Photometric stereo data for the validation of a structural health monitoring test rig

Photometric stereo uses images of objects illuminated from various directions to calculate surface normals which can be used to generate 3D meshes of the object. Such meshes can be used by engineers to estimate damage of a concrete surface, or track damage progression over time to inform maintenance decisions. This dataset [1] was collected to quantify the uncertainty in a photometric stereo test rig through both the comparison with a well characterised method (coordinate measurement machine) and experiment virtualisation. Data was collected for 9 real objects using both the test rig and the coordinate measurement machine. These objects range from clay statues to damaged concrete slabs. Furthermore, synthetic data for 12 objects was created via virtual renders generated using Blender (3D software) [2]. The two methods of data generation allowed the decoupling of the physical rig (used to light and photograph objects) and the photometric stereo algorithm (used to convert images and lighting information into 3D meshes). This data can allow users to: test their own photometric stereo algorithms, with specialised data created for structural health monitoring applications; provide an industrially relevant case study to develop and test uncertainty quantification methods on test rigs for structural health monitoring of concrete; or develop data processing methodologies for the alignment of scaled, translated, and rotated data.

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).

Self-Sensing Rubber for Bridge Bearing Monitoring

Elastomeric bearings are widely used in bridges to support the superstructure, to transfer loads to substructures, and to accommodate movements induced by, for example, temperature changes. Bearing mechanical properties affect the bridge's performance and its response to permanent and variable loadings (e.g., traffic). This paper describes the research carried out at Strathclyde towards the development of smart elastomeric bearings that can be used as a low-cost sensing technology for bridge and/or weigh-in-motion monitoring. An experimental campaign was performed, under laboratory conditions, on various natural rubber (NR) specimens enhanced with different conductive fillers. Each specimen was characterized under loading conditions that replicated in-situ bearings to determine their mechanical and piezoresistive properties. Relatively simple models can be used to describe the relationship between rubber bearing resistivity and deformation changes. Gauge factors (GFs) in the range between 2 and 11 are obtained, depending on the compound and the applied loading. Experiments were also carried out to show that the developed model can be used to predict the state of deformation of the bearings under random loadings of different amplitudes that are characteristic of the passage of traffic over a bridge.

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.

Ambient Cured Fly Ash Geopolymer Coatings for Concrete

The reinforced concrete structures that support transport, energy and urban networks in developed countries are over half a century old, and are facing widespread deterioration. Geopolymers are an affordable class of materials that have promising applications in concrete structure coating, rehabilitation and sensing, due to their high chloride, sulphate, fire and freeze-thaw resistances and electrolytic conductivity. Work to date has, however, mainly focused on geopolymers that require curing at elevated temperatures, and this limits their ease of use in the field, particularly in cooler climates. Here, we outline a design process for fabricating ambient-cured fly ash geopolymer coatings for concrete substrates. Our technique is distinct from previous work as it requires no additional manufacturing steps or additives, both of which can bear significant costs. Our coatings were tested at varying humidities, and the impacts of mixing and application methods on coating integrity were compared using a combination of calorimetry, x-ray diffraction and image-processing techniques. This work could allow geopolymer coatings to become a more ubiquitous technique for updating ageing concrete infrastructure so that it can meet modern expectations of safety, and shifting requirements due to climate change.