PUNCHING SHEAR BEHAVIOR OF THREE DIMENSION TEXTILES REINFORCED CEMENTITIOUS COMPOSITE PLATES

Self compacting mortars (SCMs) plate specimens with dimension of (500×500×40) mm were cast with three-dimension (3D) textile glass fiber having three diverse thicknesses 6, 10, and 15mm to measure their punching strength. Plates with one and two layers of chicken wires, as well as micro steel fiber of 0.75 % volume fraction were tested under punching for comparison. Punching shear tests have been carried out by applying concentrated load with steel cylinder of 50mm diameter and 10 mm height. The mechanical behavior of SCMs plate was discussed in terms of observed behavior, ultimate load, load - deflection curves, and crack pattern. The results indicated an enhancement in the ultimate load at (28 and 90) day ages by about (7.82% and 24%), respectively. The maximum ultimate load was increased by about (58.4 and 54.1) % for plates reinforced by micro steel fiber at 28 and 90 days, respectively as compared with reference. The maximum deflection at the center of the Self-compact mortars plates for all tested plates was improved

Use of polypropylene ropes in concrete to minimize steel reinforcement

Progress in construction and buildings industry depends on so many parameters especially materials used. Concrete materials are cheap but still need minimization in their costs. Steel is one of the materials used in concrete structural members to support the concrete as reinforcement. In this study a polypropylene rope (PP ropes) were used to support the task of steel reinforcement especially in tension zones and to decrease the total cost of the concrete section. To know how this matter achieved seven concrete beams were casted one of them was without polypropylene ropes as control beam where as others were reinforced only with polypropylene ropes. The dimensions of the concrete beams were (200cm ×30cm ×20cm). Whole concrete tests were done to find out the most effective properties of concrete like compression, rupture modulus and tensile strength. Seven beams were exposed to monotonic load to find out the load at failure and corresponding deflection at mid span. Results show that if four ropes were used in tension zone of the concrete section the strength increased by about 9%. This ratio seems to be low but the cheap cost of these ropes encourages designers to use more number of ropes in concrete section. This idea needs more future work

Розробка моделі прогнозування міцності на стиск сталефібробетону з використанням алгоритму випадкового лісу в поєднанні з налаштуванням гіперпараметрів і k-кратною перехресною перевіркою

