• Design & Manufacturing
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• v.14 no.3
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• pp.50-56
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• 2020

Ceramic tile is widely used as a floor or interior decoration of buildings. The main processes are raw material blending, molding, drying, firing, etc., and since dimensional and quality stability are very important, they are generally molded by a dry press method. In ceramic tile molds, there is a liner that can be easily replaced in case of wear. The liner is constantly abrasion due to a continuous pressing process during tile forming, and it is required to be replaced every certain period. Even in the liner, use a wear-resistant fitting material only in areas where wear is concentrated. However, there was a risk that the fitting material was applied to large-sized tile molding due to problems such as damage to the molding machine and decrease in productivity when detached during the actual tile molding process due to weak fitting strength with the liner. Therefore, in this study, thermal-structural analysis for fitting tolerance analysis and structural analysis for fitting force analysis were performed for the shrink fit process of the fitting material.

• Applied Biological Chemistry
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• v.32 no.3
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• pp.216-221
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• 1989

Soaking of soybeans and the subsequent effect on cooking kinetics were investigated by the means of puncture test and shear press with Instron universal testing machine. Soaked soybeans were water cooked at temperatures of $90{\sim}135^{\circ}C$ adjusted with oil bath. Instron puncture force of 0.15kg and shear force of 1.2kg/g-soybean were appeared as the eating soft texture by sensory evaluation. Softening activation energies of yellow soybeans for puncture and shear force were 14,540cal/g-mole and 21,374cal/g-mole. z-values were calculated as $42.1^{\circ}C$ and $37.4^{\circ}C$, respectively.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Structural Engineering and Mechanics
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• v.60 no.6
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• pp.939-952
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• 2016

A compression to tensile load transforming (CTT) device was developed to determine indirect tensile strength of concrete material. Before CTT test, Particle flow code was used for the determination of the standard dimension of physical samples. Four numerical models with different dimensions were made and were subjected to tensile loading. The geometry of the model with ideal failure pattern was selected for physical sample preparation. A concrete slab with dimensions of $15{\times}19{\times}6cm$ and a hole at its center was prepared and subjected to tensile loading using this special loading device. The ratio of hole diameter to sample width was 0.5. The samples were made from a mixture of water, fine sand and cement with a ratio of 1-0.5-1, respectively. A 30-ton hydraulic jack with a load cell applied compressive loading to CTT with the compressive pressure rate of 0.02 MPa per second. The compressive loading was converted to tensile stress on the sample because of the overall test design. A numerical modeling was also done to analyze the effect of the hole diameter on stress concentrations of the hole side along its horizontal axis to provide a suitable criterion for determining the real tensile strength of concrete. Concurrent with indirect tensile test, the Brazilian test was performed to compare the results from two methods and also to perform numerical calibration. The numerical modeling shows that the models have tensile failure in the sides of the hole along the horizontal axis before any failure under shear loading. Also the stress concentration at the edge of the hole was 1.4 times more than the applied stress registered by the machine. Experimental Results showed that, the indirect tensile strength was clearly lower than the Brazilian test strength.

• Fashion & Textile Research Journal
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• v.21 no.3
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• pp.267-276
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• 2019

This study develops 3D-printed-garment collections for a fashion show presentation. A design concept using traditional patterns that consisted of garments regarding the limitation of the printing technology was investigated in order to develop the collection. The structures of the connecting joints of the textile parts which could be easily and sturdily interconnected were invented. Wearability as garments that could be naturally worn on the human body were sought. As a result, four 3D-printed-garments were developed. The 1st garment composed of objects based on a 'Yeon-Dang-Cho'-pattern was constructed as a geometric robe style using a FDM 3D printer and transparent TPU filaments. The 2nd and 3rd 3D-printed-garments composed of an object based on a 'Boe-Sang-Hwa'-pattern was constructed as a distorted one-piece exaggerating the silhouettes of shoulders and waist parts as well as a straight asymmetric tunic style that used the same printer and material as the 1st garment. The last garment composed of an object based on a 'Boe-Sang-Hwa'-pattern printed using a SLA 3D printer and flexible-liquid-resin was constructed attaching the objects on the fabric material by the hot-press machine. The four developed garments were presented in the opening fashion show of 'the 6th International 3D-printing Korea Expo'. This study provides a basic case for related studies to adapt 3D-printing technology in textile pattern development of garment construction.

• Geomechanics and Engineering
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• v.16 no.6
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• pp.643-654
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• 2018

Tunnel excavation leads to a disturbance on the initial stress balance of surrounding soils, which causes convergences around the tunnel and settlements at the ground surface. Considering the effective impact of settlements on the structures at the surface, it is necessary to estimate them, especially in urban areas. In the present study, ground settlements due to the excavation of East-West Line 7 of the Tehran Metro (EWL7) and the Abuzar tunnels are evaluated and the effect of the lateral earth pressure coefficient ($K_0$) on their extension is investigated. The excavation of the tunnels was performed by TBMs (Tunnel Boring Machines). The coefficient of lateral earth pressure ($K_0$) is one of the most important geotechnical parameters for tunnel design and is greatly influenced by the geological characteristics of the surrounding soil mass along the tunnel route. The real (in-situ) settlements of the ground surface were measured experimentally using leveling methods along the studied tunnels and the results were compared with evaluated settlements obtained from both semi-empirical and numerical methods (using the finite difference software FLAC3D). The comparisons permitted to show that the adopted numerical models can effectively be used to predict settlements induced by a tunnel excavation. Then a numerical parametric study was conducted to show the influence of the $K_0$ values on the ground settlements. Numerical investigations also showed that the shapes of settlement trough of the studied tunnels, in a transverse section, are not similar because of their different diameters and depths of the tunnels.

