Concept: Reinforced concrete
Calcium silicate hydrate (C-S-H) is the binder in concrete, the most used synthetic material in the world. The main weakness of concrete is the lack of elasticity and poor flexural strength considerably limiting its potential, making reinforcing steel constructions necessary. Although the properties of C-S-H could be significantly improved in organic hybrids, the full potential of this approach could not be reached because of the random C-S-H nanoplatelet structure. Taking inspiration from a sea urchin spine with highly ordered nanoparticles in the biomineral mesocrystal, we report a bioinspired route toward a C-S-H mesocrystal with highly aligned C-S-H nanoplatelets interspaced with a polymeric binder. A material with a bending strength similar to nacre is obtained, outperforming all C-S-H-based materials known to date. This strategy could greatly benefit future construction processes because fracture toughness and elasticity of brittle cementitious materials can be largely enhanced on the nanoscale.
The construction industry is one of the biggest and most active sectors of the European Union (EU), consuming more raw materials and energy than any other economic activity. Furthermore, construction waste is the commonest waste produced in the EU. Current EU legislation sets out to implement construction and demolition waste (CDW) prevention and recycling measures. However it lacks tools to accelerate the development of a sector as bound by tradition as the building industry. The main objective of the present study was to determine indicators to estimate the amount of CDW generated on site both globally and by waste stream. CDW generation was estimated for six specific sectors: new residential construction, new non-residential construction, residential demolition, non-residential demolition, residential refurbishment, and non-residential refurbishment. The data needed to develop the indicators was collected through an exhaustive survey of previous international studies. The indicators determined suggest that the average composition of waste generated on site is mostly concrete and ceramic materials. Specifically for new residential and new non-residential construction the production of concrete waste in buildings with a reinforced concrete structure lies between 17.8 and 32.9 kg m(-2) and between 18.3 and 40.1 kg m(-2), respectively. For the residential and non-residential demolition sectors the production of this waste stream in buildings with a reinforced concrete structure varies from 492 to 840 kg m(-2) and from 401 to 768 kg/m(-2), respectively. For the residential and non-residential refurbishment sectors the production of concrete waste in buildings lies between 18.9 and 45.9 kg/m(-2) and between 18.9 and 191.2 kg/m(-2), respectively.
In this paper the influence of an example indoor environment on narrowband radio channel path loss for body area networks operating around 2.4 GHz is investigated using computer simulations and on-site measurements. In contrast to other similar studies, the simulation model included both a numerical human body phantom and its environment-room walls, floor and ceiling. As an example, radio signal attenuation between two different configurations of transceivers with dipole antennas placed in a direct vicinity of a human body (on-body scenario) is analyzed by computer simulations for several types of reflecting environments. In the analyzed case the propagation environments comprised a human body and office room walls. As a reference environment for comparison, free space with only a conducting ground plane, modelling a steel mesh reinforced concrete floor, was chosen. The transmitting and receiving antennas were placed in two on-body configurations chest-back and chest-arm. Path loss vs. frequency simulation results obtained using Finite Difference Time Domain (FDTD) method and a multi-tissue anthropomorphic phantom were compared to results of measurements taken with a vector network analyzer with a human subject located in an average-size empty cuboidal office room. A comparison of path loss values in different environments variants gives some qualitative and quantitative insight into the adequacy of simplified indoor environment model for the indoor body area network channel representation.
Ground penetrating radar (GPR) is used to evaluate deterioration of reinforced concrete bridge decks based on measuring signal attenuation from embedded rebar. The existing methods for obtaining deterioration maps from GPR data often require manual interaction and offsite processing. In this paper, a novel algorithm is presented for automated rebar detection and analysis. We test the process with comprehensive measurements obtained using a novel state-of-the-art robotic bridge inspection system equipped with GPR sensors. The algorithm achieves robust performance by integrating machine learning classification using image-based gradient features and robust curve fitting of the rebar hyperbolic signature. The approach avoids edge detection, thresholding, and template matching that require manual tuning and are known to perform poorly in the presence of noise and outliers. The detected hyperbolic signatures of rebars within the bridge deck are used to generate deterioration maps of the bridge deck. The results of the rebar region detector are compared quantitatively with several methods of image-based classification and a significant performance advantage is demonstrated. High rates of accuracy are reported on real data that includes thousands of individual hyperbolic rebar signatures from three real bridge decks.
