• Steel and Composite Structures
• /
• v.49 no.5
• /
• pp.473-486
• /
• 2023

A through-railway bridge with an inclined girder has recently been applied to optimize the cross-section of a slender bridge structure in railway bridges. To achieve the additional cross-section optimization effect by the bolted end-plate connection, it is necessary to investigate the application of the bolted end-plate tension connection between the inclined girder and the crossbeam. This basic study was conducted on the application of the bolted end-plate moment connection of crossbeams to half-through girders with inclined webs. The combined behavior of vertical deflection and rotational behavior was observed due to the effect of the web inclination in the inclined girder where the steel crossbeam was connected to the girder by the bolted end-plate moment connection. Therefore, in the experiment, the deflection of the inclined girder was 1.77-2.93 times greater than that of the vertical girder but the lateral deflection of the inclined girder was 0.4 times less than that of the vertical girder. Moreover, the tensile stress of the upper bolts in the inclined girder with low crossbeams was clearly 0.81 times lower than that of the vertical girder. According to the results, the design formula for vertical girders does not reflect the influence of the web inclination. Therefore, this study proposed the design procedures for the inclined girder to apply the bolted end-plate moment connection of the crossbeam to the inclined girder by reflecting the design change factors according to the effect of the web inclination.

• Steel and Composite Structures
• /
• v.44 no.2
• /
• pp.241-254
• /
• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.1
• /
• pp.413-435
• /
• 2018

Although the accuracy of handwritten character recognition based on deep networks has been shown to be superior to that of the traditional method, the use of an overly deep network significantly increases time consumption during parameter training. For this reason, this paper took the training time and recognition accuracy into consideration and proposed a novel handwritten character recognition algorithm with newly designed network structure, which is based on an extended nonlinear kernel residual network. This network is a non-extremely deep network, and its main design is as follows:(1) Design of an unsupervised apriori algorithm for intra-class clustering, making the subsequent network training more pertinent; (2) presentation of an intermediate convolution model with a pre-processed width level of 2;(3) presentation of a composite residual structure that designs a multi-level quick link; and (4) addition of a Dropout layer after the parameter optimization. The algorithm shows superior results on MNIST and SVHN dataset, which are two character benchmark recognition datasets, and achieves better recognition accuracy and higher recognition efficiency than other deep structures with the same number of layers.

• Composites Research
• /
• v.16 no.3
• /
• pp.18-24
• /
• 2003

Sandwich structures have been widely used in the applications of vessel industry, where high structural stiffness is required with small addition of weight. It is so significant to think of the effect of the variables in the design process of the sandwich structure for the concentrated loads. This paper describes the influence of design variables, such as core density, core thickness and face thickness ratio, on the strength of sandwich beam. The theoretical failure loads based on the 2-D elasticity theory agree well with the experimental yield or failure loads, which are measured at the three point bending laboratory test using AS4/3501-6 facing and polyurethane foam core sandwich beam. The comparison of those yield or failure loads was also done with the ratio of the top to bottom face thickness. The theoretical optimum condition is obtained by finding the intersection point of failure modes involved, which gives optimum core density of the sandwich beam for strength and stiffness. In the addition, the effect of unequal face thickness for the optimized and off-optimized sandwich beams for the strength was compared with the ratio of loading length to beam length, and the variations of strength and stiffness were discussed with the relative ratio of core to face mass.

• Journal of the Society of Naval Architects of Korea
• /
• v.29 no.2
• /
• pp.115-122
• /
• 1992

A sandwich element is a special Hybrid structural form of the composite construction, which is consisted of three main parts : thin, stiff and relatively high density faces separated by a thick, light, and weaker core material. In a sandwich construction, the shear deformation of the faces. Therefore, in the calculation of the bending stiffness, the shear effect should be included. In this paper, the minimum weight is selected as an object function, as the weight critical structures are usually composed of these kind of construction. To obtain the minimum weight of sandwich panel, the principle of minimum potential energy is used and as for the design constraints, the allowable bending stress of face material, the allowable shear stress of core material, the allowable value of panel deflection and the wrinkling stress of faces are adopted, as well as the different boundary conditions. For the engineering purpose of sandwich panel design, the results are tabulated, which are calculated by using the nonlinear optimization technique SUMT.

• Steel and Composite Structures
• /
• v.25 no.1
• /
• pp.117-126
• /
• 2017

The component method is an analytical approach for investigating the moment-rotation relationship of steel connections. In this study, the component method was improved from two aspects: (i) load analysis of mechanical model; and (ii) combination of spring elements. An optimized component method with more reasonable component models, spring arrangement position, and boundary conditions was developed using finite element analysis. An experimental testing program in two major-axis and two minor-axis connections under symmetrically loading was carried out to verify this method. The initial rotational stiffness obtained from the optimized component method was consistent with the experimental results. It can be concluded that (i) The coupling stiffness between column and beam flanges significantly affects the effective height of the tensile-column web. (ii) The mechanical properties of the bending components were obtained using an equivalent t-stub model considering the bending capacity of bolts. (iii) Using the optimized mechanical components, the initial rotational stiffness was accurately calculated using the spring system. (iv) The characteristics of moment-rotation relationship for beam to column connections were effectively expressed by the SPRING element analysis model using ABAQUS. The calculations are simpler, and the results are accurate.

