Ionic thermoelectric materials integrated into the concrete members of buildings have potential to generate electric energy utilizing thermal gradients. This paper investigates the effects of nickel foam (NF)-carbon nanotube (CNT) electrodes and the polyvinyl alcohol (PVA) hydrogel on the thermoelectric performance of hardened cement and evaluates the use of PVA as a charge carrier between the CNT electrodes and cement. The porosity of the cement samples was studied using micro-computed tomography. The results indicate that modifying the cement with PVA hydrogel increases capillary porosity while it reduces the Seebeck coefficient, overall strength, and capacitance. The increase of porosity observed to decrease the thermoelectric power. Pure cement samples without any modifications produced significant values up to 28 mV/K for the Seebeck coefficient, which can be explained by the diffusion of water inside the CNT nanochannels and its flow in cement microcracks and capillaries. These values pave the way for energy-efficient housing with increased grid independence.

External lighting is essential for autonomous inspections of concrete structures in low-light environments. However, previous studies have primarily relied on uniformly diffused lighting to illuminate images and faced challenges in detecting complex crack patterns. This paper proposes two novel algorithms that use directional lighting to classify concrete defects. The first method, named fused neural network, uses the maximum intensity pixel-level image fusion technique and selects the maximum intensity pixel values from all directional images for each pixel to generate a fused image. The second proposed method, named multi-channel neural network, generates a five-channel image, with each channel representing the grayscale version of images captured in the Right (R), Down (D), Left (L), Up (U), and Diffused (A) directions, respectively. The proposed multi-channel neural network model achieved the best performance, with accuracy, precision, recall, and F1 score of 96.6%, 96.3%, 97%, and 96.6%, respectively. It also outperformed the FusedNet and other models found in the literature, with no significant change in evaluation time. The results from this work have the potential to improve concrete crack classification in environments where external illumination is required. Future research focuses on extending the concepts of multi-channel and image fusion to white-box techniques.

Visual inspections of concrete infrastructure in low-light environments require external lighting to ensure adequate visibility. Directional lighting sources, where an image scene is illuminated with an angled lighting source from one direction, can enhance the visibility of surface defects in an image. This paper compares directional and diffused scene illumination images for pixel-level concrete crack segmentation. A novel directional lighting image segmentation algorithm is proposed, which applies crack segmentation image processing techniques to each directionally lit image before combining all images into a single output, highlighting the extremities of the defect. This method was benchmarked against two diffused lighting crack detection techniques across a dataset with crack widths typically ranging from 0.07 mm to 0.4 mm. When tested on cracked and uncracked data, the directional lighting method significantly outperformed other benchmarked diffused lighting methods, attaining a 10% higher true-positive rate (TPR), 12% higher intersection over union (IoU), and 10% higher F1 score with minimal impact on precision. Further testing on only cracked data revealed that directional lighting was superior across all crack widths in the dataset. This research shows that directional lighting can enhance pixel-level crack segmentation in infrastructure requiring external illumination, such as low-light indoor spaces (e.g., tunnels and containment structures) or night-time outdoor inspections (e.g., pavement and bridges).

Visual identification of reinforced concrete structure defects is vital to ensuring structural longevity and resilience. This paper explores the use of selective reflection imaging for concrete surface corrosion and exposed reinforcement detection and measurement. Selective reflection occurs when certain image colours are reflected more strongly than others by a surface. This effect can enhance colour contrast, making certain objects in a captured image appear darker. By imaging concrete surfaces using a blue lighting source, the contrast of red-coloured corrosion and exposed reinforcement is enhanced, particularly in greyscale images used by many defect detection algorithms. An inspection algorithm is proposed to segment these features using blue light images and Otsu thresholding, enabling accurate measurement of corrosion surface area. On laboratory slab samples, the algorithm obtained Intersection over Union (IoU) scores of over 84 % when compared to human-defined ground truths. Additionally, the algorithm’s corrosion surface area measurements produced a maximum relative error of negative 8 %. These findings demonstrate how colour-controlled illumination can enhance visual inspections, leading to more efficient monitoring and improved durability of reinforced concrete structures.

