E. Júlio

Factors Affecting Bond between New and Old Concrete

This paper describes how the bond strength of concrete-to-concrete interfaces, of reinforced concrete (RC) members with parts cast at different ages, is highly influenced by the curing conditions. Therefore, the monolithic behavior is dependent on these conditions. Current design codes only consider: a) the compressive strength; b) the normal stress at the interface; c) the amount of reinforcement crossing the interface; and d) the roughness of the substrate surface. Due to the fact that the curing conditions of both substrate and added concrete are ignored, the influence of the differential shrinkage is neglected. The influence of the differential stiffness due to the mismatch between the Young’s modulus of both materials is also not considered. This paper presents an experimental study that was conducted to assess the influence of differential shrinkage and stiffness on the bond strength of new-to-old concrete interfaces. Both parameters were shown to have a significant influence on the bond strength and failure mode of concrete-to-concrete interfaces.

REINFORCED CONCRETE JACKETING-INTERFACE INFLUENCE ON MONOTONIC LOADING RESPONSE

This paper reports on experimental and numerical studies conducted to analyze the influence of the interface treatment on the seismic behavior of columns strengthened by reinforced concrete (RC) jacketing to increase their ultimate bending moment. Results show that for undamaged columns with a bending moment/shear force ratio greater than 1.0, it is not necessary to consider any type of interface treatment before casting a RC jacket with a thickness less than 17.5% of the column width to achieve a monolithic behavior of the composite element subjected to cyclic loading. This finding can result in significant money and time savings due to the elimination of unnecessary interface treatment.

Comparison of Methods for Texture Assessment of Concrete Surfaces

This article describes how the bond strength of the interface between concrete layers cast at different times is important to ensure the monolithic behavior of reinforced concrete (RC) composite members. The roughness of the substrate surface has a significant influence in this scope. Current design codes use a qualitative approach based on visual inspection to assess roughness. This procedure is highly dependent on the designer and, therefore, can lead to inaccurate results. Previous studies conducted by the authors proved that it is possible to use a quantitative criterion to classify roughness. This paper describes an experimental study conducted to compare four roughness quantification methods: the processing of the digital image (PDI) and the two-dimensional (2D) laser roughness analyzer (2D-LRA) methods, both developed by the authors; an upgrade of these, using a three-dimensional (3D) laser scanner; and the sand patch test (SPT), a simple and widespread method. The 2D-LRA proved to be the best from the four methods considered because it gives a quantitative assessment of the roughness with adequate accuracy, is nondestructive, easy and fast to use, and is cost effective.

A method to incorporate uncertainty in the classification of remote sensing images

The aim of this paper is to investigate if the incorporation of the uncertainty associated with the classification of surface elements into the classification of landscape units (LUs) increases the results accuracy. To this end, a hybrid classification method is developed, including uncertainty information in the classification of very high spatial resolution multi-spectral satellite images, to obtain a map of LUs. The developed classification methodology includes the following steps: (1) a pixel-based hard classification with a probabilistic Bayesian classifier; (2) computation of the posterior probabilities and quantification of the classification uncertainty using an uncertainty measure; (3) image segmentation and (4) object classification based on decision rules. The classification of the resulting objects into LUs is performed considering a set of decision rules that incorporate the pixel-based classification uncertainty. The proposed methodology was tested on the classification of an IKONOS satellite image. The accuracy of the classification was computed using an error matrix. The comparison between the results obtained with the proposed approach and those obtained without considering the classification uncertainty revealed a 12% increase in the overall accuracy. This shows that the information about uncertainty can be valuable when making decisions and can actually increase the accuracy of the classification results.

Applications of photogrammetry to structural assessment

Photogrammetry is a method developed in the early XIXth century. Nowadays, with digital photography associated to the development of image processing and its automation, this technique has become appealing in different fields of application. In this paper, the authors analysed the current feasibility and advantages of using photogrammetry in structural monitoring. First, the method was calibrated using two laboratorial tests: (1) monitoring of failure and creep tests of long-span reinforced concrete beams; and (2) monitoring of steel beam-to-column connections. Then, the method was tested on site, namely, in monitoring the deformations of a footbridge subjected to different loading situations. It was concluded that photogrammetry can be used in structural monitoring with accuracy and exhibiting additional advantages in relation to traditional methods, namely: (1) an almost unlimited number of measuring points can be considered and automatically processed; and (2) hardware is unnecessary and, therefore, it can be used under almost any situation where traditional methods cannot be employed.

