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Utility as-built plans, which typically provide information about underground utilities’ position and spatial locations, are known to comprise inaccuracies. Over the years, the reliance on utility investigations using an array of sensing equipment has increased in an attempt to resolve utility as-built inaccuracies and mitigate the high rate of accidental underground utility strikes during excavation activities. Adapting data fusion into utility engineering and investigation practices has been shown to be effective in generating information with improved accuracy. However, the complexities in data interpretation and associated prohibitive costs, especially for large-scale projects, are limiting factors. This paper addresses the problem of data interpretation, costs, and large-scale utility mapping with a novel framework that generates probabilistic inferences by fusing data from an automatically generated initial map with as-built data. The probabilistic inferences expose regions of high uncertainty, highlighting them as prime targets for further investigations. The proposed model is a collection of three main processes. First, the automatic initial map creation is a novel contribution supporting rapid utility mapping by subjecting identified utility appurtenances to utility inference rules. The second and third processes encompass the fusion of the created initial utility map with available knowledge from utility as-builts or historical satellite imagery data and then evaluating the uncertainties using confidence value estimators. The proposed framework transcends the point estimation of buried utility locations in previous works by producing a final probabilistic utility map, revealing a confidence level attributed to each segment linking aboveground features. In this approach, the utility infrastructure is rapidly mapped at a low cost, limiting the extent of more detailed utility investigations to low-confidence regions. In resisting obsolescence, another unique advantage of this framework is the dynamic nature of the mapping to automatically update information upon the arrival of new knowledge. This ultimately minimizes the problem of utility as-built accuracies dwindling over time.

Recent advances in soft polymer materials have enabled the design of soft machines and devices at multiple scales. Their intrinsic compliance and robust mechanical properties and the potential for a rapid scaling of the production process make them ideal candidates for flexible and stretchable electronics and sensors. Large-area electronics (LAE) made from soft polymer materials that are capable of sustaining large deformations and covering large surfaces and are applicable to complex and irregular surfaces and transducing deformations into readable signals have been explored for structural health monitoring (SHM) applications. The authors have previously proposed and developed an LAE consisting of a corrugated soft elastomeric capacitor (cSEC). The corrugation is used to engineer the directional strain sensitivity by using a thermoplastic styrene-ethylene-butadiene-styrene (SEBS). A key limitation of the SEBS-cSEC technology is the need of an epoxy for reliable bonding of the sensor onto the monitored surface, mainly attributable to the sensor’s fabrication process that comprises a solvent that limits its direct deployment through a painting process. Here, with the objective to produce a paintable cSEC, we study an improved solvent-free fabrication method by using a commercial room-temperature-vulcanizing silicone as the host matrix. The matrix is filled with titania particles to form the dielectric layer, yielding a permittivity of 4.05. Carbon black powder is brushed onto the dielectric and encapsulated with the same silicone to form the conductive stretchable electrodes. The sensor is deployed by directly painting a layer of the silicone onto the monitored surface and then depositing the parallel plate capacitor. The electromechanical behavior of the painted silicone-cSEC was characterized and exhibited good linearity, with an R2 value of 0.9901, a gauge factor of 1.58, and a resolution of 70 με. This resolution compared well with that of the epoxied SEBS-cSEC reported in previous work (25 με). Its performance was compared against that of its more mature version, the SEBS-cSEC, in a network configuration on a cantilever plate subjected to a step-deformation and to free vibrations. Results showed that the performance of the painted silicone-sCEC compared well with that of the SEBS-cSEC, but that the use of a silicone paint instead of an epoxy could be responsible for larger noise and the under-estimation of the dominating frequency by 6.7%, likely attributable to slippage.

Accurately identifying the location and depth of buried utility assets became a considerable challenge in the construction industry, for which accidental strikes can cause important economic losses and safety concerns. While the collection of as-built utility locations is becoming more accurate, there still exists an important need to be capable of accurately detecting buried utilities in order to eliminate risks associated with digging. Current practices typically involve the use of trained agents to survey and detect underground utilities at locations of interest, which is a costly and time-consuming process. With advances in artificial intelligence (AI), an opportunity arose in conducting virtual sensing of buried utilities by combining robotics (e.g., drones), knowledge, and logic. This paper reviewed methods that are based on AI in mapping underground infrastructure. In particular, the use of AI in aerial and terrestrial mapping of utility assets was reviewed, followed by a summary of AI techniques used in fusing multi-source data in creating underground infrastructure maps. Key observations from the consolidated literature were that (1) when leveraging computer vision methods, automatic mapping techniques vastly focus on manholes localized from aerial imagery; (2) when applied to non-intrusive sensing, AI methods vastly focus on empowering ground-penetrating radar (GPR)-produced data; and (3) data fusion techniques to produce utility maps should be extended to any utility assets/types. Based on these observations, a universal utility mapping model was proposed, one that could enable mapping of underground utilities using limited information available in the form of different sources of data and knowledge.

