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In the context of existing post‐tensioned concrete bridges, a reliable assessment of load carrying capacity is still a challenging issue. The effective concrete compressive strength is related to the degradation process of post‐tensioned cables which cannot be detected by visual inspection only. Currently, in‐situ test methods combined with destructive techniques have been used as common methodology to assess either the concrete compressive strength and post‐tension cables losses. Yet, in‐situ tests are indirect approaches, and can be affected by several uncontrolled factors, playing a misleading role in strength determination. In this perspective, the paper aims to firstly introduce a theoretical background of pull‐out tests with their developments and limitations. Then, configuration test has been discussed to obtain reliable strength relationship, with emphasis on post‐installed inserts for pull‐out test. The main current standard provisions have been compared. This study intends to set the basis to test existing post‐tensioned concrete bridges with modified pull‐out test, having no effects on the structural integrity. [ABSTRACT FROM AUTHOR]

Two test series were examined using nondestructive measuring methods by six independent laboratories before determining their compressive strength. The nondestructive test methods used were the rebound hammer and ultrasonic pulse velocity measurement. Two types of geometries were investigated: drilled cores and cubes. The measurement procedure for each of these datasets is conditioned to the geometry and is therefore different.The first series consists of 20 drilled cores (approximately diameter/height = 10 cm/20 cm) from the 55-year-old Lahntal Viaduct near Limburg, Germany. After preparation in the first laboratory, the lateral surface of the drilled cores was tested with the rebound hammer using a given pattern. Every laboratory tested every drilled core at different locations. Ultrasonic measurements in transmission were performed repeatedly at predefined points on the flat surfaces of the specimen.The second series consisted of 25 newly manufactured concrete cubes of a mix with a target concrete strength class of C30/37. The edge length was 15 cm. Each laboratory received five specimens of this test series. Thus, contrary to the first series, each specimen was tested by only one laboratory. Two side faces of each cube were tested with the rebound hammer. In addition, ultrasonic measurements were performed by one laboratory. The time of flight was measured between the tested side faces of the rebound hammer at different positions.For both series, rebound hammers were used to determine the R-value as well as the Q-value. The rebound hammer models within the laboratories were always the same, while they differed between the laboratories. The ultrasonic measurements took place with different measurement systems and couplants. Finally, both specimen series were tested destructively for compressive strength.The dataset contains the raw data summarized in tabular form. In addition, relevant calculated data are included in some cases. For the ultrasonic measurements, the time of flight has already been converted into the ultrasonic velocity. Besides, in addition to the raw data of the compressive strength test (force, weight, and geometry values), the calculated compressive strengths and densities are also provided.

The strengths and mechanical characteristics of concrete play a key role in the safety levels for the recovery and reuse of existing RC buildings and civil engineering works. This is one of the main focuses of the current research trend. To this aim, the characteristics of concrete must be investigated: the characterization of the concrete and its in situ conditions play a key role. For these reasons, many studies on in situ and laboratory test methods and procedures have been carried out over the last two decades. In the past few years, non-destructive investigation methods have been considered reliable and used in many engineering applications, also for RC constructions. More recent codes and guidelines identify destructive test methods as a reference for practice application. However, non-destructive investigation methods can be used though exclusively in combination with destructive tests to support them. In this study, a significant database is considered to assess the reliability of the relationship between destructive and non-destructive methods for in situ concrete in existing RC constructions. The results of the analyses are used to verify the effectiveness of the methods and prediction models and suggest more effective test procedures. It can be stated that many of the existing empirical methods (based on pre-established correlations) are unable to provide a reliable evaluation of the compressive concrete strength and its variability. In practical applications, non-destructive methods often lead to unsatisfactory results for the existing reinforced concrete constructions. Finally, based on the results, some first operational indications are provided for practical investigations and future possible codes and guideline improvements. [ABSTRACT FROM AUTHOR]

Abstract Aging and structural deterioration under severe environments are major causes of damage in reinforced concrete (RC) structures, such as buildings and bridges. Degradations such as concrete cracks, corrosion of steel, and deformation of structural members can significantly degrade the structural performance and safety. Therefore, effective and easy-to-use methods are desired for repairing and strengthening such concrete structures. Various methods for the strengthening and rehabilitation of RC structures have been developed over the past several decades. Recently, FRP composite materials have emerged as a cost-effective alternative to conventional materials for repairing, strengthening, and retrofitting deteriorating/deficient concrete structures, by externally bonding FRP laminates to concrete structural members. The main purpose of this study is to investigate the effectiveness of the FRP retrofit for circular type concrete columns under the framework of the adaptive neuro-fuzzy inference system (ANFIS). Retrofit ratio, strength of existing concrete, thickness, number of layer, stiffness, ultimate strength of fiber, and size of specimens are used as input parameters to predict strength, strain, and stiffness of the post-yielding modulus. These proposed ANFIS models show reliable increased accuracy in predicting the constitutive properties of concrete retrofitted by FRP, compared to the constitutive models suggested by other researchers.

