• Computers and Concrete
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• v.23 no.1
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• pp.61-68
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• 2019

This paper presents a computational rational model to predict the ultimate and optimized load capacity of reinforced concrete (RC) beams strengthened by a combination of longitudinal and transverse fiber reinforced polymer (FRP) composite plates/sheets (flexure and shear strengthening system). Several experimental and analytical studies on the confinement effect and failure mechanisms of fiber reinforced polymer (FRP) wrapped columns have been conducted over recent years. Although typical axial members are large-scale square/rectangular reinforced concrete (RC) columns in practice, the majority of such studies have concentrated on the behavior of small-scale circular concrete specimens. A high performance concrete, known as polymer concrete, made up of natural aggregates and an orthophthalic polyester binder, reinforced with non-metallic bars (glass reinforced polymer) has been studied. The material is described at micro and macro level, presenting the key physical and mechanical properties using different experimental techniques. Furthermore, a full description of non-metallic bars is presented to evaluate its structural expectancies, embedded in the polymer concrete matrix. In this paper, the mechanism of mechanical interaction of smooth and lugged FRP rods with concrete is presented. A general modeling and application of various elements are demonstrated. The contact parameters are defined and the procedures of calculation and evaluation of contact parameters are introduced. The method of calibration of the calculated parameters is presented. Finally, the numerical results are obtained for different bond parameters which show a good agreement with experimental results reported in literature.

• Advances in Computational Design
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• v.4 no.2
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• pp.119-139
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• 2019

Planning and management building projects should tackle the coordination of works and the management of limited spaces, traffic and supplies. Activities cannot be performed without the resources available and resources cannot be used beyond the capacity of workplaces. Otherwise, workspace congestion will negatively affect the flow of works. Better on-site management allows for substantial productivity improvements and cost savings. The procurement system should be able to manage a wider variety of materials and products of the required quality in order to have less stock, in less time, using less space, with less investment and avoiding multiple storage stations. The objective of this paper is to demonstrate the advantages of using the Chronographic modeling, by combining spatiotemporal technical scheduling with the 4D simulations, the Last Planner System and the Takt-time when modeling the construction of building projects. This paper work toward the aforementioned goal by examining the impact that material flow has on site occupancy. The proposed spatiotemporal model promotes efficient site use, defines optimal site-occupancy and workforce-rotation rates, minimizes intermediate stocks, and ensures a suitable procurement process. This paper study the material flow on the site and consider horizontal and vertical paths, traffic flows and appropriate means of transportation to ensure fluidity and safety. This paper contributes to the existing body of knowledge by linking execution and supply to the spatial and temporal aspects. The methodology compare the performance and procurement processes for the proposed Chronographic model with the Gantt-Precedence diagram. Two examples are presented to demonstrate the benefits of the proposed model and to validate the related concepts. This validation is designed to test the model's graphical ability to simulate construction and procurement.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.120-130
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• 2021

The spinal cord or CSF surgery is a very complex process. It requires continuous pre and post-surgery evaluation to have a better ability to diagnose the disease. To detect automatically the suspected areas of tumors and symptoms of CSF leakage during the development of the tumor inside of the brain. We propose a new method based on using computer software that generates statistical results through data gathered during surgeries and operations. We performed statistical computation and data collection through the Google Source for the UK National Cancer Database. The purpose of this study is to address the above problems related to the accuracy of missing hybrid KNN values and finding the distance of tumor in terms of brain cancer or CSF images. This research aims to create a framework that can classify the damaged area of cancer or tumors using high-dimensional image segmentation and Laplace transformation method. A high-dimensional image segmentation method is implemented by software modelling techniques with measures the width, percentage, and size of cells within the brain, as well as enhance the efficiency of the hybrid KNN algorithm and Laplace transformation make it deal the non-zero values in terms of missing values form with the using of Frobenius Matrix for deal the space into non-zero values. Our proposed algorithm takes the longest values of KNN (K = 1-100), which is successfully demonstrated in a 4-dimensional modulation method that monitors the lighting field that can be used in the field of light emission. Conclusion: This approach dramatically improves the efficiency of hybrid KNN method and the detection of tumor region using 4-D segmentation method. The simulation results verified the performance of the proposed method is improved by 92% sensitivity of 60% specificity and 70.50% accuracy respectively.

