In recent times, there has been a growing need to retrofit and strengthen reinforced concrete (RC) structures that have been damaged. Numerous studies have explored various methods for strengthening RC beams. However, there is a significant dearth of research investigating the utilization of ultra-high-performance concrete (UHPC) for retrofitting damaged RC beams within a concrete frame. This study aims to develop a finite element (FE) model capable of accurately simulating the nonlinear behavior of RC beams and subsequently implementing it in an RC concrete frame. The RC frame is subjected to loading until failure at two distinct degrees, followed by retrofitting and strengthening using Ultra high performance shotcrete (UHPS) through two different methods. The results indicate the successful simulation of the load-displacement curve and crack patterns by the FE model, aligning well with experimental observations. Novel techniques for reinforcing deteriorated concrete frame structures through ABAQUS are introduced. The second strengthening method notably improves both the load-carrying capacity and initial stiffness of the load-displacement curve. By incorporating embedded rebars in the frame's columns, the beam's load-carrying capacity is enhanced by up to 31% compared to cases without embedding. These findings indicate the potential for improving the design of strengthening methods for damaged RC beams and utilizing the FE model to predict the strengthening capacity of UHPS for damaged concrete structures.

The technique of experimentally determining concrete's compressive strength for a given mix design is time-consuming and difficult. The goal of the current work is to propose a best working predictive model based on different machine learning algorithms such as Gradient Boosting Machine (GBM), Stacked Ensemble (SE), Distributed Random Forest (DRF), Extremely Randomized Trees (XRT), Generalized Linear Model (GLM), and Deep Learning (DL) that can forecast the compressive strength of ternary geopolymer concrete mix without carrying out any experimental procedure. A geopolymer mix uses supplementary cementitious materials obtained as industrial by-products instead of cement. The input variables used for assessing the best machine learning algorithm not only include individual ingredient quantities, but molarity of the alkali activator and age of testing as well. Myriad statistical parameters used to measure the effectiveness of the models in forecasting the compressive strength of ternary geopolymer concrete mix, it has been found that GBM performs better than all other algorithms. A sensitivity analysis carried out towards the end of the study suggests that GBM model predicts results close to the experimental conditions with an accuracy between 95.6 % to 98.2 % for testing and training datasets.

This study presents a random-aggregate mesoscale model integrating the random distribution of the coarse aggerates and the damage mechanics of the mortar and interfacial transition zone (ITZ). This mesoscale model can generate the random distribution of the coarse aggregates according to the prescribed particle size distribution which enables the automation of the current methodology with different coarse aggregates' distribution. The main innovation of this work is to propose the "correction factor" to eliminate the dimensionally dependent mesh sensitivity of the concrete damaged plasticity (CDP) model. After implementing the correction factor through the user-defined subroutine in the randomly meshed mesoscale model, the predicted fracture resistance is in good agreement with the average experimental results of a series of geometrically similar single-edge-notched beams (SENB) concrete specimens. The simulated cracking pattern is also more realistic than the conventional concrete material models. The proposed random-aggregate mesoscale model hence demonstrates its validity in the application of concrete fracture failure and statistical size effect analysis.

Fiber-reinforced polymers (FRP) have a proven strength enhancement capability when installed into Reinforced Concrete (RC) beams. The brittle failure of traditional FRP strengthening systems has attracted researchers to develop novel materials with improved strength and ductility properties. One such material is that known as polyethylene terephthalate (PET). This study presents a numerical investigation of the flexural behavior of reinforced concrete beams externally strengthened with PET-FRP systems. This material is distinguished by its large rupture strain, leading to an improvement in the ductility of the strengthened structural members compared to conventional FRPs. A three-dimensional (3-D) finite element (FE) model is developed in this study to predict the load-deflection response of a series of experimentally tested beams published in the literature. The numerical model incorporates constitutive material laws and bond-slip behavior between concrete and the strengthening system. Moreover, the validated model was applied in a parametric study to inspect the effect of concrete compressive strength, PET-FRP sheet length, and reinforcing steel bar diameter on the overall performance of concrete beams externally strengthened with PET-FRP.

