Replacement of Concrete Aggregates with Coal-Derived Flash Graphene.

Each year, the growth of cities across developing economies in Asia, Africa, and Latin America drives demand for concrete to house and serve their burgeoning populations. Since 1950, the number of people living in urban areas has quadrupled to 4.2 billion, with another predicted 2.5 billion expected to join them in the next three decades. The largest component of concrete by volume is aggregates, such as sand and rocks, with sand as the most mined material in the world. However, the extraction rate of sand currently exceeds its natural replenishment rate, meaning that a global concrete-suitable sand shortage is extremely likely. As such, replacements for fine aggregates, such as sand, are in demand. Here, flash Joule heating (FJH) is used to convert coal-derived metallurgical coke (MC) into flash graphene aggregate (FGA), a blend of MC-derived flash graphene (MCFG), which mimics a natural aggregate (NA) in size. While graphene and graphene oxide have previously been used as reinforcing additives to concrete, in this contribution, FGA is used as a total aggregate replacement for NA, resulting in 25% lighter concrete with increases in toughness, peak strain, and specific compressive strength of 32, 33, and 21%, respectively, with a small reduction in specific Young's modulus of 11%. FJH can potentially enable the replacement of fine NA with FGA, resulting in lighter, stronger concrete.

Deep Learning-Based Subsurface Damage Localization Using Full-Field Surface Strains.

Structures in their service life are often damaged as a result of aging or extreme events such as earthquakes or storms. It is essential to detect damage in a timely fashion to ensure the safe operation of the structure. If left unchecked, subsurface damage (SSD) can cause significant internal damage and may result in premature structural failure. In this study, a Convolutional Neural Network (CNN) has been developed for SSD detection using surface strain measurements. The adopted network architecture is capable of pixel-level image segmentation, that is, it classifies each location of strain measurement as damaged or undamaged. The CNN which is fed full-field strain measurements as an input image of size 256 × 256 projects the SSD onto an output image of the same size. The data for network training is generated by numerical simulation of aluminum bars with different damage scenarios, including single damage and double damage cases at a random location, direction, length, and thickness. The trained network achieves an Intersection over Union (IoU) score of 0.790 for the validation set and 0.794 for the testing set. To check the applicability of the trained network on materials other than aluminum, testing is performed on a numerically generated steel dataset. The IoU score is 0.793, the same as the aluminum dataset, affirming the network's capability to apply to materials exhibiting a similar stress-strain relationship. To check the generalization potential of the network, it is tested on triple damage cases; the IoU score is found to be 0.764, suggesting that the network works well for unseen damage patterns as well. The network was also found to provide accurate predictions for real experimental data obtained from Strain Sensing Smart Skin (S4). This proves the efficacy of the network to work in real-life scenarios utilizing the full potential of the novel full-field strain sensing methods such as S4. The performance of the proposed network affirms that it can be used as a non-destructive testing method for subsurface crack detection and localization.

Full-Field Vibration Response Estimation from Sparse Multi-Agent Automatic Mobile Sensors Using Formation Control Algorithm.

In structural vibration response sensing, mobile sensors offer outstanding benefits as they are not dedicated to a certain structure; they also possess the ability to acquire dense spatial information. Currently, most of the existing literature concerning mobile sensing involves human drivers manually driving through the bridges multiple times. While self-driving automated vehicles could serve for such studies, they might entail substantial costs when applied to structural health monitoring tasks. Therefore, in order to tackle this challenge, we introduce a formation control framework that facilitates automatic multi-agent mobile sensing. Notably, our findings demonstrate that the proposed formation control algorithm can effectively control the behavior of the multi-agent systems for structural response sensing purposes based on user choice. We leverage vibration data collected by these mobile sensors to estimate the full-field vibration response of the structure, utilizing a compressive sensing algorithm in the spatial domain. The task of estimating the full-field response can be represented as a spatiotemporal response matrix completion task, wherein the suite of multi-agent mobile sensors sparsely populates some of the matrix's elements. Subsequently, we deploy the compressive sensing technique to obtain the dense full-field vibration complete response of the structure and estimate the reconstruction accuracy. Results obtained from two different formations on a simply supported bridge are presented in this paper, and the high level of accuracy in reconstruction underscores the efficacy of our proposed framework. This multi-agent mobile sensing approach showcases the significant potential for automated structural response measurement, directly applicable to health monitoring and resilience assessment objectives.

Upcycling of Waste Plastic into Hybrid Carbon Nanomaterials.

Graphitic 1D and hybrid nanomaterials represent a powerful solution in composite and electronic applications due to exceptional properties, but large-scale synthesis of hybrid materials has yet to be realized. Here, a rapid, scalable method to produce graphitic 1D materials from polymers using flash Joule heating (FJH) is reported. This avoids lengthy chemical vapor deposition and uses no solvent or water. The flash 1D materials (F1DM), synthesized using a variety of earth-abundant catalysts, have controllable diameters and morphologies by parameter tuning. Furthermore, the process can be modified to form hybrid materials, with F1DM bonded to turbostratic graphene. In nanocomposites, F1DM outperform commercially available carbon nanotubes. Compared to current 1D material synthetic strategies using life cycle assessment, FJH synthesis represents an 86-92% decrease in cumulative energy demand and 92-94% decrease in global-warming potential. This work suggests that FJH affords a cost-effective and sustainable route to upcycle waste plastic into valuable 1D and hybrid nanomaterials.

Next-generation 2D optical strain mapping with strain-sensing smart skin compared to digital image correlation.

