Natural Frequencies Identification of a Reinforced Concrete Beam using Carbon Nanotube Cement-based Sensors.

Cementitious materials doped with carbon nanoparticles are robust materials capable of transducing strain into changes in electrical resistance. These properties encourage the development of spatially distributed sensors for structural health monitoring of concrete structures. Yet, very few applications of transducers made of cement-based nanocomposites to structural elements have been documented. The majority of applications are limited to measurement of static responses

Automated crack detection in conductive smart-concrete structures using a resistor mesh model

Various nondestructive evaluation techniques are currently used to automatically detect and monitor cracks in concrete infrastructure. However, these methods often lack the scalability and cost-effectiveness over large geometries. A solution is the use of self-sensing carbon-doped cementitious materials. These self-sensing materials are capable of providing a measurable change in electrical output that can be related to their damage state. Previous work by the authors showed that a resistor mesh model could be used to track damage in structural components fabricated from electrically conductive concrete, where damage was located through the identification of high resistance value resistors in a resistor mesh model. In this work, an automated damage detection strategy that works through placing high value resistors into the previously developed resistor mesh model using a sequential Monte Carlo method is introduced. Here, high value resistors are used to mimic the internal condition of damaged cementitious specimens. The proposed automated damage detection method is experimentally validated using a $500 x 500 x 50$ mm reinforced cement paste plate doped with multi-walled carbon nanotubes exposed to 100 identical impact tests. Results demonstrate that the proposed Monte Carlo method is capable of detecting and localizing the most prominent damage in a structure, demonstrating that automated damage detection in smart-concrete structures is a promising strategy for real-time structural health monitoring of civil infrastructure

Crack detection in RC structural components using a collaborative data fusion approach based on smart concrete and large-area sensors

Recent advances in the fields of nanocomposite technologies have enabled the development of highly scalable, low-cost sensing solution for civil infrastructures. This includes two sensing technologies, recently proposed by the authors, engineered for their high scalability, low-cost and mechanical simplicity. The first sensor consists of a smart-cementitious material doped with multi-wall carbon nanotubes, which has been demonstrated to be suitable for monitoring its own deformations (strain) and damage state (cracks). Integrated to a structure, this smart cementitious material can be used for detecting damage or strain through the monitoring of its electrical properties. The second sensing technology consists of a sensing skin developed from a flexible capacitor that is mounted externally onto the structure. When deployed in a dense sensor network configuration, these large area sensors are capable of covering large surfaces at low cost and can monitor both strain- and crack-induced damages. This work first presents a comparison of the capabilities of both technologies for crack detection in a concrete plate, followed by a fusion of sensor data for increased damage detection performance. Experimental results are conducted on a 50 50 5 cm3 plate fabricated with smart concrete and equipped with a dense sensor network of 20 large area sensors. Results show that both novel technologies are capable of increased damage localization when used concurrently

Smart bricks for strain sensing and crack detection in masonry structures

The paper proposes the novel concept of smart bricks as a durable sensing solution for structural health monitoring of masonry structures. The term smart bricks denotes piezoresistive clay bricks with suitable electronics capable of outputting measurable changes in their electrical properties under changes in their state of strain. This feature can be exploited to evaluate stress at critical locations inside a masonry wall and to detect changes in loading paths associated with structural damage, for instance following an earthquake. Results from an experimental campaign show that normal clay bricks, fabricated in the laboratory with embedded electrodes made of a special steel for resisting the high baking temperature, exhibit a quite linear and repeatable piezoresistive behavior. That is a change in electrical resistance proportional to a change in axial strain. In order to be able to exploit this feature for strain sensing, high-resolution electronics are used with a biphasic DC measurement approach to eliminate any resistance drift due to material polarization. Then, an enhanced nanocomposite smart brick is proposed, where titania is mixed with clay before baking, in order to enhance the brick\u27s mechanical properties, improve its noise rejection, and increase its electrical conductivity. Titania was selected among other possible conductive nanofillers due to its resistance to high temperatures and its ability to improve the durability of construction materials while maintaining the aesthetic appearance of clay bricks. An application of smart bricks for crack detection in masonry walls is demonstrated by laboratory testing of a small-scale wall specimen under different loading conditions and controlled damage. Overall, it is demonstrated that a few strategically placed smart bricks enable monitoring of the state of strain within the wall and provide information that is capable of crack detection