• Geomechanics and Engineering
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• v.30 no.3
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• pp.259-272
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• 2022

Rockburst is a dynamic, multivariate, and non-linear phenomenon that occurs in underground mining and civil engineering structures. Predicting rockburst is challenging since conventional models are not standardized. Hence, machine learning techniques would improve the prediction accuracies. This study describes decision based uncertainty models to predict rockburst in underground engineering structures using gradient boosting algorithms (GBM). The model input variables were uniaxial compressive strength (UCS), uniaxial tensile strength (UTS), maximum tangential stress (MTS), excavation depth (D), stress ratio (SR), and brittleness coefficient (BC). Several models were trained using different combinations of the input variables and a 3-fold cross-validation resampling procedure. The hyperparameters comprising learning rate, number of boosting iterations, tree depth, and number of minimum observations were tuned to attain the optimum models. The performance of the models was tested using classification accuracy, Cohen's kappa coefficient (k), sensitivity and specificity. The best-performing model showed a classification accuracy, k, sensitivity and specificity values of 98%, 93%, 1.00 and 0.957 respectively by optimizing model ROC metrics. The most and least influential input variables were MTS and BC, respectively. The partial dependence plots revealed the relationship between the changes in the input variables and model predictions. The findings reveal that GBM can be used to anticipate rockburst and guide decisions about support requirements before mining development.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.385-396
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• 2022

The accurate determination of the subgrade reaction modulus (K_s) of soil is an important factor for geotechnical engineers. This study estimated the K_s of soft soil improved with floating deep cement mixing (DCM) columns. A novel prediction model was developed that emphasizes the accuracy of identifying the most significant parameters of K_s. Several multi-layer perceptron (MLP) models that were trained using the Levenberg Marquardt (LM) backpropagation method were developed to estimate K_s. The models were trained using a reliable database containing the results of 36 physical modelling tests. The input parameters were the undrained shear strength of the DCM columns, undrained shear strength of soft soil, area improvement ratio and length-to-diameter ratio of the DCM columns. Grey wolf optimization (GWO) was coupled with the MLPs to improve the performance indices of the MLPs. Sensitivity tests were carried out to determine the importance of the input parameters for prediction of K_s. The results showed that both the MLP-LM and MLP-GWO methods showed high ability to predict K_s. However, it was shown that MLP-GWO (R = 0.9917, MSE = 0.28 (MN/m²/m)) performed better than MLP-LM (R =0.9126, MSE =6.1916 (MN/m²/m)). This proves the greater reliability of the proposed hybrid model of MLP-GWO in approximating the subgrade reaction modulus of soft soil improved with floating DCM columns. The results revealed that the undrained shear strength of the soil was the most effective factor for estimation of K_s.

• Wind and Structures
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• v.34 no.5
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• pp.407-419
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• 2022

Many factors should be considered by architects and designers for designing a tall building. Wind load is one of these important factors that govern the design of tall building structures and can become a serious challenge when buildings tend to be built very tall and slender. On the other hand, through the initial stages of a design process, choosing the design geometry greatly affects the wind-induced forces on a tall building. With this respect, geometric shapes with 3-fold rotational symmetry are one of the applied plan shapes in tall buildings. This study, therefore, aims to investigate the aerodynamic characteristics of 8 different geometrical shapes using Computational Fluid Dynamics (CFD) by measuring the drag and lift forces. A case study approach was conducted in which different building shape models have the same total gross area and the same height of 300 meters. The simulation was an incompressible transient flow that ran 1700 timesteps (85 seconds on the real-time scale). The results show a great difference between wind-induced force performance of buildings with different plan shapes. Generally, it is stated that the shapes with the same area, but with smaller perimeters, are better choices for reducing the drag force on buildings. Applying the lift force, the results show that the buildings with plan shapes that have rounded corners act better in crosswind flow while, those with sharp corners induce larger forces in the same direction. This study delivers more analytical understanding of building shapes and their behavior against the wind force through the parametric modelling.

