• Computers and Concrete
• /
• v.34 no.4
• /
• pp.393-408
• /
• 2024

In recent decades the strengthening of reinforced concrete (RC) structural elements using Fiber-reinforced polymer (FRP) has received much attention. The behavior of RC elements can vary from axial compression to pure bending, depending on their loading. When the compressive behavior is dominant, the FRP jacket application is common, but when the flexural behavior is prevalent, the codes consider the FRP jacket ineffective. Codes suggest applying FRP bars or strips as Near-surface Mounted (NSM) or Externally Bonded (EB) in the tensile face to strengthen the beams under flexure. To strengthen the columns in tension-control mode, some researchers have suggested NSM FRP bars in both tension and compression faces alone or with the FRP jacket (hybrid). However, the number of tests that evaluate the pure bending of the strengthened columns as one of the pivotal points of the axial force-moment interaction curve is limited. In this paper, 11 RC elements strengthened using the NSM (in both tension and compression faces) or hybrid method were subjected to bending to assess the effect of the amount and material type of the FRP bar and jacket and the dimensions of the groove. The test results revealed that the NSM method increased the flexural capacity of the members between 10% to 50%. Furthermore, using the hybrid method increased the capacity between 51% to 91%. Finally, an analytical model was presented considering the effect of the NSM FRP bond in different circumstances, and its results were in good agreement with the experimental results.

• Computers and Concrete
• /
• v.34 no.4
• /
• pp.409-425
• /
• 2024

This paper utilizes LS-DYNA software to numerically investigate impact response and damage evaluation of fiber-reinforced polymer (FRP) bars-reinforced ultra-high-performance concrete (UHPC) composite beams (FRP-UHPC beams). Three-dimensional finite element (FE) models are established and calibrated by using literature-based static and impact tests, demonstrating high accuracy in simulating FRP-UHPC beams under impact loading. Parametric analyses explore the effects of impact mass, impactor height, FRP bar type and diameter, and clear span length on dynamic response and damage modes. Two failure modes emerge: tensile failure with bottom longitudinal reinforcement fracture and compression failure with local concrete compression near the impact region. Impact mass or height variation under the same impact energy significantly affects the first peak impact force, but minimally influences peak midspan displacement with a difference of no more than 5% and damage patterns. Increasing static flexural load-carrying capacity enhances FRP-UHPC beam impact resistance, reducing displacement deformation by up to 30%. Despite similar static load-carrying capacities, different FRP bars result in varied impact resistance. The paper proposes a damage assessment index based on impact energy, static load-carrying capacity, and clear span length, correlating well with beam end rotation. Their linearly-fitting coefficient was 1.285, 1.512, and 1.709 for the cases with CFRP, GFRP, and BFRP bars, respectively. This index establishes a foundation for an impact-resistant design method, including a simplified formula for peak midspan displacement assessment.

• Computers and Concrete
• /
• v.34 no.4
• /
• pp.447-476
• /
• 2024

The current research proposes an innovative finite element model established within the context of higher-order beam theory to examine the bending and buckling behaviors of functionally graded carbon nanotube-reinforced composite (FG-CNTRC) beams resting on Winkler-Pasternak elastic foundations. This two-node beam element includes four degrees of freedom per node and achieves inter-element continuity with both C¹ and C⁰ continuities for kinematic variables. The isoparametric coordinate system is implemented to generate the elementary stiffness and geometric matrices as a way to enhance the existing model formulation. The weak variational equilibrium equations are derived from the principle of virtual work. The mechanical properties of FG-CNTRC beams are considered to vary gradually and smoothly over the beam thickness. The current investigation highlights the influence of porosity dispersions through the beam cross-section, which is frequently omitted in previous studies. For this reason, this analysis offers an enhanced comprehension of the mechanical behavior of FG-CNTRC beams under various boundary conditions. Through the comparison of the current results with those published previously, the proposed finite element model demonstrates a high rate of efficiency and accuracy. The estimated results not only refine the precision in the mechanical analysis of FG-CNTRC beams but also offer a comprehensive conceptual model for analyzing the performance of porous composite structures. Moreover, the current results are crucial in various sectors that depend on structural integrity in specific environments.

• Computers and Concrete
• /
• v.34 no.2
• /
• pp.151-168
• /
• 2024

This research aimed to appraise the effectiveness of four optimization approaches - cuckoo optimization algorithm (COA), multi-verse optimization (MVO), particle swarm optimization (PSO), and teaching-learning-based optimization (TLBO) - that were enhanced with an artificial neural network (ANN) in predicting the bearing capacity of shallow foundations located on cohesionless soils. The study utilized a database of 97 laboratory experiments, with 68 experiments for training data sets and 29 for testing data sets. The ANN algorithms were optimized by adjusting various variables, such as population size and number of neurons in each hidden layer, through trial-and-error techniques. Input parameters used for analysis included width, depth, geometry, unit weight, and angle of shearing resistance. After performing sensitivity analysis, it was determined that the optimized architecture for the ANN structure was 5×5×1. The study found that all four models demonstrated exceptional prediction performance: COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP. It is worth noting that the MVO-MLP model exhibited superior accuracy in generating network outputs for predicting measured values compared to the other models. The training data sets showed R² and RMSE values of (0.07184 and 0.9819), (0.04536 and 0.9928), (0.09194 and 0.9702), and (0.04714 and 0.9923) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively. Similarly, the testing data sets produced R² and RMSE values of (0.08126 and 0.07218), (0.07218 and 0.9814), (0.10827 and 0.95764), and (0.09886 and 0.96481) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively.