Automatic concrete health monitoring: assessment and monitoring of concrete surfaces

To predict the degradation of concrete structures is extremely challenging. The typical approach combines periodic visual inspections with required non-destructive tests. However, this methodology only discretely evaluates few areas of the structure, being also time consuming and subject to human error. Therefore, a new method designated ‘automatic concrete health monitoring’ is herein presented which aims at automatically characterising and monitoring the state of conservation of concrete surfaces by combining photogrammetry, image processing and multi-spectral analysis. The method was designed to (i) characterise crack pattern, displacement and strain fields; (ii) map damages and (iii) assess and define restoration tasks.

Prediction of Fresh and Hardened State Properties of UHPC: Comparative Study of Statistical Mixture Design and an Artificial Neural Network Model

AbstractThe main objective of the research study described herein is to build two analytical models based on artificial neural networks (ANNs) and the statistical mixture design (SMD) method to predict the required performance of ultra-high-performance concrete (UHPC). Two different curing conditions—heat treatment and water storage—were applied to the specimens. To train the neural network, a total set of 53 different mixtures was designed based on the design matrix of SMD. The statistical analysis results showed the adequacy of both models to predict the required performance of UHPC; however, the ANN model could predict the compressive strength (water storage) and slump flow with higher accuracy than the SMD. The optimum combination of the cement, silica fume, and quartz flour was determined to be 24, 9, and 5% by total volume to achieve a flowable mixture with the highest compressive strength. The accuracy of the model was verified with additional experimental tests.

Effect of Filtering on Texture Assessment of Concrete Surfaces

The bond strength of the interface between concrete layers cast at different times is important to ensure the monolithic behavior of reinforced concrete (RC) composite members. The surface roughness of the concrete substrate has a significant influence on the interface strength. The authors developed two methodologies to assess the texture profile of the substrate surface; have proved that numerical parameters can be used to classify its roughness; and showed that some of these correlate well with the interface strength, both in shear and in tension. Because roughness and waviness parameters are obtained from the primary profile using a filter, the selection of the latter has a significant effect on the results. This paper describes a study performed to analyze this effect and to assess if filtering is a necessary step. It was concluded that filtering can be avoided and the surface texture can be characterized only with primary parameters.

Design of UHPC using artificial neural networks

Ultra-high performance concrete (UHPC) results from the mixture of several constituents giving rise to a highly complex material in hardened state. The higher number of constituents in relation to current concrete, together with a higher number of possible combinations and relative proportioning, makes the behavior of this type of concrete more difficult to predict. Until now, most of the proposed mixture design methods are based on a trial and error procedure, which is expensive and work intensive. Moreover, these methods are not efficient in predicting neither the consistency in fresh state nor the strength in hardened state, and do not consider the effect of curing on the latter. The main objective of the research study herein described is to build an analytical model, based on artificial neural networks (ANN), to predict the required performance of UHPC. Specifically, back-propagation neural networks (BPNN) are adopted to model the relation between the input and the output parameters of UHPC, for two different curing conditions, including heat treatment and water storage. In order to train the neural network, a total set of 53 different mixtures were designed. It is concluded that the developed model can be used as a reliable method to predict the performance of UHPC.

Evaluation of soft possibilistic classifications with non-specificity uncertainty measures

The aim of this paper was to investigate the usefulness of non-specificity uncertainty measures to evaluate soft classifications of remote sensing images. In particular, we analysed whether these measures could be used to identify the difficulties found by the classifier and to estimate the classification accuracy. Two non-specificity uncertainty measures were considered, the non-specificity measure (NSp) and the U-uncertainty measure, and their behaviour was analysed to evaluate which is the most appropriate for this application. To overcome the fact that these two measures have different ranges, a normalized version (Un) of the U-uncertainty measure was used. Both measures were applied to evaluate the uncertainty of a soft classification of a very high spatial resolution multispectral satellite image, performed with an object-oriented image analysis based on a fuzzy classification. The classification accuracy was evaluated using an error matrix and the users and producers accuracies were computed. Two uncertainty indexes are proposed for each measure, and the correlation between the information given by them and the users and producers accuracies was determined to assess the relationship and compatibility of both sources of information. The results show that there is a positive correlation between the information given by the uncertainty and accuracy indexes, but mainly between the uncertainty indexes and the users accuracy, where the correlation achieved 77%. This study shows that uncertainty indexes may be used, along with the possibility distributions, as indicators of the classification performance, and may therefore be very useful tools.