• Steel and Composite Structures
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• v.44 no.1
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• pp.33-47
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• 2022

This paper presents a finite element model for predicting the monotonic behaviour of bolted endplate joints connecting steel-concrete composite walls and steel beams. The demountable Hollo-bolts are utilised to facilitate the quick installation and dismantling for replacement and reuse. In the developed model, material and geometric nonlinearities were included. The accuracy of the developed model was assessed by comparing the numerical results with previous experimental tests on hollow/composite column-to-steel beam joints that incorporated endplates and Hollo-bolts. In particular, the Hollo-bolts were modelled with the expanded sleeves involved, and different material properties of the Hollo-bolt shank and sleeves were considered based on the information provided by the manufacture. The developed models, therefore, can be applied in the present study to simulate the wall-to-beam joints with similar structural components and characteristics. Based on the validated model, the authors herein compared the behaviour of wall-to-beam joints of two commonly utilised composite walling systems (Case 1: flat steel plates with headed studs; Case 2: lipped channel section with partition plates). Considering the ease of manufacturing, onsite erection and the pertinent costs, composite walling system with flat steel plates and conventional headed studs (Case 1) was the focus of present study. Specifically, additional headed studs were pre-welded inside the front wall plates to enhance the joint performance. On this basis, a series of parametric studies were conducted to assess the influences of five design parameters on the behaviour of bolted endplate wall-to-beam joints. The initial stiffness, plastic moment capacity, as well as the rotational capacity of the composite wall-to-beam joints based on the numerical analysis were further compared with the current design provision.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.586-597
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• 2022

In this study, a model was developed for the estimating the locational thickness information of the upper clay layer to be used for the consolidation vulnerability evaluation in the Nakdong river estuary. To estimate ground layer thickness information, we developed four spatial estimation models using machine learning algorithms, which are RF (Random Forest), SVR (Support Vector Regression) and GPR (Gaussian Process Regression), and geostatistical technique such as Ordinary Kriging. Among the 4,712 borehole data in the study area collected for model development, 2,948 borehole data with an upper clay layer were used, and Pearson correlation coefficient and mean squared error were used to quantitatively evaluate the performance of the developed models. In addition, for qualitative evaluation, each model was used throughout the study area to estimate the information of the upper clay layer, and the thickness distribution characteristics of it were compared with each other.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.168-168
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• 2023

The frequent occurrence of cyanobacterial harmful algal blooms (CHABs) in inland waters under climate change seriously damages the ecosystem and human health and is becoming a big problem in South Korea. Satellite remote sensing is suggested for effective monitoring CHABs at a larger scale of water bodies since the traditional method based on sparse in-situ networks is limited in space. However, utilizing a standalone variable of satellite reflectances in common CHABs dual-models, which relies on both chlorophyll-a (Chl-a) and phycocyanin or cyanobacteria cells (Cyano-cell), is not fully beneficial because their seasonal variation is highly impacted by surrounding meteorological and bio-environmental factors. Along with the development of Artificial Intelligence (AI), monitoring CHABs from space with analyzing the effects of environmental factors is accessible. This study aimed to investigate the potential application of AI in the dual-model strategy (Chl-a and Cyano-cell are output parameters) for monitoring seasonal dynamics of CHABs from satellites over Korean inland waters. The Sentinel-3 satellite was selected in this study due to the variety of spectral bands and its unique band (620 nm), which is sensitive to cyanobacteria. Via the AI-based feature selection, we analyzed the relationships between two output parameters and major parameters (satellite water-leaving reflectances at different spectral bands), together with auxiliary (meteorological and bio-environmental) parameters, to select the most important ones. Several AI models were then employed for modelling Chl-a and Cyano-cell concentration from those selected important parameters. Performance evaluation of the AI models and their comparison to traditional semi-analytical models were conducted to demonstrate whether AI models (using water-leaving reflectances and environmental variables) outperform traditional models (using water-leaving reflectances only) and which AI models are superior for monitoring CHABs from Sentinel-3 satellite over a Korean inland water body.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1B
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• pp.1-9
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• 2008

The impact of snow accumulation and snowmelt in rainfall-runoff modelling was analyzed for the Soyanggang dam basin by comparing the measured and simulated discharges simulated by the NWS-PC model. Sugawara's conceptual model was used to simulate the snow accumulation and snowmelt phenomena and NWS-PC model was employed to simulate rainfall-runoff. Parameters in model calibration were estimated by the Multi-step Automated Calibration Scheme and optimized using SCE-UA algorithm in each step. The results of the model calibration and verification show that the model considering snowmelt process is better than the one without consideration of snowmelt under the performance criteria such as RMSE, PBIAS, NSE, and PME. The measured discharge time series has over 60 days of persistence. Correlograms for each simulation showed that the simulated discharge with snowmelt model reproduce the persistence closely to the measured discharge's while the one without snow accumulation and snowmelt model reproduce only 20 days of persistence. The study result indicates that the inclusion of snow accumulation and snowmelt model is important for the accurate simulation of rainfall-runoff phenomena in the Soyanggang dam basin.