Self-powered weigh-in-motion system combining vibration energy harvesting and self-sensing composite pavements

Overloaded vehicles are the primary cause of accelerated degradation of road infrastructures. In this context, although weigh-in-motion (WIM) systems are most efficient to enforce weight regulations, current technologies require costly investments limiting their extensive implementation. Recent advances in multifunctional composites enabled cost-efficient alternatives in the form of smart pavements. Nevertheless, the need for a stable power supply still represents a major practical limitation. This work presents a novel proof-of-concept self-sustainable WIM technology combining smart pavements and vibration-based energy harvesting (EH). The feasibility of piezoelectric bimorph cantilevered beams to harvest traffic-induced vibrations is firstly investigated, followed by the demonstration of the proposed technology under laboratory conditions. The main original contributions of this work comprise (i) the development of a new self-powered data acquisition system, (ii) a novel approach for the fabrication and electromechanical testing of the piezoresistive composite pavement, and (iii) laboratory feasibility analysis of the developed EH unit to conduct traffic load identification through electrical resistivity measurements of the smart pavement. While the presented results conclude the need for dense EH networks or combinations of different EH technologies to attain complete self-sustainability, this work represents an initial feasibility evidence paving the way towards the development of self-powered low-cost WIM systems

Towards smart concrete for smart cities: Recent results and future application of strain-sensing nanocomposites

The use of smart technologies combined with city planning have given rise to smart cities, which empower modern urban systems with the efficient tools to cope with growing needs from increasing population sizes. For example, smart sensors are commonly used to improve city operations and management by tracking traffic, monitoring crowds at events, and performance of utility systems and public transportation. Recent advances in nanotechnologies have enabled a new family of sensors, termed self-sensing materials, which would provide smart cities with means to also monitor structural health of civil infrastructures. This includes smart concrete, which has the potential to provide any concrete structure with self-sensing capabilities. Such functional property is obtained by correlating the variation of internal strain with the variation of appropriate material properties, such as electrical resistance. Unlike conventional off-the-shelf structural health monitoring sensors, these innovative transducers combine enhanced durability and distributed measurements, thus providing greater scalability in terms of sensing size and cost. This paper presents recent advances on sensors fabricated using a cementitious matrix with nanoinclusions of Carbon Nanotubes (CNTs). The fabrication procedures providing homogeneous piezoresistive properties are presented, and the electromechanical behavior of the sensors is investigated under static and dynamic loads. Results show that the proposed sensors compare well against existing technologies of stress/strain monitoring, like strain gauges and accelerometers. Example of possible field applications for the developed nanocomposite cement-based sensors include traffic monitoring, parking management and condition assessment of masonry and concrete structures

Impatto dell'epidemia di COVID-19 sulla salute mentale degli infermieri di Terapia Intensiva. Uno studio multicentrico Italiano