• Earthquakes and Structures
• /
• v.17 no.5
• /
• pp.511-519
• /
• 2019

The concrete gravity dam is one of the most important parts of the nation's infrastructure. Besides the benefits, the dam also has some potentially catastrophic disasters related to the life of citizens directly. During the lifetime of service, some degradations in a dam may occur as consequences of operating conditions, environmental aspects and deterioration in materials from natural causes, especially from dynamic loads. Cumulative Absolute Velocity (CAV) plays a key role to assess the operational condition of a structure under seismic hazard. In previous researches, CAV is normally used in Nuclear Power Plant (NPP) fields, but there are no particular criteria or studies that have been made on dam structure. This paper presents a method to calculate the limitation of CAV for the Bohyeonsan Dam in Korea, where the critical Peak Ground Acceleration (PGA) is estimated from twelve sets of selected earthquakes based on High Confidence of Low Probability of Failure (HCLPF). HCLPF point denotes 5% damage probability with 95% confidence level in the fragility curve, and the corresponding PGA expresses the crucial acceleration of this dam. For determining the status of the dam, a 2D finite element model is simulated by ABAQUS. At first, the dam's parameters are optimized by the Minitab tool using the method of Central Composite Design (CCD) for increasing model reliability. Then the Response Surface Methodology (RSM) is used for updating the model and the optimization is implemented from the selected model parameters. Finally, the recorded response of the concrete gravity dam is compared against the results obtained from solving the numerical model for identifying the physical condition of the structure.

• Smart Structures and Systems
• /
• v.26 no.6
• /
• pp.681-692
• /
• 2020

Conventional Piezoelectric Energy Harvesters (CPEH) have been extensively studied for maximizing their electrical output through material selection, geometric and structural optimization, and adoption of efficient interface circuits. In this paper, the performance of Stepped Piezoelectric Energy Harvester (SPEH) under harmonic base excitation is studied analytically, numerically and experimentally. The motivation is to compare the energy harvesting performance of CPEH and SPEHs with the same characteristics (resonant frequency). The results of this study challenge the notion of achieving higher voltage and power output through incorporation of geometric discontinuities such as step sections in the harvester beams. A CPEH consists of substrate material with a patch of piezoelectric material bonded over it and a tip mass at the free end to tune the resonant frequency. A SPEH is designed by introducing a step section near the root of substrate beam to induce higher dynamic strain for maximizing the electrical output. The incorporation of step section reduces the stiffness and consequently, a lower tip mass is used with SPEH to match the resonant frequency to that of CPEH. Moreover, the electromechanical coupling coefficient, forcing function and damping are significantly influenced because of the inclusion of step section, which consequently affects harvester's output. Three different configurations of SPEHs characterized by the same resonant frequency as that of CPEH are designed and analyzed using linear electromechanical model and their performances are compared. The variation of strain on the harvester beams is obtained using finite element analysis. The prototypes of CPEH and SPEHs are fabricated and experimentally tested. It is shown that the power output from SPEHs is lower than the CPEH. When the prototypes with resonant frequencies in the range of 56-56.5 Hz are tested at 1 m/s2, three SPEHs generate power output of 482 μW, 424 μW and 228 μW when compared with 674 μW from CPEH. It is concluded that the advantage of increasing dynamic strain using step section is negated by increase in damping and decrease in forcing function. However, SPEHs show slightly better performance in terms of specific power and thus making them suitable for practical scenarios where the ratio of power to system mass is critical.

• Steel and Composite Structures
• /
• v.44 no.2
• /
• pp.271-281
• /
• 2022

This study discusses challenges of running expandable profile liners (EPLs) to isolate trouble zones in directional section of a deep well, and summary the expandable profile liner technology (EPLT) field trial experience. Technically, the trial result reveals that it is feasible to apply the EPLT solving lost-circulation control problem and wellbore instability in the deep directional section. Propose schemes for optimizing the EPLT operation procedure to break through the existing bottleneck of EPLT in the deep directional section. Better-performing transition joints are developed to improve EPL string reliability in high borehole curvature section. High-performing and reliable expanders reduce the number of trips, offer excellent mechanical shaping efficiency, simplify the EPLT operation procedure. Application of the expansion and repair integrated tool could minimize the risk of insufficient expansion and increase the operational length of the EPL string. The new welding process and integrated automatic welding equipment improve the welding quality and EPL string structural integrity. These optimization schemes and recent new advancements in EPLT can bring significant economic benefits and promote the application of EPLT to meet future challenges.

• Steel and Composite Structures
• /
• v.48 no.2
• /
• pp.179-190
• /
• 2023

Many recent attempts have sought accurate prediction of pile pullout resistance (P_ul) using classical machine learning models. This study offers an improved methodology for this objective. Adaptive neuro-fuzzy inference system (ANFIS), as a popular predictor, is trained by a capable metaheuristic strategy, namely equilibrium optimizer (EO) to predict the P_ul. The used data is collected from laboratory investigations in previous literature. First, two optimal configurations of EO-ANFIS are selected after sensitivity analysis. They are next evaluated and compared with classical ANFIS and two neural-based models using well-accepted accuracy indicators. The results of all five models were in good agreement with laboratory P_uls (all correlations > 0.99). However, it was shown that both EO-ANFISs not only outperform neural benchmarks but also enjoy a higher accuracy compared to the classical version. Therefore, utilizing the EO is recommended for optimizing this predictive tool. Furthermore, a comparison between the selected EO-ANFISs, where one employs a larger population, revealed that the model with the population size of 75 is more efficient than 300. In this relation, root mean square error and the optimization time for the EO-ANFIS (75) were 19.6272 and 1715.8 seconds, respectively, while these values were 23.4038 and 9298.7 seconds for EO-ANFIS (300).