• Advances in nano research
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• v.16 no.4
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• pp.375-394
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• 2024

The extensive utilization of concrete has given rise to environmental concerns, specifically concerning the depletion of river sand. To address this issue, waste deposits can provide manufactured-sand (MS) as a substitute for river sand. The objective of this study is to explore the application of machine learning techniques to facilitate the production of manufactured-sand concrete (MSC) containing stone nano-powder through estimating the splitting tensile strength (STS) containing compressive strength of cement (CSC), tensile strength of cement (TSC), curing age (CA), maximum size of the crushed stone (Dmax), stone nano-powder content (SNC), fineness modulus of sand (FMS), water to cement ratio (W/C), sand ratio (SR), and slump (S). To achieve this goal, a total of 310 data points, encompassing nine influential factors affecting the mechanical properties of MSC, are collected through laboratory tests. Subsequently, the gathered dataset is divided into two subsets, one for training and the other for testing; comprising 90% (280 samples) and 10% (30 samples) of the total data, respectively. By employing the generated dataset, novel models were developed for evaluating the STS of MSC in relation to the nine input features. The analysis results revealed significant correlations between the CSC and the curing age CA with STS. Moreover, when delving into sensitivity analysis using an empirical model, it becomes apparent that parameters such as the FMS and the W/C exert minimal influence on the STS. We employed various loss functions to gauge the effectiveness and precision of our methodologies. Impressively, the outcomes of our devised models exhibited commendable accuracy and reliability, with all models displaying an R-squared value surpassing 0.75 and loss function values approaching insignificance. To further refine the estimation of STS for engineering endeavors, we also developed a user-friendly graphical interface for our machine learning models. These proposed models present a practical alternative to laborious, expensive, and complex laboratory techniques, thereby simplifying the production of mortar specimens.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.1
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• pp.39-58
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• 2024

Tunnel Boring Machines (TBM) use multiple disc cutters to excavate tunnels through rock. These cutters wear out due to continuous contact and friction with the rock, leading to decreased cutting efficiency and reduced excavation performance. The rock's abrasivity significantly affects cutter wear, with highly abrasive rocks causing more wear and reducing the cutter's lifespan. The Cerchar Abrasivity Index (CAI) is a key indicator for assessing rock abrasivity, essential for predicting disc cutter life and performance. This study aims to develop a new method for effectively estimating CAI using rock strength, petrological characteristics, linear regression, and machine learning. A database including CAI, uniaxial compressive strength, Brazilian tensile strength, and equivalent quartz content was created, with additional derived variables. Variables for multiple linear regression were selected considering statistical significance and multicollinearity, while machine learning model inputs were chosen based on variable importance. Among the machine learning prediction models, the Gradient Boosting model showed the highest predictive performance. Finally, the predictive performance of the multiple linear regression analysis and the Gradient Boosting model derived in this study were compared with the CAI prediction models of previous studies to validate the results of this research.

• Journal of the Korean Geophysical Society
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• v.5 no.3
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• pp.199-209
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• 2002

We have carried out a nondestructive close examination for the purpose of the structural safety diagnosis of the Dabo Pagoda of Bulkuk temple located in Kyungju, Kyungbuk Korea. For estimating the mechanical properties of each rock block of the pagoda, ultrasonic measurements were conducted at 641 points of 255 blocks. The P-wave velocity ranges from 584m/sec through 5,169m/sec, and averages 2,901m/sec Based on this result, the uniaxial compressive strength was estimated to be $93{\sim}1,943kg/cm^2\;with\;396kg/cm^2$ of average, and the index of weathering is $0.07{\sim}0.88$ with 0.43 of average, which means the moderate degree of weathering. The comparison of the rock strength of each block with the overburden acting on the block reveals that the rock blocks related to the structure of the pagoda are relatively sound for uniform stress, but it is highly possible for a concentrated stress to lead to a partial failure. We suggest a monitoring of cracks due to the concentrated stress. The parapets of 1st and 2nd floors composed of small rock pieces are severely weathered. However, this is not directly related to the structural safety of the pagoda.

• Composites Research
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• v.23 no.4
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• pp.44-51
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• 2010

Recently, to solve the problems associated with the neutralization and corrosion of reinforced concrete compression members, the structural configurations such as CFFT (Concrete Filled GFRP Tube) and RCFFT (Reinforced Concrete Filled GFRR Tube) have been developed and applied to main members of civil engineering structure. These members can increase structural performance in terms of structural stability, ductility as well as chemical resistance compared with conventional concrete structural members. Many researches in numerous institutions to predict the load carrying capacity of the concrete compression member strengthened with FRP materials have been conducted and they have been suggested an equation for the prediction of the load carrying capacity of the members. Through the review of the research results, it was found that their results are similar each other. Moreover, it was also found that the results are not directly applicable to our specimens since the results are largely depended upon the member configurations. Also, since the accurate design criteria for the RC members strengthened with FRP such as RCFFT have not been established properly, relevant theoretical and experimental investigations must be conducted for the application to the practical structures. In this study, structural behavior of RCFFT was evaluated through compressive and quasi-static flexural tests in order to formulate design criteria for the structural design. In addition, the RCFFT members were also investigated to examine their confinement effect and the equations capable of estimating the compressive ultimate strength and flexural stiffness of the RCFFT members were proposed.

• The Journal of Engineering Geology
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• v.28 no.4
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• pp.673-686
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• 2018

The physical properties of rocks constituting the rock mass were analyzed by using various methods such as 7 kinds of physical properties of about 2,400 data. The correlation equation was derived from the correlation equation with the dependent variables by screening independent variables through the significance level using multiple regression analysis. In order to verify the reliability of this equation, verification was performed through comparison with actual data using artificial neural network learning. The analysis results by petrogenesis and strength confirmed that the elastic wave velocity (compressional wave) and elastic modulus as the main influence factors for the independent variables affecting the dependent variables. This proves that most of the correlation equations using the above items are found in existing studies. And through this study, it is confirmed whether the rock classification is based on the above items in various standards. In addition, the analysis results of representative rocks showed a high correlation as the equation for estimating unconfined compressive strength and elastic modulus exceeds the coefficient of determination 0.8.

• Journal of the Korean Geotechnical Society
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• v.30 no.7
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• pp.17-26
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• 2014

Tunneling-induced displacement in a jointed rock mass is an important factor to control tunnel stability and to secure a demanded space and construction quality. The magnitude of the inducible displacements is significantly affected by an elastic modulus and therefore, in a rock mass where a joint controls tunnel behavior, it is very important to estimate an elastic modulus of jointed rock mass reliably. Elastic modulus of jointed rock mass is affected by many factors such as rock type, joint condition, and loading condition. Nevertheless, most existing studies were focused on rough empirical relationships based on compressive loading conditions, which are different from tunnel excavation loading conditions, without a systematic approach of rock, joint, and loading conditions together. Therefore, this study considered rock and joint conditions systematically to estimate an elastic modulus of jointed rock mass under tunnel excavation loading. The controlled factors considered in this study are rock types and joint conditions (joint shear strength, joint inclination angle, number of joint sets, and joint spacing). Numerical parametric studies have been carried out with a consideration of different rock and joint conditions; the results have been compared with existing empirical relationships; and charts of elastic modulus change of different rock and joint conditions have been provided. The results are expected to have a great practical use for estimating the convergence induced by tunnel excavation in jointed rockmass.