Aim: The aim of this study was to evaluate variations in ICU nurses ' mental health status over the COVID-19 outbreak by quantifying the extent of symptoms of depression, anxiety and PTSD over time. Methods: This study was an Italian multicenter prospective cohort study assessing caseness of anxiety, depression and PTSD at 6 and 12 months from the beginning of the COVID-19 outbreak in Italy. Results: A total of 359 nurses, 233 (64.9%) were males and 126 (35.1%) were females were enrolled. At 6 months the caseness prevalence for anxiety, depression and PTSD were 31.3%, 32.1% and 18.7% respectively. At 12 months the caseness prevalence for anxiety, depression and PTSD were 34.8%, 36.4% and 24.1 % respectively. No statistically significant increase between 6 and 12 months was recorded for the caseness prevalence anxiety (p= .29) and depression (p= .19). However, an increase for the caseness prevalence PTSD at 12 months was observed (p= .049). The significant risk factors for the 221 patients with at least one disorders were age 31-40 (RR= 1.44, IC= 1.25-1.89; p < .001), female gender (RR= 1.31, IC= 1.02-1.51; p=. 042) and had 0-5 years of professional experience (RR= 1.36, IC= 1.02-1.63; p = .031). Conclusion: The results of our study may provide support for the implementation of some interventions for well-being in COVID-19 outbreak condition. Key words: Anxiety, Depression, Post-Traumatic stress disorder, Covid-19, Nurses, Mental health.Scopo: Lo scopo di questo studio era valutare le variazioni dello stato di salute mentale degli infermieri in terapia intensiva durante l'epidemia di COVID-19 quantificando l'entità dei sintomi di depressione, ansia e PTSD nel tempo. Metodi: Si tratta di uno studio di coorte prospettico multicentrico italiano che ha valutato la presenza di di ansia, depressione e PTSD a 6 e 12 mesi dall'inizio dell'epidemia di COVID-19. Risultati: Sono stati arruolati un totale di 359 infermieri, 233 (64.9%) uomini e 126 (35.1%) donne. A 6 mesi dall’inizio della pandemia, la prevalenza di disturbi di ansia, depressione e disturbo da stress post-traumatico era rispettivamente del 31.3%, 32.1% e 18.7%. A 12 mesi la prevalenza per ansia, depressione e PTSD era rispettivamente del 34.8%, 36.4% e 24.1%. Nessun aumento statisticamente significativo tra 6 e 12 mesi è stato registrato per l’ansia (p = .29) o la depressione (p = .19). Tuttavia, è stato osservato un aumento del disturbo da stress post-traumatico a 12 mesi (p = .049). I fattori di rischio significativi per i 221 pazienti con almeno un disturbo, erano un età di 31-40 (RR = 1.44, IC = 1.25-1.89; p < .001), sesso femminile (RR = 1.31, IC = 1.02-1.51; p = .042) e avere un esperienza professionale di 0-5 anni (RR = 1.36, IC = 1.02-1.63; p = .031). Conclusioni: I risultati del nostro studio possono fornire supporto per l'implementazione di alcuni interventi per il benessere lavorativo nella condizione di epidemia di COVID-19. Parole chiave: Ansia, depressione, disturbo da stress post-traumatico, Coronavirus, Infermieri, salute mentale

Strain monitoring in masonry structures using smart bricks

Monitoring a building’s structural performance is critical for the identification of incipient damages and the optimization of maintenance programs. The characteristics and spatial deployment of any sensing system plays an essential role in the reliability of the monitored data and, therefore, on the actual capability of the monitoring system to reveal early-stage structural damage. A promising strategy for enhancing the quality of a structural health monitoring system is the use of sensors fabricated using materials exhibiting similar mechanical properties and durability as those of the construction materials. Based on this philosophy, the authors have recently proposed the concept of smart-bricks that are nanocomposite clay bricks capable of transducing a change in volumetric strain into a change in a selected electrical property. Such brick-like sensors could be easily placed at critical locations within masonry walls, being an integral part of the structure itself. The sensing is enabled through the dispersion of fillers into the constitutive material. Examples of fillers include titania, carbon-based particles, and metallic microfibers. In this paper, experimental tests are conducted on bricks doped with different types of carbon-based fillers, tested both as standalone sensors and within small wall systems. Results show that mechanical properties as well as the smart brick’s strain sensitivity depend on the type of filler used. The capability of the bricks to work as strain monitoring sensors within small masonry specimens is also demonstrated

Strain sensitivity of carbon nanotube cement-based composites for structural health monitoring

Cement-based smart sensors appear particularly suitable for monitoring applications, due to their self-sensing abilities, their ease of use, and their numerous possible field applications. The addition of conductive carbon nanofillers into a cementitious matrix provides the material with piezoresistive characteristics and enhanced sensitivity to mechanical alterations. The strain-sensing ability is achieved by correlating the variation of external loads or deformations with the variation of specific electrical parameters, such as the electrical resistance. Among conductive nanofillers, carbon nanotubes (CNTs) have shown promise for the fabrication of self-monitoring composites. However, some issues related to the filler dispersion and the mix design of cementitious nanoadded materials need to be further investigated. For instance, a small difference in the added quantity of a specific nanofiller in a cement-matrix composite can substantially change the quality of the dispersion and the strain sensitivity of the resulting material. The present research focuses on the strain sensitivity of concrete, mortar and cement paste sensors fabricated with different amounts of carbon nanotube inclusions. The aim of the work is to investigate the quality of dispersion of the CNTs in the aqueous solutions, the physical properties of the fresh mixtures, the electromechanical properties of the hardened materials, and the sensing properties of the obtained transducers. Results show that cement-based sensors with CNT inclusions, if properly implemented, can be favorably applied to structural health monitoring