3D printed cement-based repairs and strain sensors

This paper presents 3D printed strain sensors based on alkali activated cement repairs, demonstrating a fixed-cost method for remotely deploying a combined monitoring and maintenance technology for construction. Experimental protocols to quantitatively assess the compatibility of cements and 3D printing processes are defined and investigated in this paper. The strain sensing response of printed self-sensing cements is then investigated under compression and tension by monitoring changes in material electrical impedance. Gauge factors for self-sensing repairs printed onto concrete substrates were 8.6 ± 1.6 under compression, with an average adhesion strength of 0.6 MPa between printed repair and concrete substrate. Gauge factors for repairs printed onto glass fibre reinforced polymers were 38.4 ± 21.6 under tension: more variable than for concrete substrates due to incompatibilities between the repair and the polymer substrate. This proof-of-concept is a step towards monitoring and maintenance methods that are more compatible with the time and cost drivers of modern construction

A low-cost electrical impedance analyser for interrogating self-sensing cement repairs

In this paper, we showcase initial results from a bespoke, low-cost interrogator for complex impedance measurements of our robotically deployed self-sensing cement (geopolymer) technology for concrete monitoring and maintenance. Our low-cost (£30, 40USD)interrogation system is benchmarked against the performance of a£12k(16k USD) commercially available lab-spec electrical impedance analyser. Results show the low-cost interrogator is able to match the commercial interrogation system well-enough for the field measurement of impedance, with an impedance root mean square error (RMSE) of ±5.4 % for an ideal cell. For pure geopolymer samples, similar results are found, with an RMSE of ±5.2 %. During patch measurements, although non-linearity was witnessed, the low-cost interrogator showcased the ability to measure the impedance and impedance-frequency variations. Therefore, the first iteration of low-cost interrogator design shows promise for monitoring geopolymer self-sensing repair complex impedances in the field

Concrete fatigue experiment for sensor prototyping and validation of industrial SHM trials

In this paper, preliminary results from a concrete fatigue experiment using a custom built machine are demonstrated. A pre-cracked concrete member is instrumented with bespoke metallic-bonded and epoxy-bonded fiber Bragg grating (FBG) displacement sensors, retrofitted over the crack. Fatigue loading is applied to the beam, with cycle magnitudes replicating results from a previous industrial trial concerning structural health monitoring (SHM) of a wind turbine foundation. Results are compared to an FEM model for verification. The new metallic-bonded crack displacement sensor design is compared in performance with the traditional epoxy-bonded design. Both sensors were sufficiently resilient under dynamic loading to successfully undergo 105 cycle fatigue test. The sensors display a linear relationship with respect to one another; however, from the initial thermal characterization of the devices between 20 and 65 °C, the epoxy-bonded sensor exhibited considerable drift with every subsequent temperature cycle while the metallic-bonded construction was stable within the experimental error. The set up can be used over a long term to validate in situ results from distributed SHM sensors and for initial testing of sensors and data analytics strategies prior to any future field installations

3D printed temperature-sensing repairs for concrete structures

Multifunctional coating materials have enjoyed extensive development within civil engineering in the last few decades, with numerous proposals for self-sensing and self-healing repairs. Less thought has been afforded to coating material deployment, but a reliance on conventional manual methods is leading to high costs and variabilities in performance. This is prohibiting the application of new materials in the field. This paper addresses this issue by outlining, for the first time a 3D printable temperature sensing repair for concrete. The multifunctional material used in this study is a geopolymer: a durable alternative to ordinary Portland cement repairs, which can be electrically interrogated to act as a sensor. In this paper, we outline the material and 3D printing process development, and demonstrate 3D printed repair patches with a temperature sensing precision of 0.1 °C, a long-term sensing repeatability of 0.3 °C, a compressive strength of 24 MPa, and an adhesion strength to concrete of 0.6 MPa. The work demonstrates the feasibility of using additive manufacturing as a new means of applying repairs to concrete substrates, and provides one clear pathway to removing some of the barriers to the field deployment of multifunctional materials in a civil engineering context. The process shown here could enhance the design versatility of self-sensing repairs, unlock remote deployment, and de-cost and de-risk actions that prolong the lifespan and performance of existing concrete structures

Robotic concrete inspection with illumination-enhancement

Existing automated concrete inspection methods are intractable: capturing images under ambient conditions which can vary substantially. Furthermore, an opportunity may have been overlooked: utilizing illumination techniques to enhance defect contrast during imaging which may improve automatic defect detection accuracy. In this work, we present a robotic-mountable lighting apparatus that implements contrast enhancing illumination techniques in an automated package in order to improve crack detection and classification in concrete. Geometrical lighting techniques; directional and angled, were tested on three cracked concrete slab samples. Results from blind/referenceless image spatial quality evaluation (BRISQUE) show that both directional and varied angled lighting influence the quality in different associated regions in an image. Furthermore, the region-based crack detection algorithm Faster R-CNN attained a higher accuracy when images were enhanced with directional lighting during all samples tested. The direction of highest accuracy was not consistent over samples, and is likely dependant on features such as crack location, width, orientation etc. This emphasises the importance of adaptive lighting: illuminating the surface with the most suitable conditions based on an initial observation of the feature or defect. This system represents the initial step in a fully-automated and optimised concrete inspection system capable of defect capture, classification, localization and segmentation

