• Magazine of the Korea Concrete Institute
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• v.9 no.2
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• pp.121-132
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• 1997

The thermal behavior of' concrete can be ch;lracterized from a knowledge of concrete ternperatu1.e at early ages, environmental conditions, and cement hydration in the mixture. 'l'o account for thost. interactions, a computer model was developed for prwlicting the temperature pr.ol'ile in hnrdcning c o n c r c t ~ st.r~icture in terms of material and tmvironmcntal factors. The cerncnt hydration cha~.acteristics such as the activating energy, total heat 1ihei.atr.d. anti th\ulcorner degree of' hydration. can represent the internal heat gc,neration. In this study. th(> activating c1ncrgy and the tlcgree of' hydration curve were determined well fmm the rnortn~. compressive strength tests while total amount of heat liberated was determined by tht> isothermal calorimctcr method. The main purpose of' this study is to correlate measured tt>mperaturr distributions in a concrete st1,ucture during thc hardening process with the ~ c s u l t s computed f'ro~n theoretical considrl.ations. Using twodimensional heat transfer model, first. the importance of several parameters will be identified by a parametric analysis. Then, the tcmpcmture distribution of thc cylindrical concrete specimen in the laboratory was mensuwti and compared with that yielded by thc theoretical considel.ations.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.221-229
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• 2023

Urbanization and industrialization have significantly increased the amount of solid waste produced in recent decades, posing considerable disposal problems and environmental burdens. The practice of waste utilization in concrete has gained popularity among construction practitioners and researchers for the efficient use of resources and the transition to the circular economy in construction. This study employed Lytag aggregate, an environmentally friendly pulverized fuel ash-based lightweight aggregate, as a substitute for natural coarse aggregate. At the same time, fly ash, an industrial by-product, was used as a partial substitute for cement. Concrete mix M20 was experimented with using fly ash and Lytag lightweight aggregate. The percentages of fly ash that make up the replacements were 5%, 10%, 15%, 20%, and 25%. The Compressive Strength (CS), Split Tensile Strength (STS), and deflection were discovered at these percentages after 56 days of testing. The concrete cube, cylinder, and beam specimens were examined in the explorations, as mentioned earlier. The results indicate that a 10% substitution of cement with fly ash and a replacement of coarse aggregate with Lytag lightweight aggregate produced concrete that performed well in terms of mechanical properties and deflection. The cementitious composites have varying characteristics as the environment changes. Therefore, understanding their mechanical properties are crucial for safety reasons. CS, STS, and deflection are the essential property of concrete. Machine learning (ML) approaches have been necessary to predict the CS of concrete. The Artificial Fish Swarm Optimization (AFSO), Particle Swarm Optimization (PSO), and Harmony Search (HS) algorithms were investigated for the prediction of outcomes. This work deftly explains the tremendous AFSO technique, which achieves the precise ideal values of the weights in the model to crown the mathematical modeling technique. This has been proved by the minimum, maximum, and sample median, and the first and third quartiles were used as the basis for a boxplot through the standardized method of showing the dataset. It graphically displays the quantitative value distribution of a field. The correlation matrix and confidence interval were represented graphically using the corrupt method.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.223-241
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• 2024

Evaluating the performance of Tunnel Boring Machines (TBMs) stands as a pivotal juncture in the domain of hard rock mechanized tunneling, essential for achieving both a dependable construction timeline and utilization rate. In this investigation, three advanced artificial neural networks namely, gated recurrent unit (GRU), back propagation neural network (BPNN), and simple recurrent neural network (SRNN) were crafted to prognosticate TBM-rate of penetration (ROP). Drawing from a dataset comprising 1125 data points amassed during the construction of the Alborze Service Tunnel, the study commenced. Initially, five geomechanical parameters were scrutinized for their impact on TBM-ROP efficiency. Subsequent statistical analyses narrowed down the effective parameters to three, including uniaxial compressive strength (UCS), peak slope index (PSI), and Brazilian tensile strength (BTS). Among the methodologies employed, GRU emerged as the most robust model, demonstrating exceptional predictive prowess for TBM-ROP with staggering accuracy metrics on the testing subset (R² = 0.87, NRMSE = 6.76E-04, MAD = 2.85E-05). The proposed models present viable solutions for analogous ground and TBM tunneling scenarios, particularly beneficial in routes predominantly composed of volcanic and sedimentary rock formations. Leveraging forecasted parameters holds the promise of enhancing both machine efficiency and construction safety within TBM tunneling endeavors.