• Computers and Concrete
• /
• v.34 no.2
• /
• pp.179-193
• /
• 2024

The bending and buckling effect for carbon nanotube-reinforced composite (CNTRC) beams can be evaluated by developing the theory of third shear deformation (TSDT). This study examines beams supported by viscoelastic foundations, where single-walled carbon nanotubes (SWCNTs) are dispersed and oriented within a polymer matrix. Four patterns of reinforcement are used for the CNTRC beams. The rule of mixtures is assessed for the material properties of CNTRC beams. The effective functionally graded materials (FGM) properties are studied by considering three different uneven distribution types of porosity. The damping coefficient is considered to investigate the viscosity effect on the foundation in addition to Winkler's and Pasternak's parameters. The accuracy of the current theory is inspected with multiple comparison works. Moreover, the effects of different beam parameters on the CNTRC beam bending and buckling over a viscoelastic foundation are discussed. The results demonstrated that the O-beam is the weakest type of CNTRC beam to resist buckling and flexure loads, whereas the X-beam is the strongest. Moreover, it is indicated that the presence of porosity in the beams decreases the stiffness and increases deflection. In comparison, the deflection was reduced in the presence of a viscoelastic foundation.

• Computers and Concrete
• /
• v.34 no.2
• /
• pp.247-265
• /
• 2024

Measuring the fracture toughness of concrete in laboratory settings is challenging due to various factors, such as complex sample preparation procedures, the requirement for precise instruments, potential sample failure, and the brittleness of the samples. Therefore, there is an urgent need to develop innovative and more effective tools to overcome these limitations. Supervised learning methods offer promising solutions. This study introduces seven machine learning algorithms for predicting concrete's effective fracture toughness (K-eff). The models were trained using 560 datasets obtained from the central straight notched Brazilian disc (CSNBD) test. The concrete samples used in the experiments contained micro silica and powdered stone, which are commonly used additives in the construction industry. The study considered six input parameters that affect concrete's K-eff, including concrete type, sample diameter, sample thickness, crack length, force, and angle of initial crack. All the algorithms demonstrated high accuracy on both the training and testing datasets, with R² values ranging from 0.9456 to 0.9999 and root mean squared error (RMSE) values ranging from 0.000004 to 0.009287. After evaluating their performance, the gated recurrent unit (GRU) algorithm showed the highest predictive accuracy. The ranking of the applied models, from highest to lowest performance in predicting the K-eff of concrete, was as follows: GRU, LSTM, RNN, SFL, ELM, LSSVM, and GEP. In conclusion, it is recommended to use supervised learning models, specifically GRU, for precise estimation of concrete's K-eff. This approach allows engineers to save significant time and costs associated with the CSNBD test. This research contributes to the field by introducing a reliable tool for accurately predicting the K-eff of concrete, enabling efficient decision-making in various engineering applications.

• Computers and Concrete
• /
• v.34 no.2
• /
• pp.213-230
• /
• 2024

The use of waste tires and industrial wastes such as fly ash (FA) and ground granulated blast furnace slag (GGBS) in concrete is an important issue in terms of sustainability. In this study, the effect of parameters affecting the physical, mechanical and microstructural properties of FA/GGBS-based geopolymer concretes with waste rubber fiber was investigated. For this purpose, the effects of rubber fiber percentage (0.6%, 0.9%, 1.2%), binder (75FA25GGBS, 50FA50GGBS, 25FA75GGBS) and curing temperature (75 ℃, 90 ℃ and 105 ℃) were investigated. The Taguchi-Grey Relational Analysis (TGRA) method was used to obtain optimum parameter levels of rubber fiber geopolymer concrete (RFGC). The slump, fresh and hardened density, compressive strength, flexural strength, static and dynamic modulus of elasticity, ultrasonic pulse velocity (UPV) tests and scanning electron microscopy (SEM) analysis were performed on the produced concretes. The analysis of variance (ANOVA) method was used to statistically determine the effects of the parameters on the experimental results. A confirmation test was performed to test the accuracy of the optimum values found by the TGRA method. With the increase of GGBS percentage, the compressive strength of RFGC increased up to 196%. The increase in rubber fiber percentage and curing temperature adversely affected the mechanical properties of RFGC. As a result of TGRA, the optimum value was found to be A1B3C1. ANOVA results showed that the most effective parameter on the experimental results was the binder with 99% contribution percentage. It is understood from the SEM images that the optimum concrete had a denser microstructure and less capillary cracks and voids. For this study, the use of the TGRA method in multiple optimization has proven to provide very useful and reliable results. In cases where many factors are effective on its strength and durability, such as geopolymer concrete, using the TGRA method allows for finding the optimum value of the parameters by saving both time and cost.