Researchers have embarked on an active investigation into the feasibility of adopting alternative materials as a solution to the mounting environmental and economic challenges associated with traditional concrete-based construction materials, such as reinforced concrete. The examination of concrete's mechanical properties using laboratory methods is a complex, time-consuming, and costly endeavor. Consequently, the need for models that can overcome these drawbacks is urgent. Fortunately, the ever-increasing availability of data has paved the way for the utilization of machine learning methods, which can provide powerful, efficient, and cost-effective models. This study aims to explore the potential of twelve machine learning algorithms in predicting the tensile strength of geopolymer concrete (GPC) under various curing conditions. To fulfill this objective, 221 datasets, comprising tensile strength test results of GPC with diverse mix ratios and curing conditions, were employed. Additionally, a number of unseen datasets were used to assess the overall performance of the machine learning models. Through a comprehensive analysis of statistical indices and a comparison of the models' behavior with laboratory tests, it was determined that nearly all the models exhibited satisfactory potential in estimating the tensile strength of GPC. Nevertheless, the artificial neural networks and support vector regression models demonstrated the highest robustness. Both the laboratory tests and machine learning outcomes revealed that GPC composed of 30% fly ash and 70% ground granulated blast slag, mixed with 14 mol of NaOH, and cured in an oven at 300°F for 28 days exhibited superior tensile strength.

Floor inertial forces are transferred to lateral force resisting systems through a diaphragm action during earthquakes. The diaphragm action requires floor slabs to carry in-plane forces. In precast concrete diaphragms, these forces must be carried across the joints between precast floor units as they represent planes of weakness. Therefore, diaphragm reinforcement with sufficient strength and deformability is necessary to ensure the diaphragm action for the floor inertial force transfer. In a shake table test for a three-story precast concrete structure, an unexpected local failure in the diaphragm flexural reinforcement occurred. This failure caused loss of the diaphragm action but did not trigger collapse of the structure due to a possible alternative path for the floor inertial force transfer. This paper investigates this failure event and its impact on structural seismic responses based on the shake table test and simulation results. The simulations were conducted on a structural model with discrete diaphragm elements. The structural model was also validated from the test results. The investigation indicates that additional floor inertial force will be transferred into the gravity columns after loss of the diaphragm action which can further result in the increase of seismic demands in the gravity column and diaphragms in adjacent floors.

Self-Curing Self Compacting Concrete (SCSCC), is a special concrete in contemporary construction practice aimed at enhancing the performance of structural concrete. Its primary function is to ensure a sufficient moisture supply that facilitates hydration along with flow, particularly in the context of high-rise buildings and tall structures. This innovative concrete addresses the challenges of maintaining adequate curing conditions in large-scale projects, maintaining requisite workability, contributing to the overall durability and longevity of concrete structures. For implementing such a versatile material in construction, it is imperative to understand the stress-strain (S-S) behaviour. The primary aim of this study is to develop the S-S curves for TCSCSCC and compare through experimental results. Finite element (FE) analysis based ATENA-GiD was employed for the numerical simulation and develop the analytical stress-strain curves by introducing parameters viz., grade of concrete, tie diameter, tie spacing and yield strength. The stress ratio and the strain ratios are evaluated and compared with experimental values. The mean error is 1.2% with respect to stresses and 2.2% in case of strain. Finally, the stress block parameters for tie confined SCSCC are evaluated and equations are proposed for the same in terms of confinement index.

The purpose of this study is to propose new hysteretic characteristics of medium- to low-rise RC structures controlled by both shear and flexure. Through previous study, the dual lateral force-resisting system composed of shear and flexural failure members has a new failure mechanism that cooperates to enhance the flexural capacity of the flexural failure member even after the failure of the shear member, and the existing theoretical equation significantly underestimates the ultimate strength. In this study, the residual lateral strength mechanism of the dual lateral force-resisting system was analyzed, and, as a result, an equation for estimating the residual flexural strength of each shear-failure member was proposed. The residual flexural strength of each shear-failure member was verified in comparison with the structural testing results obtained in previous study, and the proposed residual flexural strength equation for shear-failure members was tested for reliability using FEA, and its applicable range was also determined. In addition, restoring-force characteristics for evaluating the seismic performance of the dual lateral force-resisting system (nonlinear dynamic analysis), reflecting the proposed residual flexural strength equation, were proposed. Finally, the validity of the restoring-force characteristics of RC buildings equipped with the dual lateral force-resisting system proposed in the present study was verified by performing pseudo-dynamic testing and nonlinear dynamic analysis based on the proposed restoring-force characteristics. Based on this comparative analysis, the applicability of the proposed restoring-force characteristics was verified.