Because of the incorporation of discontinuous fibers, steel fiber-reinforced concrete (SFRC) outperforms regular concrete. However, due to its complexity and limited available data, the development of SFRC strength prediction techniques is still in its infancy when compared to that of standard concrete. In this paper, the compressive strength of steel fiber-reinforced concrete was predicted from different variables using the Random forest model. Case studies of 133 samples were used for this aim. To design and validate the models, we generated training and testing datasets. The proposed models were developed using ten important material parameters for steel fiber-reinforced concrete characterization. To minimize training and testing split bias, the approach used in this study was validated using the 10-fold Cross-Validation procedure. To determine the optimal hyperparameters for the Random Forest algorithm, the Grid Search Cross-Validation approach was utilized. The root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) between measured and estimated values were used to validate and compare the models. The prediction performance with RMSE=5.66, R2=0.88 and MAE=3.80 for the Random forest model. Compared with the traditional linear regression model, the outcomes showed that the Random forest model is able to produce enhanced predictive results of the compressive strength of steel fiber-reinforced concrete. The findings show that hyperparameter tuning with grid search and cross-validation is an efficient way to find the optimal parameters for the RF method. Also, RF produces good results and gives an alternate way for anticipating the compressive strength of SFRCБлагодаря введению прерывистых волокон сталефибробетон (СФБ) превосходит обычный бетон. Однако из-за сложности и ограниченности имеющихся данных разработка методов прогнозирования прочности СФБ все еще находится в зачаточном состоянии по сравнению со стандартным бетоном. В данной работе на основе различных переменных с использованием модели случайного леса выполнено прогнозирование прочности на сжатие сталефибробетона. Для этой цели были использованы тематические исследования 133 образцов. Для разработки и проверки моделей мы создали обучающие и тестовые наборы данных. Предложенные модели были разработаны с использованием десяти важных параметров материала для характеристики сталефибробетона. Чтобы свести к минимуму систематическую ошибку при разделении обучающих и тестовых данных, подход, использованный в исследовании, был проверен с использованием процедуры 10-кратной перекрестной проверки. Для определения оптимальных гиперпараметров для алгоритма случайного леса был использован подход перекрестной проверки с поиском по сетке. Для проверки и сравнения моделей использовались среднеквадратичная ошибка (RMSE), коэффициент детерминации (R2) и средняя абсолютная ошибка (MAE) измеренных и расчетных значений. Еффективность прогнозирования с RMSE=5,66, R2=0,88 и MAE=3,80 для модели случайного леса. По сравнению с традиционной моделью линейной регрессии результаты показали, что модель случайного леса позволяет получить более точные результаты прогнозирования прочности на сжатие сталефибробетона. Результаты показывают, что настройка гиперпараметров с поиском по сетке и перекрестной проверкой является эффективным способом поиска оптимальных параметров для метода СЛ. Кроме того, СЛ дает хорошие результаты и предоставляет альтернативный способ прогнозирования прочности на сжатие СФБЗавдяки введенню переривчастих волокон сталефібробетон (СФБ) перевершує звичайний бетон. Однак через складність і обмеженість наявних даних розробка методів прогнозування міцності СФБ все ще знаходиться в зародковому стані в порівнянні зі стандартним бетоном. У даній роботі на основі різних змінних з використанням моделі випадкового лісу виконано прогнозування міцності на стиск сталефібробетону. Для цієї мети були використані тематичні дослідження 133 зразків. Для розробки та перевірки моделей ми створили навчальні та тестові набори даних. Запропоновані моделі були розроблені з використанням десяти важливих параметрів матеріалу для характеристики сталефібробетону. Щоб звести до мінімуму систематичну помилку під час поділу навчальних та тестових даних, підхід, використаний в дослідженні, був перевірений з використанням процедури 10-кратної перехресної перевірки. Для визначення оптимальних гіперпараметрів для алгоритму випадкового лісу був використаний підхід перехресної перевірки з пошуком по сітці. Для перевірки і порівняння моделей використовувалися середньоквадратична помилка (RMSE), коефіцієнт детермінації (R2) і середня абсолютна помилка (MAE) виміряних і розрахункових значень. Ефективність прогнозування з RMSE=5,66, R2=0,88 і MAE=3,80 для моделі випадкового лісу. У порівнянні з традиційною моделлю лінійної регресії результати показали, що модель випадкового лісу дозволяє отримати більш точні результати прогнозування міцності на стиск сталефібробетону. Результати показують, що налаштування гіперпараметрів з пошуком по сітці і перехресною перевіркою є ефективним способом пошуку оптимальних параметрів для методу ВЛ. Крім того, ВЛ дає хороші результати і надає альтернативний спосіб прогнозування міцності на стиск СФ

Development of Prediction Model to Predict the Compressive Strength of Eco-Friendly Concrete Using Multivariate Polynomial Regression Combined with Stepwise Method

Concrete is the most widely used building material, but it is also a recognized pollutant, causing significant issues for sustainability in terms of resource depletion, energy use, and greenhouse gas emissions. As a result, efforts should be concentrated on reducing concrete’s environmental consequences in order to increase its long-term viability. In order to design environmentally friendly concrete mixtures, this research intended to create a prediction model for the compressive strength of those mixtures. The concrete mixtures that were used in this study to build our proposed prediction model are concrete mixtures that contain both recycled aggregate concrete (RAC) and ground granulated blast-furnace slag (GGBFS). A white-box machine learning model known as multivariate polynomial regression (MPR) was developed to predict the compressive strength of eco-friendly concrete. The model was compared with the other two machine learning models, where one is also a white-box machine learning model, namely linear regression (LR), and the other is the black-box machine learning model, which is a support vector machine (SVM). The newly suggested model shows robust estimation capabilities and outperforms the other two models in terms of R2 (coefficient of determination) and RMSE (root mean absolute error) measurements