• Computers and Concrete
• /
• v.34 no.3
• /
• pp.307-328
• /
• 2024

The concrete damage plasticity (CDP) model is widely used to simulate concrete behaviour using either implicit or explicit analysis methods. To effectively execute the models and resolve convergence issues in implicit analysis, activating the viscosity parameter of this material model is a common practice. Despite the frequent application of implicit analysis to analyse concrete structures with the CDP model, the viscosity parameter significantly varies among available models and lacks consistency. The adjustment of the viscosity parameter at the element/structural level disregards its indirect impact on the material. Therefore, the accuracy of the numerical model is confined to the validated range and might not hold true for other values, often explored in parametric studies subsequent to validations. To address these challenges and develop a unified numerical model for varied conditions, a quasi-static analysis using the explicit solver was conducted in this study. Fifteen thick flat plates tested under load control with different geometries and different eccentric loads were considered to verify the accuracy of the model. The study first investigated various concrete material behaviours under compression and tension as well as the concrete tensile strength to identify the most reliable models from previous methodologies. The study compared the results using both implicit and explicit analysis. It was found that, in implicit analysis, the viscosity parameter should be as low as 0.0001 to avoid affecting material properties. However, at the structural level, the optimum value may need adjustment between 0.00001 to 0.0001 with changing geometries and loading type. This observation raises concerns about further parametric study if the specific value of the viscosity parameter is used. Additionally, activating the viscosity parameter in load control simulations confirmed its inability to capture the peak load. Conversely, the unified explicit model accurately simulated the behaviour of the test specimens under varying geometries, load eccentricities, and column sizes. This study recommends restricting implicit solutions to the viscosity values proposed in this research. Alternatively, for highly nonlinear problems under load control simulation, explicit analysis stands as an effective approach, ensuring unified parameters across a wide range of applications without convergence problems.

• Computers and Concrete
• /
• v.34 no.3
• /
• pp.279-296
• /
• 2024

In this study, ensemble machine learning (ML) models are employed to estimate the hardened properties of Self-Compacting Geopolymer Concrete (SCGC). The input variables affecting model development include the content of the SCGC such as the binder material, the age of the specimen, and the ratio of alkaline solution. On the other hand, the output parameters examined includes compressive strength, flexural strength, and split tensile strength. The ensemble machine learning models are trained and validated using a database comprising 396 records compiled from 132 unique mix trials performed in the laboratory. Diverse machine learning techniques, notably K-nearest neighbours (KNN), Random Forest, and Extreme Gradient Boosting (XGBoost), have been employed to construct the models coupled with Bayesian optimisation (BO) for the purpose of hyperparameter tuning. Furthermore, the application of nested cross-validation has been employed in order to mitigate the risk of overfitting. The findings of this study reveal that the BO-XGBoost hybrid model confirms better predictive accuracy in comparison to other models. The R² values for compressive strength, flexural strength, and split tensile strength are 0.9974, 0.9978, and 0.9937, respectively. Additionally, the BO-XGBoost hybrid model exhibits the lowest RMSE values of 0.8712, 0.0773, and 0.0799 for compressive strength, flexural strength, and split tensile strength, respectively. Furthermore, a SHAP dependency analysis was conducted to ascertain the significance of each parameter. It is observed from this study that GGBS, Flyash, and the age of specimens exhibit a substantial level of influence when predicting the strengths of geopolymers.

• Korean Journal of Environmental Agriculture
• /
• v.33 no.4
• /
• pp.388-394
• /
• 2014

BACKGROUND: Aclonifen is used as a systemic and selective herbicide to control a wide spectrum broad-leaf weeds by inhibition carotenoid biosynthesis, and then its MRLs(Maximum Residue Limits) will be determined in onion and garlic. In this study, a new official method was developed for aclonifen determination in agricultural products to routinely inspect the violation of MRL as well as to evaluate the terminal residue level. METHODS AND RESULTS: Aclonifen was extracted from crop samples with acetone and the extract was partitioned with dichloromethane and then purified by silica solid phase extraction(SPE) cartridge. The purified samples were detected GC using an ECD detector. Limits of detection(LOD) was 0.001 mg/kg and quantification(LOQ) was 0.005 mg/kg, respectively. For validation purposes, recovery studies were carried out at three different concentration levels (LOQ, $10{\times}LOQ$, $50{\times}LOQ$, n=5). The recoveries were ranged from 74.3 to 95.0% with relative standard deviations(RSDs) of less than 8%. All values were consistent with the criteria ranges requested in the Codex guidelines(CAC/GL 40). CONCLUSION: The proposed analytical method was accurate, effective and sensitive for aclonifen determination and it will be used to as an official method in Korea.