• Journal of Korean Tunnelling and Underground Space Association
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• v.13 no.3
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• pp.243-260
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• 2011

To predict TBM performance in design stage is indispensable for its successful application. The NTNU model, one of the representative TBM performance prediction models uses two distinct parameters such as DRI and CLI obtained from three different tests on bored rock cores. Based on DRI and CLI, it is possible to predict TBM advance rate and cutter life in the NTNU model. In this study, NTNU testing methods and their related testing equipments were introduced to measure DRl and CLI for the NTNU model. Then, in order to derive their relationships, the two key parameters measured for 39 domestic rocks were compared with physico-mechanical properties of rock such as uniaxial compressive strength and quartz content. Lastly, the experimental results were also compared with NTNU database to verify their reliability.

• Journal of the Korea Concrete Institute
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• v.25 no.6
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• pp.631-639
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• 2013

Non-linear finite element analysis for newly developed precast concrete details for beam-to-column connection which can be used in moderate seismic region was carried out in this study. Developed precast system is based on composite structure and which have steel tube in column and steel plate in beam. Improving cracking strength of joint under reversed cyclic loading, joint area was casted with ECC (Engineering Cementitious Composites). Since this newly developed precast system have complex sectional properties and newly developed material, new analysis method should be developed. Using embedded elements and models of non-linear finite element analysis program ABAQUS previously tested specimens were successfully analyzed. Analysis results show comparatively accurate and conservative prediction. Using finite element model, effect of axial load magnitude and flexural strength ratio were investigated. Developed connection have optimized performance under axial load of 10~20% of compressive strength of column. Plastic hinge was successfully developed with flexural strength ratio greater than 1.2.

• Journal of the Korea Concrete Institute
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• v.23 no.4
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• pp.413-420
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• 2011

The test results of high-strength concrete beam specimens, which have various combinations of different types of flexural reinforcement and short fibers, were compared with the prediction results of codes, guidelines and models proposed by researchers. The theoretical calculation based on the ultimate strength method of the KCI and ACI Code underestimated the ultimate moments of FRP bar-reinforced beams without fibers. The models proposed by ACI 544.4R and Campione predicted the ultimate moment capacities inaccurately for the FRP bar-reinforced beam with steel fibers, because these models do not consider the increased ultimate compressive strain of fiber reinforced concrete. Bischoff's deflection model predicted the service load deflections reasonably well, while the deflection model of ACI Committee 440 underestimated the deflection of FRP bar-reinforced beams. Because the ACI 440 expression, used to predict member deflection, cannot directly apply to the beams reinforced with different types of reinforcing bars, an alternative method to estimate the deflections of beams with different types of reinforcing bars using the ACI 440 expression was proposed. In addition, Bischoff's approach for computing deflection was extended to include deflection after yielding of the steel reinforcement in the beams reinforced with steel and FRP bars simultaneously.

• Korean Chemical Engineering Research
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• v.46 no.3
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• pp.619-625
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• 2008