This study aims at applying a machine learning-based model to establish the relationship between different input variables to the 28-day compressive strength of normal and High-Performance Concrete (HPC). An Artificial Neural Network (ANN) model was trained, validated, and tested using a comprehensive database consisted of 361 records gathered from the previously circulated source. Various models with different learning algorithms and neuron numbers in the hidden layer were examined to attain the best performance model. The examination results revealed that the ANN model using the “trainlm” learning algorithm delivered the best prediction outcomes with the overall coefficient of determination (R 2 ) of 0.9277. The influence of input parameters on the output was also examined by performing the sensitivity analysis. It was observed that the compressive strength of concrete at 28 days was more responsive to the changes in the cement parameter (CM) and the amount of water (WT). In contrast, the 28-day concrete compressive strength was found less sensitive to the variation of the fly ash (FL) parameter. [ABSTRACT FROM AUTHOR]

The recycled aggregate is an alternative with great potential to replace the conventional concrete alongside with other benefits such as minimising the usage of natural resources in exploitation to produce new conventional concrete. Eventually, this will lead to reducing the construction waste, carbon footprints and energy consumption. This paper aims to study the recycled aggregate concrete compressive strength using Artificial Neural Network (ANN) which has been proven to be a powerful tool for use in predicting the mechanical properties of concrete. Three different ANN models where 1 hidden layer with 50 number of neurons, 2 hidden layers with (50 10) number of neurons and 2 hidden layers (modified activation function) with (60 3) number of neurons are constructed with the aid of Levenberg-Marquardt (LM) algorithm, trained and tested using 1030 datasets collected from related literature. The 8 input parameters such as cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age are used in training the ANN models. The number of hidden layers, number of neurons and type of algorithm affect the prediction accuracy. The predicted recycled aggregates compressive strength shows the compositions of the admixtures such as binders, water–cement ratio and blast furnace–fly ash ratio greatly affect the recycled aggregates mechanical properties. The results show that the compressive strength prediction of the recycled aggregate concrete is predictable with a very high accuracy using the proposed ANN-based model. The proposed ANN-based model can be used further for optimising the proportion of waste material and other ingredients for different targets of concrete compressive strength.

The compressive strength of concrete f is one of most important mechanical parameter in the performance assessment of existing reinforced concrete (RC) structures. Many non-destructive testing methods have been developed to estimate the in situ value of f. In particular, the combination of rebound hammer and ultrasonic pulse velocity (UPV) tests, known as SonReb method, is frequently used. In this paper an experimental research has being carried out involving both destructive and non-destructive testing methods for the evaluation of structural performance of existing RC structures. The aim of this study is to verify the reliability of predicting f by some formulas available in literature involving measured parameters such rebound index and UPV. [ABSTRACT FROM AUTHOR]

Aging and structural deterioration under severe environments are major causes of damage in reinforced concrete (RC) structures, such as buildings and bridges. Degradations such as concrete cracks, corrosion of steel, and deformation of structural members can significantly degrade the structural performance and safety. Therefore, effective and easy-to-use methods are desired for repairing and strengthening such concrete structures. Various methods for the strengthening and rehabilitation of RC structures have been developed over the past several decades. Recently, FRP composite materials have emerged as a cost-effective alternative to conventional materials for repairing, strengthening, and retrofitting deteriorating/deficient concrete structures, by externally bonding FRP laminates to concrete structural members. The main purpose of this study is to investigate the effectiveness of the FRP retrofit for circular type concrete columns under the framework of the  adaptive neuro-fuzzy inference system (ANFIS). Retrofit ratio, strength of existing concrete, thickness, number of layer, stiffness, ultimate strength of fiber, and size of specimens are used as input parameters to predict strength, strain, and stiffness of the post-yielding modulus. These proposed ANFIS models show reliable increased accuracy in predicting the constitutive properties of concrete retrofitted by FRP, compared to the constitutive models suggested by other researchers. [ABSTRACT FROM AUTHOR]