This article demonstrates the robotic deployment of sensor nodes into precast concrete tunnel segments during manufacturing. Magnetic embeddable sensor nodes based on vibrating wire strain gauges (VWSGs) were deployed on a steel precast segment mold using a six-axis collaborative robot at the lab scale. Robotic sensor deployment proved to be significantly more accurate and consistent than manual sensor deployment methods. On average, positional and angular errors in sensor placement were reduced by 85% when using robotic deployment. The strain transfer coefficients for robotically embedded sensors were evaluated using mechanical bending tests and a finite element model (FEM). Strain transfers across a population of ten segments were found to be 0.93 +/- 0.012 in the longitudinal direction, and 0.567 +/- 0.011 in the transversal direction. The repeatability of strain measurements within these segments was also confirmed, with low coefficient of variation values of 1% for longitudinal strains and 1.9% for transversal strains. The work presented in this article underscores the measurement performance enhancements that can result from using robotics for sensor deployment in precast manufacturing environments. This could translate to a lower uncertainty and risk for civil asset managers and structural health monitoring (SHM) practitioners.

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.

Current concrete spalling detection and measurement methods are sparse; despite recent research and commercial offerings using laser scanners, manual measurement is still the industry standard. This paper presents a spalling 3D reconstruction and measurement method. The method uses images illuminated with angled and directional lighting and three neural networks for photometric stereo 3D mesh generation and spalling volume measurement. The proposed method was benchmarked on a laboratory dataset of spalled concrete slabs against a high-resolution laser scanner, yielding an average height error of 0.0 mm and a standard deviation of 1.3 mm. Volume comparisons showed that with manual input, the method achieved a mean absolute percentage error of 22%. Finally, the proposed technique was compared to manual measurements and benchmarked on a spalled concrete structure against a Trimble X12 laser scanner. This research can provide inspectors with increased data interpretability and reduced imaging time for concrete defect mapping.

Previous research has demonstrated how angled and directional lighting can enhance the detection of concrete cracks in low-light environments and outperform diffused lighting alternatives. This paper investigates the effect of different angles of incidence of directional lighting for concrete crack pixel-level segmentation. Five directional lighting datasets of cracked concrete slabs were captured, each using an angle of incidence of 10, 20, 30, 40, and 50 degrees, respectively. A directional lighting crack segmentation algorithm was applied to each lighting angle dataset. Algorithm output comparisons with ground truths revealed that the directional lighting method performed best on the 50-degree lighting dataset, obtaining a recall, precision, and F1 score of 68%, 81%, and 74%, respectively. However, qualitative analysis of the segmentation outputs on a sub-image scale revealed that towards the edges of the images, the segmentation performance of 30-degree lighting was significantly better, with results closely matching those of the ground truth. This research highlights that the lighting angle of incidence can increase the performance of directional lighting concrete crack segmentation depending on defect position. The results from this work have the potential to improve low-light environment concrete crack detection and monitoring.

As polyvinyl alcohol (PVA) hydrogels have shown a high potential as an ionic thermoelectric generator, this paper aims to study the thermoelectric (TE) power of salt-free PVA hydrogel. Ionic thermoelectric performance, cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS) are measured at different relative humidities. Findings show that salt-free PVA hydrogels can achieve a relatively high Seebeck (9.26 mV K−1) using only surrounding moisture. Hydrogels with low PVA content have a more stable TE output than hydrogels with high PVA content in high relative humidity conditions. Embedded Ni-foam-CNT electrodes stabilize performance, reduce noise, and provide superior capacitance, resistance, and thermoelectric performance over direct drop-casting of CNT on hydrogels. Recommended operating relative humidity (RH) ranges between 40 % and 60 %, while recommended PVA content ranges from 10 % to 15 % mass ratio of PVA in water. The results pave the way for using PVA hydrogels as thermoelectric devices in biocompatible applications.

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/reference less 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.

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.

External lighting is required for autonomous inspections of concrete structures in low-light environments; however, previous studies have only considered uniformly diffused lighting to illuminate images. This study proposes a novel algorithm that utilises angled and directional lighting to obtain pixel-level segmentation of concrete cracks. The method applies a concrete crack detection algorithm to separate images, each illuminated with lighting from a different direction. Using a bitwise OR operation, the findings from all images are combined; the resulting image highlights the extremities of any present cracks in all lighting directions. When tested on a dataset of cracks ranging in widths from 0.07 mm to 0.3 mm, the algorithm obtained recall, precision and F1 score results of 77%, 84% and 92%, respectively. The algorithm was able to correctly segment cracks that were deemed too thin for similar diffused lighting segmentation methods found in literature. The proposed directional lighting algorithm has the potential to improve concrete inspections in low-light environments.

