• International Journal of Oral Biology
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• v.45 no.4
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• pp.190-196
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• 2020

Several factors, including genetic and environmental insults, impede protein folding and secretion in the endoplasmic reticulum (ER). Accumulation of unfolded or mis-folded protein in the ER manifests as ER stress. To cope with this morbid condition of the ER, recent data has suggested that the intracellular event of an unfolded protein response plays a critical role in managing the secretory load and maintaining proteostasis in the ER. Tauroursodeoxycholic acid (TUDCA) is a chemical chaperone and hydrophilic bile acid that is known to inhibit apoptosis by attenuating ER stress. Numerous studies have revealed that TUDCA affects hepatic diseases, obesity, and inflammatory illnesses. Recently, molecular regulation of ER stress in tooth development, especially during the secretory stage, has been studied. Therefore, in this study, we examined the developmental role of ER stress regulation in tooth morphogenesis using in vitro organ cultivation methods with a chemical chaperone treatment, TUDCA. Altered cellular events including proliferation, apoptosis, and dentinogenesis were examined using immunostaining and terminal deoxynucleotidyl transferase dUTP nick end labeling assay. In addition, altered localization patterns of the formation of hard tissue matrices related to molecules, including amelogenin and nestin, were examined to assess their morphological changes. Based on our findings, modulating the role of the chemical chaperone TUDCA in tooth morphogenesis, especially through the modulation of cellular proliferation and apoptosis, could be applied as a supporting data for tooth regeneration for future studies.

• Structural Engineering and Mechanics
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• v.81 no.3
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• pp.293-303
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• 2022

As infrastructure ages and traffic load increases, serious public concerns have arisen for the well-being of bridges. The current health monitoring practice focuses on large-scale bridges rather than short span bridges. However, it is critical that more attention should be given to these behind-the-scene bridges. The relevant information about the construction methods and as-built properties are most likely missing. Additionally, since the condition of a bridge has unavoidably changed during service, due to weathering and deterioration, the material properties and boundary conditions would also have changed since its construction. Therefore, it is not appropriate to continue using the design values of the bridge parameters when undertaking any analysis to evaluate bridge performance. It is imperative to update the model, using finite element (FE) analysis to reflect the current structural condition. In this study, a FE model is established to simulate a concrete culvert bridge in New South Wales, Australia. That model, however, contains a number of parameter uncertainties that would compromise the accuracy of analytical results. The model is therefore updated with a neural network (NN) optimisation algorithm incorporating Monte Carlo (MC) simulation to minimise the uncertainties in parameters. The modal frequency and strain responses produced by the updated FE model are compared with the frequency and strain values on-site measured by sensors. The outcome indicates that the NN model updating incorporating MC simulation is a feasible and robust optimisation method for updating numerical models so as to minimise the difference between numerical models and their real-world counterparts.

• Steel and Composite Structures
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• v.43 no.6
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• pp.735-757
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• 2022

The behavior of shear deficient under-balanced reinforced concrete beams with rectangular cross-sections, which were externally strengthened with CFRP composite along shear spans, was experimentally investigated under vertical load. One of the specimens represents a reference beam without CFRP strengthening and the other specimens have different width/strip spacing ratios (w_f/s_f). The optimum strip in terms of w_f/s_f, which will bring the beam behavior to the ideal level in terms of strength and ductility, was determined according to the regulations. When the w_f/s_f ratio exceeds 0.55, the behavior of the beam shifted from shear failure to bending failure. However, it has been observed that the w_f/s_f ratio should be increased up to 0.82 in order for the beam to reach sufficient shear reserve value according to the codes. It is also observed that the direction and weight of the CFRP composite are one of the most critical factors and 240 gr/m² CFRP strips experienced sudden ruptures in the shear span after the cracking of the concrete. It is considered as a deficiency that the empirical shear capacity formulas given for the beams reinforced with CFRP in the regulations do not take into account both direction and weight of CFRP composites.

