Macro-strain based deflection and neutral axis position monitoring of reinforced concrete beams during corrosion

This article proposes a new technique of monitoring neutral axis positions and deflection of Reinforced Concrete (RC) beam during corrosion of steel reinforcement using macro-strain measurements of distributed long-gauge sensors. A different group of distributed long-gauge Packaged Carbon Fiber Line (PCFL) sensors with self-compensation and effective packaging system is installed on the compression and tension fibers of the concrete surface and steel reinforcements of RC beam to verify the proposed method experimentally. An accelerated corrosion method utilizing a salt solution and the constant current was used to achieve the required corrosion levels. The estimated deflection measured by the developed method is compared with the results using Linear Variable Displacement Transducer (LVDTs). It has been demonstrated that long-gauge PCFL sensors could provide the same accuracy. The distributed measured strains were utilized to evaluate the deterioration of the structure’s health with the advance of corrosion. Based on corrosion monitoring experimental results, it can be confirmed that using distributed PCFL sensors mounted on steel reinforcements or concrete surface, the locations and progress of the damage with corrosion time can be detected effectively. The maximum error in the estimated deflection from PCFL sensors mounted on the concrete surface compared to the LVDTs before the onset and after 24 h of accelerated corrosion was 0.5% and 2.5%, respectively.

Quantification of Chlorides and Sulphates on Concrete Surfaces Using Portable X-ray Fluorescence. Optimization of the Measurement Method Using Monte Carlo Simulation

A correct assessment of the pathologies that can affect a reinforced concrete structure is required in order to define the repair procedure. This work addresses the challenge of quantifying chlorides and sulphates directly on the surface of concrete. The quantification was carried out by means of X-ray fluorescence analysis on the surface of concrete specimens at different points with portable equipment. Concrete prisms were made with different amounts of NaCl and Na2SO4. To avoid the influence of coarse aggregate, a qualitative estimate of the amount of coarse aggregate analyzed has been made, although the results show that there is no significant influence. Monte Carlo simulations were carried out in order to establish the necessary number of random analyses of the mean value to be within an acceptable range of error. In the case of quantifying sulphates, it is necessary to carry out six random analyses on the surface, and eight measurements in the case of quantifying chlorides; in this way, it is ensured that errors are below 10% in 95% of the cases. The results of the study highlight that a portable XRF device can be used in situ to obtain concentrations of chlorides and sulphates of a concrete surface with good accuracy. There is no need to take samples and bring them to a laboratory, allowing lower overall costs in inspection and reparation works.

Characterising Penetrometer Tip Contact during Concrete Condition Assessment

Concrete condition assessing penetrometers need to be able to distinguish between making contact with a hard (concrete) surface as opposed to a semi-solid (corroded concrete) surface. If a hard surface is mistaken for a soft surface, concrete corrosion may be over-estimated, with the potential for triggering unnecessary remediation works. Unfortunately, the variably-angled surface of a concrete pipe can cause the tip of a force-sensing tactile penetrometer to slip and thus to make this mistake. We investigated whether different shaped tips of a cylindrical penetrometer were better than others at maintaining contact with concrete and not slipping. We designed a range of simple symmetric tip shapes, controlled by a single superellipse parameter. We performed a finite element analysis of these parametric models in SolidWorks before machining in stainless steel. We tested our penetrometer tips on a concrete paver cut to four angles at 20∘ increments. The results indicate that penetrometers with a squircle-shaped steel tip (a=b=1,n=4) have the least slip, in the context of concrete condition assessment.

Concrete Spalling Severity Classification Using Image Texture Analysis and a Novel Jellyfish Search Optimized Machine Learning Approach

During the phase of building survey, spalling and its severity should be detected as earlier as possible to provide timely information on structural heath to building maintenance agency. Correct detection of spall severity can significantly help decision makers develop effective maintenance schedule and prioritize their financial resources better. This study aims at developing a computer vision-based method for automatic classification of concrete spalling severity. Based on input image of concrete surface, the method is capable of distinguishing between a minor spalling in which the depth of the broken-off material is less than the concrete cover layer and a deep spalling in which the reinforcing steel bars have been revealed. To characterize concrete surface condition, image texture descriptors of statistical measurement of color channels, gray-level run length, and center-symmetric local binary pattern are used. Based on these texture-based features, the support vector machine classifier optimized by the jellyfish search metaheuristic is put forward to construct a decision boundary that partitions the input data into two classes of shallow spalling and deep spalling. A dataset consisting of 300 image samples has been collected to train and verify the proposed computer vision method. Experimental results supported by the Wilcoxon signed-rank test point out that the newly developed method is highly suitable for concrete spall severity classification with accuracy rate = 93.33%, F1 score = 0.93, and area under the receiver operating characteristic curve = 0.97.