This study reports next generation optical strain measurement with "strain-sensing smart skin" (S4) and a comparison of its performance against the established digital image correlation (DIC) method. S4 measures strain-induced shifts in the emission wavelengths of single-wall carbon nanotubes embedded in a thin film on the specimen. The new S4 film improves spectral uniformity of the nanotube sensors, avoids the need for annealing at elevated temperatures, and allows for parallel DIC measurements. Noncontact strain maps measured with the S4 films and point-wise scanning were directly compared to those from DIC on acrylic, concrete, and aluminum test specimens, including one with subsurface damage. Strain features were more clearly revealed with S4 than with DIC. Finite element method simulations also showed closer agreement with S4 than with DIC results. These findings highlight the potential of S4 strain measurement technology as a promising alternative or complement to existing technologies, especially when accumulated strains must be detected in structures that are not under constant observation.

Near-infrared photoluminescence of Portland cement.

Portland cement emits bright near-infrared photoluminescence that can be excited by light wavelengths ranging from at least 500-1000 nm. The emission has a peak wavelength near 1140 nm and a width of approximately 30 nm. Its source is suggested to be small particles of silicon associated with calcium silicate phases. The luminescence peak wavelength appears independent of the cement hydration state, aggregates, and mechanical strain but increases weakly with increasing temperature. It varies slightly with the type of cement, suggesting a new non-contact method for identifying cement formulations. After a thin opaque coating is applied to a cement or concrete surface, subsequent formation of microcracks exposes the substrate's near-infrared emission, revealing the fracture locations, pattern, and progression. This damage would escape detection in normal imaging inspections. Near-infrared luminescence imaging may therefore provide a new tool for non-destructive testing of cement-based structures.

Physics-Guided Real-Time Full-Field Vibration Response Estimation from Sparse Measurements Using Compressive Sensing.

In civil, mechanical, and aerospace structures, full-field measurement has become necessary to estimate the precise location of precise damage and controlling purposes. Conventional full-field sensing requires dense installation of contact-based sensors, which is uneconomical and mostly impractical in a real-life scenario. Recent developments in computer vision-based measurement instruments have the ability to measure full-field responses, but implementation for long-term sensing could be impractical and sometimes uneconomical. To circumvent this issue, in this paper, we propose a technique to accurately estimate the full-field responses of the structural system from a few contact/non-contact sensors randomly placed on the system. We adopt the Compressive Sensing technique in the spatial domain to estimate the full-field spatial vibration profile from the few actual sensors placed on the structure for a particular time instant, and executing this procedure repeatedly for all the temporal instances will result in real-time estimation of full-field response. The basis function in the Compressive Sensing framework is obtained from the closed-form solution of the generalized partial differential equation of the system; hence, partial knowledge of the system/model dynamics is needed, which makes this framework physics-guided. The accuracy of reconstruction in the proposed full-field sensing method demonstrates significant potential in the domain of health monitoring and control of civil, mechanical, and aerospace engineering systems.

Vision and Deep Learning-Based Algorithms to Detect and Quantify Cracks on Concrete Surfaces from UAV Videos.

Immediate assessment of structural integrity of important civil infrastructures, like bridges, hospitals, or dams, is of utmost importance after natural disasters. Currently, inspection is performed manually by engineers who look for local damages and their extent on significant locations of the structure to understand its implication on its global stability. However, the whole process is time-consuming and prone to human errors. Due to their size and extent, some regions of civil structures are hard to gain access for manual inspection. In such situations, a vision-based system of Unmanned Aerial Vehicles (UAVs) programmed with Artificial Intelligence algorithms may be an effective alternative to carry out a health assessment of civil infrastructures in a timely manner. This paper proposes a framework of achieving the above-mentioned goal using computer vision and deep learning algorithms for detection of cracks on the concrete surface from its image by carrying out image segmentation of pixels, i.e., classification of pixels in an image of the concrete surface and whether it belongs to cracks or not. The image segmentation or dense pixel level classification is carried out using a deep neural network architecture named U-Net. Further, morphological operations on the segmented images result in dense measurements of crack geometry, like length, width, area, and crack orientation for individual cracks present in the image. The efficacy and robustness of the proposed method as a viable real-life application was validated by carrying out a laboratory experiment of a four-point bending test on an 8-foot-long concrete beam of which the video is recorded using a camera mounted on a UAV-based, as well as a still ground-based, video camera. Detection, quantification, and localization of damage on a civil infrastructure using the proposed framework can directly be used in the prognosis of the structure's ability to withstand service loads.

Strain paint: noncontact strain measurement using single-walled carbon nanotube composite coatings.

Composite coatings have been developed that reveal strains in underlying structural elements through noncontact optical measurement. Dilute individualized single-walled carbon nanotubes are embedded in a polymeric host and applied to form a thin coating. Strain in the substrate is transmitted through the polymer to the nanotubes, causing systematic and predictable spectral shifts of the nanotube near-infrared fluorescence peaks. This new method allows quick and precise strain measurements at any position and along any direction of the substrate.

Flexible piezoelectric ZnO-paper nanocomposite strain sensor.

The fabrication of a mechanically flexible, piezoelectric nanocomposite material for strain sensing applications is reported. Nanocomposite material consisting of zinc oxide (ZnO) nanostructures embedded in a stable matrix of paper (cellulose fibers) is prepared by a solvothermal method. The applicability of this material as a strain sensor is demonstrated by studying its real-time current response under both static and dynamic mechanical loading. The material presented highlights a novel approach to introduce flexibility into strain sensors by embedding crystalline piezoelectric material in a flexible cellulose-based secondary matrix.