• Structural Engineering and Mechanics
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• v.71 no.1
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• pp.57-63
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• 2019

During the last two decades, much joint research regarding vibration based methods has been done, leading to developing various algorithms and techniques. These algorithms and techniques can be divided into modal methods and signal methods. Although modal methods have been widely used for health monitoring and damage detection, signal methods due to higher efficiency have received considerable attention in various fields, including aerospace, mechanical and civil engineering. Signal-based methods are derived directly from the recorded responses through signal processing algorithms to detect damage. According to different signal processing techniques, signal-based methods can be divided into three categories including time domain methods, frequency domain methods, and time-frequency domain methods. The frequency domain methods are well-known and interest in using them has increased in recent years. To determine dynamic behaviours, to identify systems and to detect damages of bridges, different methods and algorithms have been proposed by researchers. In this study, a new algorithm to detect seismic damage in the bridge's piers is suggested. To evaluate the algorithm, an analytical model of a bridge with simple spans is used. Based on the algorithm, before and after damage, the bridge is excited by a sine force, and the piers' responses are measured. The dynamic specifications of the bridge are extracted by Power Spectral Density function. In addition, the Least Square Method is used to detect damage in the bridge's piers. The results indicate that the proposed algorithm can identify the seismic damage effectively. The algorithm is output-only method and measuring the excitation force is not needed. Moreover, the proposed approach does not need numerical models.

• Structural Engineering and Mechanics
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• v.69 no.2
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• pp.221-230
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• 2019

In this paper, shear behavior of non-persistent joint surrounded in concrete and gypsum layers has been investigated using experimental test and numerical simulation. Two types of mixture were prepared for this study. The first type consists of water and gypsum that were mixed with a ratio of water/gypsum of 0.6. The second type of mixture, water, sand and cement were mixed with a ratio of 27%, 33% and 40% by weight. Shear behavior of a non-persistent joint embedded in these specimens is studied. Physical models consisting of two edge concrete layers with dimensions of 160 mm by 130 mm by 60 mm and one internal gypsum layer with the dimension of 16 mm by 13 mm by 6 mm were made. Two horizontal edge joints were embedded in concrete beams and one angled joint was created in gypsum layer. Several analyses with joints with angles of $0^{\circ}$, $30^{\circ}$, and $60^{\circ}$ degree were conducted. The central fault places in 3 different positions. Along the edge joints, 1.5 cm vertically far from the edge joint face and 3 cm vertically far from the edge joint face. All samples were tested in compression using a universal loading machine and the shear load was induced because of the specimen geometry. Concurrent with the experiments, the extended finite element method (XFEM) was employed to analyze the fracture processes occurring in a non-persistent joint embedded in concrete and gypsum layers using Abaqus, a finite element software platform. The failure pattern of non-persistent cracks (faults) was found to be affected mostly by the central crack and its configuration and the shear strength was found to be related to the failure pattern. Comparison between experimental and corresponding numerical results showed a great agreement. XFEM was found as a capable tool for investigating the fracturing mechanism of rock specimens with non-persistent joint.

• Computers and Concrete
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• v.27 no.4
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• pp.333-341
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• 2021

Steel slag, an industrial reject from the steel rolling process, has been identified as one of the suitable, environmentally friendly materials for concrete production. Given that the coarse aggregate portion represents about 70% of concrete constituents, other economic approaches have been found in the use of alternative materials such as steel slag in concrete. Unfortunately, a standard framework for its application is still lacking. Therefore, this study proposed functional model equations for the determination of strength properties (compression and splitting tensile) of steel slag aggregate concrete (SSAC), using gene expression programming (GEP). The study, in the experimental phase, utilized steel slag as a partial replacement of crushed rock, in steps 20%, 40%, 60%, 80%, and 100%, respectively. The predictor variables included in the analysis were cement, sand, granite, steel slag, water/cement ratio, and curing regime (age). For the model development, 60-75% of the dataset was used as the training set, while the remaining data was used for testing the model. Empirical results illustrate that steel aggregate could be used up to 100% replacement of conventional aggregate, while also yielding comparable results as the latter. The GEP-based functional relations were tested statistically. The minimum absolute percentage error (MAPE), and root mean square error (RMSE) for compressive strength are 6.9 and 1.4, and 12.52 and 0.91 for the train and test datasets, respectively. With the consistency of both the training and testing datasets, the model has shown a strong capacity to predict the strength properties of SSAC. The results showed that the proposed model equations are reliably suitable for estimating SSAC strength properties. The GEP-based formula is relatively simple and useful for pre-design applications.

• Earthquakes and Structures
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• v.20 no.1
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• pp.109-122
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• 2021

Significant progress in the oil and gas industry advances the application of pipeline into an intelligent era, which poses rigorous requirements on pipeline safety, reliability, and maintainability, especially when crossing seismic zones. In general, strike-slip faults are prone to induce large deformation leading to local buckling and global rupture eventually. To evaluate the performance and safety of pipelines in this situation, numerical simulations are proved to be a relatively accurate and reliable technique based on the built-in physical models and advanced grid technology. However, the computational cost is prohibitive, so one has to wait for a long time to attain a calculation result for complex large-scale pipelines. In this manuscript, an efficient and accurate surrogate model based on machine learning is proposed for strain demand prediction of buried X80 pipelines subjected to strike-slip faults. Specifically, the support vector regression model serves as a surrogate model to learn the high-dimensional nonlinear relationship which maps multiple input variables, including pipe geometries, internal pressures, and strike-slip displacements, to output variables (namely tensile strains and compressive strains). The effectiveness and efficiency of the proposed method are validated by numerical studies considering different effects caused by structural sizes, internal pressure, and strike-slip movements.