• Wind and Structures
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• v.35 no.1
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• pp.55-66
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• 2022

Tall buildings are often subjected to steady and unsteady forces due to external wind flows. Measurement and mitigation of these forces becomes critical to structural design in engineering applications. Over the last few decades, many approaches such as modification of the external geometry of structures have been investigated to mitigate wind-induced load. One such proven geometric modification involved the rounding of sharp corners. In this work, we systematically analyze the impact of rounded corner radii on the reducing the flow-induced loading on a square cylinder. We perform 3-Dimensional (3D) simulations for high Reynolds number flows (Re=1 × 105) which are more likely to be encountered in practical applications. An Improved Delayed Detached Eddy Simulation (IDDES) method capable of capturing flow accurately at large Reynolds numbers is employed in this study. The IDDES formulation uses a k-ω Shear Stress Transport (SST) model for near-wall modelling that prevents mesh-induced separation of the boundary layer. The effects of these corner modifications are analyzed in terms of the resulting variations in the mean and fluctuating components of the aerodynamic forces compared to a square cylinder with no geometric changes. Plots of the angular distribution of the mean and fluctuating coefficient of pressure along the square cylinder's surface illustrate the effects of corner modifications on the different parts of the cylinder. The windward corner's separation angle was observed to decrease with an increase in radius, resulting in a narrower and longer recirculation region. Furthermore, with an increase in radius, a reduction in the fluctuating lift, mean drag, and fluctuating drag coefficients has been observed.

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

• Structural Engineering and Mechanics
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• v.81 no.6
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• pp.715-728
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• 2022

A new type of buckling-restrained braces (BRBs) with a longitudinally profiled steel plate working as the core (LPBRB) is proposed and experimentally investigated. Different from conventional BRBs with a constant thickness core, both stiffness and strength of the longitudinally profiled steel core along its longitudinal direction can change through itself variable thickness, thus the construction of LPBRB saves material and reduces the processing cost. Four full-scale component tests were conducted under quasi-static cyclic loading to evaluate the seismic performance of LPBRB. Three stiffening methods were used to improve the fatigue performance of LPBRBs, which were bolt-assembled T-shaped stiffening ribs, partly-welded stiffening ribs and stiffening segment without rib. The experimental results showed LPBRB specimens displayed stable hysteretic behavior and satisfactory seismic property. There was no instability or rupture until the axial ductility ratio achieved 11.0. Failure modes included the out-of-plane buckling of the stiffening part outside the restraining member and core plate fatigue fracture around the longitudinally profiled segment. The effect of the stiffening methods on the fatigue performance is discussed. The critical buckling load of longitudinally profiled segment is derived using Euler theory. The local bulging behavior of the outer steel tube is analyzed with an equivalent beam model. The design recommendations for LPBRB are presented finally.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Smart Structures and Systems
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• v.29 no.1
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• pp.17-28
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• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

• Environmental and Resource Economics Review
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• v.31 no.2
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• pp.141-163
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• 2022

This paper empirically analyzes the effect of renewable electricity generation on the System Marginal Price (SMP) in Korea. Using an ARX-GARCHX model with hourly data from 2016 to 2020, we evaluate SMP determinants and merit order effects. As a result, we find that solar and wind power, as well as gas price and total load, play a critical role in the SMP. In particular, solar power reduces the SMP level but raises volatility during peak and off-peak periods. This result implies that SMP may fall as renewable electricity generation increases, leading to a decrease in the profitability of existing power plants and investment in renewables. On the other hand, even if the subsidy of renewable energy increases the burden on the SMP, it can be offset by the merit order effect, which lowers the SMP.

• Steel and Composite Structures
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• v.46 no.4
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• pp.471-483
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• 2023

This paper presents an analytical hyperbolic theory based on the refined shear deformation theory for mechanical stability analysis of the simply supported advanced composites plates (exponentially, sigmoidal and power-law graded) under triangular, trapezoidal and uniform uniaxial and biaxial loading. The developed model ensures the boundary condition of the zero transverse stresses at the top and bottom surfaces without using the correction factor as first order shear deformation theory. The mathematical formulation of displacement contains only four unknowns in which the transverse deflection is divided to shear and bending components. The current study includes the effect of the geometric imperfection of the material. The modeling of the micro-void presence in the structure is based on the both true and apparent density formulas in which the porosity will be dense in the mid-plane and zero in the upper and lower surfaces (free surface) according to a logarithmic function. The analytical solutions of the uniaxial and biaxial critical buckling load are determined by solving the differential equilibrium equations of the system with the help of the Navier's method. The correctness and the effectiveness of the proposed HyRPT is confirmed by comparing the results with those found in the open literature which shows the high performance of this model to predict the stability characteristics of the FG structures employed in various fields. Several parametric analyses are performed to extract the most influenced parameters on the mechanical stability of this type of advanced composites plates.