ConcrEITS : an electrical impedance interrogator for concrete damage detection using self-sensing repairs

Concrete infrastructure requires continuous monitoring to ensure any new damage or repair failures are detected promptly. A cost-effective combination of monitoring and maintenance would be highly beneficial in the rehabilitation of existing infrastructure. Alkali-activated materials have been used as concrete repairs and as sensing elements for temperature, moisture, and chlorides. However, damage detection using self-sensing repairs has yet to be demonstrated, and commercial interrogation solutions are expensive. Here, we present the design of a low-cost tomographic impedance interrogator, denoted the "ConcrEITS", capable of crack detection and location in concrete using conductive repair patches. Results show that for pure material blocks ConcrEITS is capable of measuring 4-probe impedance with a root mean square error of ±5.4% when compared to a commercially available device. For tomographic measurements, ConcrEITS is able to detect and locate cracks in patches adhered to small concrete beam samples undergoing 4-point bending. In all six samples tested, crack locations were clearly identified by the contour images gained from tomographic reconstruction. Overall, this system shows promise as a cost-effective combined solution for monitoring and maintenance of concrete infrastructure. We believe further up-scaled testing should follow this research before implementing the technology in a field trial

Threshold-based BRISQUE-assisted deep learning for enhancing crack detection in concrete structures

Automated visual inspection has made significant advancements in the detection of cracks on the surfaces of concrete structures. However, low-quality images significantly affect the classification performance of convolutional neural networks (CNNs). Therefore, it is essential to evaluate the suitability of image datasets used in deep learning models, like Visual Geometry Group 16 (VGG16), for accurate crack detection. This study explores the sensitivity of the BRISQUE method to different types of image degradations, such as Gaussian noise and Gaussian blur. By evaluating the performance of the VGG16 model on these degraded datasets with varying levels of noise and blur, a correlation is established between image degradation and BRISQUE scores. The results demonstrate that images with lower BRISQUE scores achieve higher accuracy, F1 score, and Matthew’s correlation coefficient (MCC) in crack classification. The study proposes the implementation of a BRISQUE score threshold (BT) to optimise training and testing times, leading to reduced computational costs. These findings have significant implications for enhancing accuracy and reliability in automated visual inspection systems for crack detection and structural health monitoring (SHM)

Automated concrete crack inspection with directional lighting platform

This letter presents the development and performance evaluation of a novel platform for visual concrete crack inspection. Concrete surfaces are imaged using directional lighting to support accurate crack detection, classification, and segmentation. In addition to developing lab- and field-deployable hardware iterations, we outline customized convolutional neural networks and filters that leverage the directionally lit dataset. Crack classification and segmentation accuracies were both 10% higher than accuracies for standard imaging techniques with diffuse lighting, and crack widths of 0.1 mm were reliably detected and segmented. The major innovation described here is the combination of new hardware platforms for directional lighting, with a suite of algorithms that utilize the directionally lit dataset to improve crack detection and evaluation. This letter demonstrates that directional lighting can improve the performance and robustness of automated concrete inspection. This could be key in supporting the efforts of asset managers as they seek to automate inspections of their ageing populations of concrete assets

Design and demonstration of a low-cost small-scale fatigue testing machine for multi-purpose testing of materials, sensors and structures

Mechanical fatigue testing of materials, prototype structures or sensors is often required prior to the deployment of these components in industrial applications. Such fatigue tests often requires the continuous long-term use of an appropriate loading machine, which can incur significant costs when outsourcing and can limit customization options. In this work, design and implementation of a low-cost small-scale machine capable of customizable fatigue experimentation on structural beams is presented. The design is thoroughly modeled using FEM software and compared to a sample experiment, demonstrating long-term endurance of the machine. This approach to fatigue testing is then evaluated against the typical cost of outsourcing in the UK, providing evidence that for long-term testing of at least 373 hours, a custom machine is the preferred option

Skeleton-based noise removal algorithm for binary concrete crack image segmentation

Image processing methods for automated concrete crack detection are often challenged by binary noise. Noise removal methods decrease the false positive pixels of crack detection results, often at the cost of a reduction in true positives. This paper proposes a novel method for binary noise removal and segmentation of noisy concrete crack images. The method applies an area threshold before reducing the pixel groups in the image to a skeleton. Each skeleton is connected to its nearest neighbour before the remaining short skeletons in the image are removed using a length threshold. A morphological reconstruction follows to remove all elements in the original noisy image that do not intersect with the skeleton. Finally, pixel groups in close proximity to the endpoints of the pixel groups in the resulting image are reinstated. Testing was conducted on a dataset of noisy binary crack images; the proposed method (Skele-Marker) obtained recall, precision, and F1 score results of 77%, 91%, and 84%, respectively. Skele-marker was compared to other methods found in literature and was found to outperform other methods in terms of precision and F1 score. The proposed method is used to make crack detection results more reliable, supporting the ever-growing demand for automated inspections of concrete structures