FLEXURAL BEHAVIOR OF 3D TEXTILES REINFORCED CEMENTITIOUS COMPOSITES PLATES

This paper presents the flexural behavior of plate specimens (with dimension 500×500×40 mm) containing 3D glass fabric having three different thicknesses 6, 10 and 15mm with different number of layers and orientation. For comparison plates with one and two layers of chicken wires as well as plates with micro steel fiber of 0.75% volume fraction were casted. All plate specimens were cast with cement mortar having 61.2MPa cube compressive strength at 28 days and tested under flexural. From the test, it was observed that the load carrying capacities are higher in the case of plates with 3D glass fabric and showed a gradual increase in toughness beyond the ultimate load as compared with non - fibrous plates. The flexural strength was increased significantly the fiber thickness and number of fibers layers was increased. Based on the results a significant increase was indicated with micro steel fiber

Shear Strength Prediction of Slender Steel Fiber Reinforced Concrete Beams Using a Gradient Boosting Regression Tree Method

For the design or assessment of concrete structures that incorporate steel fiber in their elements, the accurate prediction of the shear strength of steel fiber reinforced concrete (SFRC) beams is critical. Unfortunately, traditional empirical methods are based on a small and limited dataset, and their abilities to accurately estimate the shear strength of SFRC beams are arguable. This drawback can be reduced by developing an accurate machine learning based model. The problem with using a high accuracy machine learning (ML) model is its interpretation since it works as a black-box model that is highly sophisticated for humans to comprehend directly. For this reason, Shapley additive explanations (SHAP), one of the methods used to open a black-box machine learning model, is combined with highly accurate machine learning techniques to build an explainable ML model to predict the shear strength of SFRC slender beams. For this, a database of 330 beams with varying design attributes and geometries was developed. The new gradient boosting regression tree (GBRT) machine learning model was compared statistically to experimental data and current shear design models to evaluate its performance. The proposed GBRT model gives predictions that are very similar to the experimentally observed shear strength and has a better and unbiased predictive performance in comparison to other existing developed models. The SHAP approach shows that the beam width and effective depth are the most important factors, followed by the concrete strength and the longitudinal reinforcement ratio. In addition, the outputs are also affected by the steel fiber factor and the shear-span to effective depth ratio. The fiber tensile strength and the aggregate size have the lowest effect, with only about 1% on average to change the predicted value of the shear strength. By building an accurate ML model and by opening its black-box, future researchers can focus on some attributes rather than others

Shear Strength Prediction of Slender Steel Fiber Reinforced Concrete Beams Using a Gradient Boosting Regression Tree Method

For the design or assessment of concrete structures that incorporate steel fiber in their elements, the accurate prediction of the shear strength of steel fiber reinforced concrete (SFRC) beams is critical. Unfortunately, traditional empirical methods are based on a small and limited dataset, and their abilities to accurately estimate the shear strength of SFRC beams are arguable. This drawback can be reduced by developing an accurate machine learning based model. The problem with using a high accuracy machine learning (ML) model is its interpretation since it works as a black-box model that is highly sophisticated for humans to comprehend directly. For this reason, Shapley additive explanations (SHAP), one of the methods used to open a black-box machine learning model, is combined with highly accurate machine learning techniques to build an explainable ML model to predict the shear strength of SFRC slender beams. For this, a database of 330 beams with varying design attributes and geometries was developed. The new gradient boosting regression tree (GBRT) machine learning model was compared statistically to experimental data and current shear design models to evaluate its performance. The proposed GBRT model gives predictions that are very similar to the experimentally observed shear strength and has a better and unbiased predictive performance in comparison to other existing developed models. The SHAP approach shows that the beam width and effective depth are the most important factors, followed by the concrete strength and the longitudinal reinforcement ratio. In addition, the outputs are also affected by the steel fiber factor and the shear-span to effective depth ratio. The fiber tensile strength and the aggregate size have the lowest effect, with only about 1% on average to change the predicted value of the shear strength. By building an accurate ML model and by opening its black-box, future researchers can focus on some attributes rather than others