• Computers and Concrete
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• v.33 no.4
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• pp.409-423
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• 2024

Using an extensive database, a sensitivity analysis across fifteen machine learning (ML) classifiers was conducted to evaluate the impact of various data manipulation techniques, evaluation metrics, and explainability tools. The results of this sensitivity analysis reveal that the examined models can achieve an accuracy ranging from 72-93% in predicting the fire-induced spalling of concrete and denote the light gradient boosting machine, extreme gradient boosting, and random forest algorithms as the best-performing models. Among such models, the six key factors influencing spalling were maximum exposure temperature, heating rate, compressive strength of concrete, moisture content, silica fume content, and the quantity of polypropylene fiber. Our analysis also documents some conflicting results observed with the deep learning model. As such, this study highlights the necessity of selecting suitable models and carefully evaluating the presence of possible outcome biases.

• Computers and Concrete
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• v.33 no.6
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• pp.739-754
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• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• Geomechanics and Engineering
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• v.38 no.3
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• pp.231-247
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• 2024

The use of lime stabilization and geosynthetic reinforcement is a common approach to improve the performance of fine-grained soils in geotechnical applications. However, the impact of this combination on the soil-geosynthetic interaction remains unclear. This study addresses this gap by evaluating the interface efficiency and soil-geosynthetic interaction parameters of lime-stabilized clay (2%, 4%, 6%, and 8% lime content) reinforced with geotextile or geogrid using direct shear tests at various curing times (1, 7, 14, and 28 days). Additionally, machine learning algorithms (Support Vector Machine and Artificial Neural Network) were employed to predict soil shear strength. Findings revealed that lime stabilization significantly increased soil shear strength and interaction parameters, particularly at the optimal lime content (4%). Notably, stabilization improved the performance of soil-geogrid interfaces but had an adverse effect on soil-geotextile interfaces. Furthermore, machine learning algorithms effectively predicted soil shear strength, with sensitivity analysis highlighting lime percentage and geosynthetic type as the most significant influencing factors.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.391-396
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• 2000

Today most companies are designing their automobile shapes by using 3 dimensional CAD software, CATIA. And they used to design 2 dimensional press dies to do some elastic work on their products, but they are currently trying to make use of dimensional software, Pro-Engineer. In this case, they have to change the 3 dimensional product design data to the proper format data for the following process. This paper will show the data loss and the deformation during data transfer between CATIA and Pro-Engineer, and then suggest a solution for these problems. Product's surface will be automatically placed by automatic press tipping angle setting in CATIA to prevent the product from being stuck in the press die. The 2 dimensional section view which is based on the tipping angle setting is created by Z-map. And, to remove the data loss and the data deformation in pro-Engineer, the product surface are delivered to the next process after it is changed to the 2 dimensional Z-map curves in CATIA. finally, this paper suggests an algorithm to develop the automatic design program for the press layout which regenerates product shape surface from the previous process.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.6
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• pp.38-44
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• 2002

Today most companies are designing their automobile shapes by using 3 dimensional CAD software, CATIA. And they used to design 2 dimensional press dies to do some elastic work on their products, but they are currently trying to make use of 3 dimensional software, Pro-Engineer. In this case, they have to change the 3 dimensional product design data to the proper format data for the following process. This paper will show the data loss and the deformation during data transfer between CATIA and Pro-Engineer, and then suggest a solution for these problems. Product's surface will be automatically placed by automatic press tipping angle setting in CATIA to prevent the product from being stuck m the press die. The 2 dimensional section view which is based on the tipping angle setting is created by Z-map. And, to remove the data loss and the data deformation in Pro-Engineer, the product surface are delivered to the next process after it is changed to the 2 dimensional Z-map curves in CATIA. Finally, this paper suggests an algorithm to develop the automatic design program for the press layout which regenerates product shape surface from the previous process.