Soil plug hazards pose a significant risk to the successful installation of suction caisson foundations but are currently inadequately monitored using only a single beam echosounder. To address this issue, a new artificial intelligence (AI) driven three-dimensional (3D) point scanner is proposed for monitoring soil plug hazards. The proposed scanner is controlled using a Bayesian optimisation (BO) algorithm, which automatically adapts its data acquisition path in real-time based on previously acquired measurements. Preliminary laboratory tests were conducted to assess the effectiveness of the proposed scanner. The results showed that the proposed scanner can accurately estimate 3D surfaces with fewer measurement points than a comparable scanner using the conventional scanning method, typically used in existing 3D point scanners. As the proposed scanner can estimate the state of the entire surface in much shorter time than existing sensors, it potentially offers a more effective method to monitor soil plug hazards.

Structural health monitoring (SHM) is the automation of the condition assessment process of an engineered system. When applied to geometrically large components or structures, such as those found in civil and aerospace infrastructure and systems, a critical challenge is in designing the sensing solution that could yield actionable information. This is a difficult task to conduct cost-effectively, because of the large surfaces under consideration and the localized nature of typical defects and damages. There have been significant research efforts in empowering conventional measurement technologies for applications to SHM in order to improve performance of the condition assessment process. Yet, the field implementation of these SHM solutions is still in its infancy, attributable to various economic and technical challenges. The objective of this Roadmap publication is to discuss modern measurement technologies that were developed for SHM purposes, along with their associated challenges and opportunities, and to provide a path to research and development efforts that could yield impactful field applications. The Roadmap is organized into four sections: distributed embedded sensing systems, distributed surface sensing systems, multifunctional materials, and remote sensing. Recognizing that many measurement technologies may overlap between sections, we define distributed sensing solutions as those that involve or imply the utilization of numbers of sensors geometrically organized within (embedded) or over (surface) the monitored component or system. Multi-functional materials are sensing solutions that combine multiple capabilities, for example those also serving structural functions. Remote sensing are solutions that are contactless, for example cell phones, drones, and satellites. It also includes the notion of remotely controlled robots.

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).

This project aim to robotically deploy vibrating wireless strain gauges (VWSG) into small scale steel fibre reinforced concrete (SFRC) tunnel segment making it smart. The VWSGs connected to an autonomous wireless node can establish ad-hoc modular networks with other smart segments and the segment properties can be tracked through their whole lifecycle. The main objective pursued are: (i) the design, the implementation, and the performance assessment of the robotic process of installing the sensors; (ii) the design, the fabrication and the mechanical testing of smart segment under cyclical loadings.

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, intersection over union, and F1 score results of 77%, 91%, 72%, and 84%, respectively. Skele-Marker was compared to other methods found in literature and was found to outperform other methods in terms of precision, intersection over union 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.

This study uses a novel directional lighting approach to produce a computationally efficient five-channel Visual Geometry Group-16 (VGG-16) convolutional neural network (CNN) model for concrete crack detection and classification in low-light environments. The first convolutional layer of the proposed model copies the weights for the first three channels from the pre-trained model. In contrast, the additional two channels are set to the average of the existing weights along the channel. The model employs transfer learning and fine-tuning approaches to enhance accuracy and efficiency. It utilizes variations in patterned lighting to produce five channels. Each channel represents a grayscale version of the images captured using directed lighting in the right, below, left, above, and diffused directions, respectively. The model is evaluated on concrete crack samples with crack widths ranging from 0.07 mm to 0.3 mm. The modified five-channel VGG-16 model outperformed the traditional three-channel model, showing improvements ranging from 6.5 to 11.7 percent in true positive rate, false positive rate, precision, F1 score, accuracy, and Matthew’s correlation coefficient. These performance improvements are achieved with no significant change in evaluation time. This study provides useful information for constructing custom CNN models for civil engineering problems. Furthermore, it introduces a novel technique to identify cracks in concrete buildings using directed illumination in low-light conditions.

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.

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.