Review From Multiple Perspectives for the State of the Practice on the Use of Recycled Asphalt Materials and Recycling Agents in Asphalt Concrete Surface Mixtures

The objective of this paper is to provide information from multiple perspectives on the current state of the practice with regard to using recycled materials and recycling agents (RAs) in asphalt concrete mixtures. This information was collected through a survey of U.S. transportation agencies and RA suppliers combined with a search of RA-related specifications and pilot projects previously constructed. Moreover, a case study describing the Virginia Department of Transportation’s experience with RAs provides a tangible example of how at least one agency is approaching the potential implementation of these technologies. This practice review was achieved by documenting the experience, lessons learned, and best practices of multiple asphalt experienced contractors and asphalt binder suppliers in the Virginia area. This paper follows a similar survey conducted in 2014 as part of NCHRP 09-58 and provides a second look at the use of RAs across North America. Not all state departments of transportation have experience with using RAs. Factors preventing the use of RAs included specification limitations, lack of expertise in processing recycled materials, supporting data, and negative prior experiences. Developing a performance-based testing framework is mandatory for the successful use of RAs. In general, good and frequent communication with the RA supplier is critical and necessary during the planning stages, the production of mixtures, and the continuous quality control by the supplier to resolve issues when they arise. Finally, a strong quality control and quality assurance-testing program should be implemented to ensure that materials meet the properties needed to produce a good-performing mixture.

Lightweight Neural Network for Real-Time Crack Detection on Concrete Surface in Fog

Cracks are one of the most common factors that affect the quality of concrete surfaces, so it is necessary to detect concrete surface cracks. However, the current method of manual crack detection is labor-intensive and time-consuming. This study implements a novel lightweight neural network based on the YOLOv4 algorithm to detect cracks on a concrete surface in fog. Using the computer vision algorithm and the GhostNet Module concept for reference, the backbone network architecture of YOLOv4 is improved. The feature redundancy between networks is reduced and the entire network is compressed. The multi-scale fusion method is adopted to effectively detect cracks on concrete surfaces. In addition, the detection of concrete surface cracks is seriously affected by the frequent occurrence of fog. In view of a series of degradation phenomena in image acquisition in fog and the low accuracy of crack detection, the network model is integrated with the dark channel prior concept and the Inception module. The image crack features are extracted at multiple scales, and BReLU bilateral constraints are adopted to maintain local linearity. The improved model for crack detection in fog achieved an mAP of 96.50% with 132 M and 2.24 GMacs. The experimental results show that the detection performance of the proposed model has been improved in both subjective vision and objective evaluation metrics. This performs better in terms of detecting concrete surface cracks in fog.

Inclined porous concrete surface impact on infiltration using recycled concrete aggregate

Predicting the infiltration rate on inclined surfaces is a pending case, especially when compared to rain intensity. The inclined surface has less ability to generate ponding, leading to higher runoff and higher erosion rates. In the rainy season, on the highway with a very steep slope, erosion usually occurs and becomes very dangerous. By using porous concrete, it is expected to receive higher infiltration and less runoff. This study aimed to determine the impact of the inclined surface of porous concrete on infiltration capacity. The research was conducted using both natural coarse aggregate and recycled coarse aggregate made from concrete waste. The infiltration and permeability test were conducted using porous concrete slabs under 0 to 30% inclined surface. It was shown that the infiltration rate is getting lower as the surface is being steeper. It was also shown that porous concrete made from recycled coarse aggregate has higher performance on permeability and infiltration rate compared to porous concrete made from the natural one.

Effect of Different Lateral Reinforcement and its Spacing on Column Reinforced with Hollow Composite Sections

Hollow Concrete Columns (HCCs) are one of the preferred construction systems in civil infrastructures including bridge piers, ground piles, and utility poles to minimize the overall weight and costs. HCCs are also considered a solution to increase the strength to mass ratio of structures. However, HCCs are subjected to brittle failure behaviour by concrete crushing means that the displacement capacity and the strength after steel yielding in HCCs are decreasing due to the unconfined concrete core. Absence of the concrete core changes the inner stress formation in HCCs from triaxial to biaxial causes lower strength. A new type of Hollow Composite Reinforcing System (HCRS) has recently been designed and developed to create voids in structural members. This reinforcing system has four external flanges to facilitate mechanical bonding and interaction with concrete. Therefore, providing the inner Hollow Composite Reinforced Sections (HCRS) can significantly increase strength by providing a higher reinforcement ratio and confining the inner concrete core triaxially. The corrosion of steel is also a notable factor in the case of steel reinforced HCCs which became more critical because their outer and inner surfaces exposing more concrete surface area. An alternative reinforcement is Glass Fibre Reinforced Polymer (GFRP) bars, can overcome the brittle behaviour of steel reinforced HCC. In previous studies, HCC shows high strength capacity, when appropriate reinforcement in the form of longitudinal GFRP bars, laterally using GFRP spirals and internally using rectangular HCRS which provide enough inner confinement. However, the spirals laterally restrict the expansion of the concrete core and limit the buckling of the longitudinal bars, allowing the columns to keep resisting applied loads and gives maximum strength. Therefore, in this study, the spirals are replaced by discrete hoops as lateral reinforcement to analyse the effect on structural behaviour of HCC reinforced with rectangle shaped HCRS under axial load using ANSYS software. The results show that column laterally reinforced with spiral attained insignificant increase in strength than their counterpart specimens confined with hoops. So, the circular hoops were found to be as efficient in confining concrete as spirals in a column reinforced internally with rectangle shaped HCRS. The increase in volumetric ratio can be achieved by reducing the spacing between lateral reinforcement. So, this study also investigates the effectiveness of reducing the spiral spacing in HCC reinforced with HCRS, three models with lateral spacing of 50mm, 40mm and 30mm are modelled and analysed. The results show that columns with closer spiral spacing attained more axial stability. Keywords: Hollow Concrete Column, Rectangular Hollow Composite Reinforced Sections, GFRP Spirals, GFRP Hoops, Nonlinear Static Analysis, ANSYS.