En quoi les sciences de l’humain se distinguent-elles des sciences de la nature ?

Abstract Background Suture materials and techniques are frequently evaluated in ex vivo studies by comparing tensile strengths. However, the direct measurement techniques to obtain the tensile forces in canine skin are not available, and, therefore, the conditions suture lines undergo is unknown. A soft elastomeric capacitor is used to monitor deformation in the skin over time by sensing strain. This sensor was applied to a sample of canine skin to evaluate its capacity to sense strain in the sample while loaded in a dynamic material testing machine. The measured strain of the sensor was compared with the strain measured by the dynamic testing machine. The sample of skin was evaluated with and without the sensor adhered. Results In this study, the soft elastomeric capacitor was able to measure strain and a correlation was made to stress using a modified Kelvin-Voigt model for the canine skin sample. The sensor significantly increases the stiffness of canine skin when applied which required the derivation of mechanical models for interpretation of the results. Conclusions Flexible sensors can be applied to canine skin to investigate the inherent biomechanical properties. These sensors need to be lightweight and highly elastic to avoid interference with the stress across a suture line. The sensor studied here serves as a prototype for future sensor development and has demonstrated that a lightweight highly elastic sensor is needed to decrease the effect on the sensor/skin construct. Further studies are required for biomechanical characterization of canine skin.

This paper presents a field implementation of the structural health monitoring (SHM) of fatigue cracks for steel bridge structures. Steel bridges experience fatigue cracks under repetitive traffic loading, which pose great threats to their structural integrity and can lead to catastrophic failures. Currently, accurate and reliable fatigue crack monitoring for the safety assessment of bridges is still a difficult task. On the other hand, wireless smart sensors have achieved great success in global SHM by enabling long-term modal identifications of civil structures. However, long-term field monitoring of localized damage such as fatigue cracks has been limited due to the lack of effective sensors and the associated algorithms specifically designed for fatigue crack monitoring. To fill this gap, this paper proposes a wireless large-area strain sensor (WLASS) to measure large-area strain fatigue cracks and develops an effective algorithm to process the measured large-area strain data into actionable information. The proposed WLASS consists of a soft elastomeric capacitor (SEC) used to measure large-area structural surface strain, a capacitive sensor board to convert the signal from SEC to a measurable change in voltage, and a commercial wireless smart sensor platform for triggered-based wireless data acquisition, remote data retrieval, and cloud storage. Meanwhile, the developed algorithm for fatigue crack monitoring processes the data obtained from the WLASS under traffic loading through three automated steps, including (1) traffic event detection, (2) time-frequency analysis using a generalized Morse wavelet (GM-CWT) and peak identification, and (3) a modified crack growth index (CGI) that tracks potential fatigue crack growth. The developed WLASS and the algorithm present a complete system for long-term fatigue crack monitoring in the field. The effectiveness of the proposed time-frequency analysis algorithm based on GM-CWT to reliably extract the impulsive traffic events is validated using a numerical investigation. Subsequently, the developed WLASS and algorithm are validated through a field deployment on a steel highway bridge in Kansas City, KS, USA.

The tuned liquid multiple column damper (TLMCD) is a variation of the tuned liquid column damper (TLCD) that includes multiple vertical columns. A new damping system that embeds TLMCDs within reinforced concrete shear wall systems, termed tuned liquid wall damper (TLWD), is proposed, augmenting the traditional structural component with energy dissipation capabilities. The objective of this study is to assess energy mitigation and strength trade-offs in designing TLWDs and demonstrating the promise of TLWD systems in tall buildings through vertically distributed applications. This is done by investigating the performance of the proposed TLWD through the finite element model (FEM) of a simplified representation of a 42-story building equipped with the multifunctional component. A strength model for the TLWD is developed to empower faster performance evaluation on more complex models. Results from the FEM are used to validate the strength model and show that the model could be used conservatively in assessing strength performance. Design considerations are discussed based on the simplified representation. In particular, to improve mitigation performance while maintaining strength, it is found that a single-layer arrangement of the vertical columns is preferred, while distributing the inertia among a higher number of smaller columns. The proposed TLWD is numerically evaluated on a more realistic system consisting of a multi-degrees-of-freedom representation of the 42-story building under stochastic wind excitation. Simulation results demonstrate that the TLWD, used in a vertically distributed configuration through the building, could be used to mitigate vibrations, outperforming a traditional TLCD system with geometric constraints under 20 design wind realization. Results from the numerical simulations also confirmed the design considerations established through the simplified representation.