The effects of thermal cycling on residual stresses in both inorganic passivation/insulating layer that is deposited by plasma enhanced chemical vapor deposition (PECVD) and organic thin film that is used as a bonding adhesive are evaluated by 4 point bending method and wafer curvature method. $SiO_2/SiN_x$ and BCB (Benzocyclobutene) are used as inorganic and organic layers, respectively. A model about the effect of thermal cycling on residual stress and bond strength (Strain energy release rate), $G_c$, at the interface between inorganic thin film and organic adhesive is developed. In thermal cycling experiments conducted between $25^{\circ}C$ and either $350^{\circ}C$ or $400^{\circ}C$, $G_c$ at the interface between BCB and PECVD $ SiN_x $ decreases after the first cycle. This trend in $G_c$ agreed well with the prediction based on our model that the increase in residual tensile stress within the $SiN_x$ layer after thermal cycling leads to the decrease in $G_c$. This result is compared with that obtained for the interface between BCB and PECVD $SiO_2$, where the relaxation in residual compressive stress within the $SiO_2$ induces an increase in $G_c$. These opposite trends in $G_cs$ of the structures including either PECVD $ SiN_x $ or PECVD $SiO_2$ are caused by reactions in the hydrogen-bonded chemical structure of the PECVD layers, followed by desorption of water.

• Journal of the Korea Concrete Institute
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• v.26 no.3
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• pp.307-313
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• 2014

In order to predict the post-cracking tensile behavior of fiber reinforced concrete, it is necessary to evaluate the fiber orientation factor which indicates the number of fibers bridging a crack. For investigation of fiber orientation factor on a circular cross-section, in this paper, cylindrical steel fiber reinforced concrete specimens were casted with the variables of concrete compressive strength, circular cross-section size, fiber type, and fiber volumetric ratio. The specimens were cut perpendicularly to the casting direction so that the fiber orientation factor could be evaluated through counting the number of fibers on the circular cross-section. From the test results, it was investigated that the fiber orientation factor on a circular cross-section was lower than 0.5 generally adopted, as fibers tended to be perpendicular to the casting direction. In addition, it was observed that the fiber orientation factor decreased with an increase of the number of fibers per unit cross-section area. For rational prediction of the fiber orientation factor on a circular section, a rigorous model and a simplified equation were derived through taking account of a possible fiber inclination angle considering the circular boundary surface. From the comparison of the measured data and the predicted values, it was found that the fiber orientation factor was well predicted by the proposed model. The test results and the proposed model can be useful for researches on structural behavior of steel fiber reinforced columns with a circular cross-section.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.5
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• pp.251-258
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• 2018

In construction site, conventional methods such as jackhammer or explosive methods(dynamite) have been often used for the demolition of structures. Use of those methods are more carefully treated in environmentally and historically sensitive area. For those reasons, use of Soundless Chemical Demolition Agent(SCDA) is getting the spotlight. The SCDA is a powder which has expansive strength when it is mixed with water. In these Characteristics, SCDA can destroy the concrete or rock as it is poured into boreholes of the concrete or rock structures. However, there is no industrial standard for the use of SCDA effectively yet. In this study, experimental study to measure the expansive pressure was conducted depending on various boundary conditions such as waterproof, length of the steel pipe, submerged of steel pipe. Furthermore, computational analysis using damage plasticity model to predict the minimum required pressure of the SCDA for the concrete demolition depending on spacing between holes(k-factor) and compressive strength of the concrete was conducted. Obtained results indicates that water heat dissipation with submerged steel pipe shows the stable pressure for measuring the SCDA and hole distance(k-factor) is the most important factor for crack initiation of concrete.

• Journal of the Korean Geotechnical Society
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• v.39 no.12
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• pp.37-46
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• 2023

Dynamic soil properties are essential factors for predicting the detailed behavior of the ground. However, there are limitations to gathering soil samples and performing additional experiments. In this study, we used an artificial neural network (ANN) to predict dynamic soil properties based on static soil properties. The selected static soil properties were soil cohesion, internal friction angle, porosity, specific gravity, and uniaxial compressive strength, whereas the compressional and shear wave velocities were determined for the dynamic soil properties. The Levenberg-Marquardt and Bayesian regularization methods were used to enhance the reliability of the ANN results, and the reliability associated with each optimization method was compared. The accuracy of the ANN model was represented by the coefficient of determination, which was greater than 0.9 in the training and testing phases, indicating that the proposed ANN model exhibits high reliability. Further, the reliability of the output values was verified with new input data, and the results showed high accuracy.