Environment-Friendly, Self-Sensing Concrete Blended with Byproduct Wastes.

Smart structures have attracted significant research attention in the last decade, mainly due to the capabilities of advanced concrete in electrical resistance-enabled self-sensing. In this study, we present a type of environment-friendly, self-sensing concrete enabled by electrical resistance. Environment-friendly, self-sensing concrete was casted with the additions of byproduct wastes (i.e., coal fly ash (FA), blast furnace slag (BOF) and red mud (RM)) at various volume fractions and cured using the conditions of 3, 7 and 28 days. The self-sensing concrete samples were experimentally tested to investigate the effects of the byproduct wastes on the mechanical and electrical properties (i.e., compressive strength and electrical resistance). In the end, parametric studies were experimentally conducted to investigate the influences of the byproduct wastes on the mechanical and electrical properties of the reported environment-friendly, self-sensing concrete.

A Novel Data Reduction Approach for Structural Health Monitoring Systems.

The massive amount of data generated by structural health monitoring (SHM) systems usually affects the system's capacity for data transmission and analysis. This paper proposes a novel concept based on the probability theory for data reduction in SHM systems. The beauty salient feature of the proposed method is that it alleviates the burden of collecting and analysis of the entire strain data via a relative damage approach. In this methodology, the rate of variation of strain distributions is related to the rate of damage. In order to verify the accuracy of the approach, experimental and numerical studies were conducted on a thin steel plate subjected to cyclic in-plane tension loading. Circular holes with various sizes were made on the plate to define damage states. Rather than measuring the entire strain response, the cumulative durations of strain events at different predefined strain levels were obtained for each damage scenario. Then, the distribution of the calculated cumulative times was used to detect the damage progression. The results show that the presented technique can efficiently detect the damage progression. The damage detection accuracy can be improved by increasing the predefined strain levels. The proposed concept can lead to over 2500% reduction in data storage requirement, which can be particularly important for data generation and data handling in on-line SHM systems.

Self-Triggered Thermomechanical Metamaterials with Asymmetric Structures for Programmable Response under Thermal Excitations.

In this study, we propose self-triggered thermomechanical metamaterials (ST-MM) by applying thermomechanical materials in mechanical metamaterials designed with asymmetric structures (i.e., microstructural hexagons and chiral legs). The thermomechanical metamaterials are observed with programmable mechanical response under thermal excitations, which are used in mechanical metamaterials to obtain chiral tubes with negative Poisson's ratio and microgrippers with temperature-induced grabbing response. Theoretical and numerical models are developed to analyze the thermomechanical response of the ST-MM from the material and structural perspectives. Finally, we envision advanced applications of the ST-MM as chiral stents and thermoresponsive microgrippers with maximum grabbing force of approximately 101.7 N. The emerging ST-MM provide a promising direction for the design and perception of smart mechanical metamaterials.

Monitoring of Postoperative Bone Healing Using Smart Trauma-Fixation Device With Integrated Self-Powered Piezo-Floating-Gate Sensors.

OBJECTIVE: Achieving better surgical outcomes in cases of traumatic bone fractures requires postoperative monitoring of changes in the growth and mechanical properties of the tissue and bones during the healing process. While current in-vivo imaging techniques can provide a snapshot of the extent of bone growth, it is unable to provide a history of the healing process, which is important if any corrective surgery is required. Monitoring the time evolution of in-vivo mechanical loads using existing technology is a challenge due to the need for continuous power while maintaining patient mobility and comfort. METHODS: This paper investigates the feasibility of self-powered monitoring of the bone-healing process using our previously reported piezo-floating-gate (PFG) sensors. The sensors are directly integrated with a fixation device and operate by harvesting energy from microscale strain variations in the fixation structure. RESULTS: We show that the sensors can record and store the statistics of the strain evolution during the healing process for offline retrieval and analysis. Additionally, we present measurement results using a biomechanical phantom comprising of a femur fracture fixation plate; bone healing is emulated by inserting different materials, with gradually increasing elastic moduli, inside a fracture gap. CONCLUSION: The PFG sensor can effectively sense, compute, and record continuously evolving statistics of mechanical loading over a typical healing period of a bone, and the statistics could be used to differentiate between different bone-healing conditions. SIGNIFICANCE: The proposed sensor presents a reliable objective technique to assess bone-healing progress and help decide on the removal time of the fixation device.

A sub-microwatt piezo-floating-gate sensor for long-term fatigue monitoring in biomechanical implants.

In this paper we describe an implementation of a novel fatigue monitoring sensor based on integration of piezoelectric transduction with floating gate avalanche injection. The miniaturized sensor enables continuous battery-less monitoring and time-to-failure predictions of biomechanical implants. Measured results from a fabricated prototype in a 0.5 microm CMOS process indicate that the device can compute cumulative statistics of electrical signals generated by piezoelectric transducer, while consuming less that 1 microW of power. The ultra-low power operation makes the sensor attractive for integration with poly-vinylidene difluoride (PVDF) based transducers that have already proven to be biocompatible.