deviation routing algorithm is compared based on LQI algorithm such as minimum-LQI, hop-LQI weight and RF output -7dBm. According to the outcome, the algorithm suggested has superior characters such as the number of node, retransmission rate and network life span respectively compared to existing algorithm. Therefore, energy consumption can be efficiently reduced in case that LQI standard deviation routing scheme suggested by this paper is adapted to wireless sensor network.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.809-824
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• 2017

The accurate evaluation of wind characteristics and wind-induced structural responses during a typhoon is of significant importance for bridge design and safety assessment. This paper presents an expectation maximization (EM) algorithm-based angular-linear approach for probabilistic modeling of field-measured wind characteristics. The proposed method has been applied to model the wind speed and direction data during typhoons recorded by the structural health monitoring (SHM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. In the summer of 2015, three typhoons, i.e., Typhoon Chan-hom, Typhoon Soudelor and Typhoon Goni, made landfall in the east of China and then struck the Jiubao Bridge. By analyzing the wind monitoring data such as the wind speed and direction measured by three anemometers during typhoons, the wind characteristics during typhoons are derived, including the average wind speed and direction, turbulence intensity, gust factor, turbulence integral scale, and power spectral density (PSD). An EM algorithm-based angular-linear modeling approach is proposed for modeling the joint distribution of the wind speed and direction. For the marginal distribution of the wind speed, the finite mixture of two-parameter Weibull distribution is employed, and the finite mixture of von Mises distribution is used to represent the wind direction. The parameters of each distribution model are estimated by use of the EM algorithm, and the optimal model is determined by the values of $R^2$ statistic and the Akaike's information criterion (AIC). The results indicate that the stochastic properties of the wind field around the bridge site during typhoons are effectively characterized by the proposed EM algorithm-based angular-linear modeling approach. The formulated joint distribution of the wind speed and direction can serve as a solid foundation for the purpose of accurately evaluating the typhoon-induced fatigue damage of long-span bridges.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.8A
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• pp.1218-1226
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• 1999

In decoding the turbo codes, the sliding window BCJR algorthm, derived from the BCJR algorithm, permits a continuous decoding of the coded sequence without requiring trellis fermination of the constituent codes and uses reduced memory span. However, the number of computation required is greater than that of BCJR algorithm and no study on the effect of the window length has been reported. In this paper, we propose an eddicient sliding window type scheme which maintains the advantages of the conventional sliding window algorithm, reduces its computational burdens, and improves is BER performance. A guideline is first presented to determine the proper window length and then a computationally efficient sliding window BCJR algorithm is obtained by allowing the window to be forwarded in multi-step. Simulation results show that the proposed scheme outperforms the conventional sliding window BCJR algorithm with reduced complexity. It gains 0.1dB SNR improvements over the conventional method for the constraint length 3 and BER $10^{-4}$

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.171-175
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• 2014

Gu$\acute{e}$ret et al. tries to obtain the solution using linear programming with $O(m^4)$ time complexity for cane sugar production problem a kind of bin packing problem that is classified as NP-complete problem. On the other hand, this paper suggests the maximum loss of lot first production greedy rule algorithm with O(mlogm) polynomial time complexity underlying assumption of the polynomial time rule to find the solution is exist. The proposed algorithm sorts the lots of sugar loss slope into descending order. Then, we select the lots for each slot production capacity only, and swap the exhausted life span of lots for lastly selected lots. As a result of experiments, this algorithm reduces the $O(m^4)$ of linear programming to O(mlogm) time complexity. Also, this algorithm better result than linear programming.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.6
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• pp.87-94
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• 2006

Estimating moving vehicle loads is important in modeling design loads for bridge design and construction. The paper proposes a moving force identification algorithm using moment influence lines measured at multi-axes. Density estimation function was applied to estimate more than two wheel loads when estimated load values fluctuated severely. The algorithm has been examined through simulation studies on a simple-span plate-girder bridge. Influences of measurement noise and error in velocity on the identification results were investigated in the simulation study. Also, laboratory experiments were carried out to examine the algorithm. The load identification capability was dependent on the type and speed of moving loads, but the developed algorithm could identify loads within 10% error in maximum.

• Journal of Korean Society of Steel Construction
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• v.19 no.6
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• pp.695-702
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• 2007

The Thermal Prestressing Method for continuous composite girder bridges is a new design and construction method developed to induce initial composite stresses in the concrete slab at negative bending regions. Due to the induced initial stresses, prevention of tensile cracks at the concrete slab, reduction of steel girder section, and reduction of reinforcing bars are possible. Thus, the construction efficiency can be improved and the construction can be made more economical. The method for determining the optimum heating region of the thermal prestressing method has not been established although such method is essential for improving the efficiency of the design process. The trial-and-error method used in previous studies is far from efficient, and a more rational method for computing optimal heating region is required. In this study, an efficient method for determining the optimum heating region in using the thermal prestressing method was developed based on the neural network algorithm, which is widely adopted to pattern recognition, optimization, diagnosis, and estimation problems in various fields. Back-propagation algorithm, commonly used as a learning algorithm in neural network problems, was used for the training of the neural network. Through case studies of two-span and three-span continuous composite girder bridges using the developed procedure, the optimal heating regions were obtained.