• Design & Manufacturing
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• v.10 no.2
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• pp.29-33
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• 2016

Production machines have been more important, due to quality level of vehicle motor core is getting higher. That is why, to improve assembly fit of tooling and to be emphasized how much moving down caused of deterioration of high speed press, it is also getting more important parts as solution of problems. To analyze how much move based on condition of movement as tooling and high speed press, and to measure how much impact to embossing height caused of changing movement down. As the result of investigation, in case of material thickness 0.5mm, there is highest pull and force power when emboss height is 0.45mm. If emboss height is less than 0.45mm, pull and force power is getting lower, if emboss height is higher than 0.45mm, it is impossible to make it forming caused of changed press movement, also it has been piercing.

• Geomechanics and Engineering
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• v.20 no.4
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• pp.345-358
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• 2020

Numerous studies have repeatedly demonstrated the efficiency of using skirts to increase the bearing capacity and to reduce settlement of shallow foundations subjected to static loads. However, no efforts have been made to study the efficiency of using these skirts to reduce settlement produced by machine vibration, although machines are very sensitive to settlement and the foundations of these machines should be designed properly to ensure that the settlement produced due to machine vibration is very small. This research has been conducted to investigate the efficiency of using skirts as a technique to reduce the settlement of a strip foundation subjected to machine vibration. A two-dimensional finite element model has been developed, validated, and employed to achieve the aim of the study. The results of the analyses showed that the use of skirts reduces the settlement produced due to machine vibration. However, the percentage decrease of the settlement is remarkably influenced by the density of the soil and the frequency of vibration, where it rises as the frequency of vibration increases and declines as the soil density rises. It was also found that increasing skirt length increases the percentage decrease of the settlement. Importantly, the results obtained from the analyses have been utilized to derive new dynamic impedance values that implicitly consider the presence of skirts. Finally, novel design equations of dynamic impedance that implicitly account to the effect of the skirts have been derived and validated utilizing a new intelligent data driven method. These new equations can be used in future designs of skirted strip foundations subjected to machine vibration.

• Structural Engineering and Mechanics
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• v.83 no.1
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• pp.79-92
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• 2022

In this paper, the impact of a vernacular pozzolanic kaolinite mine on concrete alkali-silica reaction and strength has been evaluated. For making the samples, kaolinite powder with various levels has been used in the quality specification test of aggregates based on the ASTM C1260 standard in order to investigate the effect of kaolinite particles on reducing the reaction of the mortar bars. The compressive strength, X-Ray Diffraction (XRD) and Scanning Electron Microscope (SEM) experiments have been performed on concrete specimens. The obtained results show that addition of kaolinite powder to concrete will cause a pozzolanic reaction and decrease the permeability of concrete samples comparing to the reference concrete specimen. Further, various machine learning methods have been used to predict ASR-induced expansion per different amounts of kaolinite. In the process of modeling methods, optimal method is considered to have the lowest mean square error (MSE) simultaneous to having the highest correlation coefficient (R). Therefore, to evaluate the efficiency of the proposed model, the results of the support vector machine (SVM) method were compared with the decision tree method, regression analysis and neural network algorithm. The results of comparison of forecasting tools showed that support vector machines have outperformed the results of other methods. Therefore, the support vector machine method can be mentioned as an effective approach to predict ASR-induced expansion.

• Geomechanics and Engineering
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• v.37 no.6
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• pp.539-554
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• 2024

This study explores development of prediction model for seismic site classification through the integration of machine learning techniques with horizontal-to-vertical spectral ratio (HVSR) methodologies. To improve model accuracy, the research employs outlier detection methods and, synthetic minority over-sampling technique (SMOTE) for data balance, and evaluates using seven machine learning models using seismic data from KiK-net. Notably, light gradient boosting method (LGBM), gradient boosting, and decision tree models exhibit improved performance when coupled with SMOTE, while Multiple linear regression (MLR) and Support vector machine (SVM) models show reduced efficacy. Outlier detection techniques significantly enhance accuracy, particularly for LGBM, gradient boosting, and voting boosting. The ensemble of LGBM with the isolation forest and SMOTE achieves the highest accuracy of 0.91, with LGBM and local outlier factor yielding the highest F1-score of 0.79. Consistently outperforming other models, LGBM proves most efficient for seismic site classification when supported by appropriate preprocessing procedures. These findings show the significance of outlier detection and data balancing for precise seismic soil classification prediction, offering insights and highlighting the potential of machine learning in optimizing site classification accuracy.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.