Forecasting the life of concrete infrastructures in corrosive environments presents a long-standing and socially relevant challenge in science and engineering. Chloride-induced corrosion of reinforcing steel in concrete is the main cause for premature degradation of concrete infrastructures worldwide. Since the middle of the past century, this challenge has been tackled by using a conceptual approach relying on a threshold chloride concentration for corrosion initiation (Ccrit). All state-of-the-art models for forecasting chloride-induced steel corrosion in concrete are based on this concept. We present an experiment that shows that Ccrit depends strongly on the exposed steel surface area. The smaller the tested specimen is, the higher and the more variable Ccrit becomes. This size effect in the ability of reinforced concrete to withstand corrosion can be explained by the local conditions at the steel-concrete interface, which exhibit pronounced spatial variability. The size effect has major implications for the future use of the common concept of Ccrit. It questions the applicability of laboratory results to engineering structures and the reproducibility of typically small-scale laboratory testing. Finally, we show that the weakest link theory is suitable to transform Ccrit from small to large dimensions, which lays the basis for taking the size effect into account in the science and engineering of forecasting the durability of infrastructures.
This article presents an application of an active all-optical photoacoustic sensing system with four elements for steel rebar corrosion monitoring. The sensor utilized a photoacoustic mechanism of gold nanocomposites to generate 8 MHz broadband ultrasound pulses in 0.4 mm compact space. A nanosecond 532 nm pulsed laser and 400 μm multimode fiber were employed to incite an ultrasound reaction. The fiber Bragg gratings were used as distributed ultrasound detectors. Accelerated corrosion testing was applied to four sections of a single steel rebar with four different corrosion degrees. Our results demonstrated that the mass loss of steel rebar displayed an exponential growth with ultrasound frequency shifts. The sensitivity of the sensing system was such that 0.175 MHz central frequency reduction corresponded to 0.02 g mass loss of steel rebar corrosion. It was proved that the all-optical photoacoustic sensing system can actively evaluate the corrosion of steel rebar via ultrasound spectrum. This multipoint all-optical photoacoustic method is promising for embedment into a concrete structure for distributed corrosion monitoring.
This work proposes a 3D shaped optic fiber sensor for ultrasonic stress waves detection based on the principle of a Mach–Zehnder interferometer. This sensor can be used to receive acoustic emission signals in the passive damage detection methods and other types of ultrasonic signals propagating in the active damage detection methods, such as guided wave-based methods. The sensitivity of an ultrasonic fiber sensor based on the Mach–Zehnder interferometer mainly depends on the length of the sensing optical fiber; therefore, the proposed sensor achieves the maximum possible sensitivity by wrapping an optical fiber on a hollow cylinder with a base. The deformation of the optical fiber is produced by the displacement field of guided waves in the hollow cylinder. The sensor was first analyzed using the finite element method, which demonstrated its basic sensing capacity, and the simulation signals have the same characteristics in the frequency domain as the excitation signal. Subsequently, the primary investigations were conducted via a series of experiments. The sensor was used to detect guided wave signals excited by a piezoelectric wafer in an aluminum plate, and subsequently it was tested on a reinforced concrete beam, which produced acoustic emission signals via impact loading and crack extension when it was loaded to failure. The signals obtained from a piezoelectric acoustic emission sensor were used for comparison, and the results indicated that the proposed 3D fiber optic sensor can detect ultrasonic signals in the specific frequency response range.
Structural Health Monitoring (SHM) has moved to data-dense systems, utilizing numerous sensor types to monitor infrastructure, such as bridges and dams, more regularly. One of the issues faced in this endeavour is the scale of the inspected structures and the time it takes to carry out testing. Installing automated systems that can provide measurements in a timely manner is one way of overcoming these obstacles. This study proposes an Artificial Neural Network (ANN) application that determines intact and damaged locations from a small training sample of impact-echo data, using air-coupled microphones from a reinforced concrete beam in lab conditions and data collected from a field experiment in a parking garage. The impact-echo testing in the field is carried out in a semi-autonomous manner to expedite the front end of the in situ damage detection testing. The use of an ANN removes the need for a user-defined cutoff value for the classification of intact and damaged locations when a least-square distance approach is used. It is postulated that this may contribute significantly to testing time reduction when monitoring large-scale civil Reinforced Concrete (RC) structures.
When using distributed optical fiber sensors (DOFS) on reinforced concrete structures, a compromise must be achieved between the protection requirements and robustness of the sensor deployment and the accuracy of the measurements both in the uncracked and cracked stages and under loading, unloading and reloading processes. With this in mind the authors have carried out an experiment where polyimide-coated DOFS were installed on two concrete beams, both embedded in the rebar elements and also bonded to the concrete surface. The specimens were subjected to a three-point load test where after cracking, they are unloaded and reloaded again to assess the capability of the sensor when applied to a real loading scenarios in concrete structures. Rayleigh Optical Frequency Domain Reflectometry (OFDR) was used as the most suitable technique for crack detection in reinforced concrete elements. To verify the reliability and accuracy of the DOFS measurements, additional strain gauges were also installed at three locations along the rebar. The results show the feasibility of using a thin coated polyimide DOFS directly bonded on the reinforcing bar without the need of indention or mechanization. A proposal for a Spectral Shift Quality (SSQ) threshold is also obtained and proposed for future works when using polyimide-coated DOFS bonded to rebars with cyanoacrylate adhesive.
For a reinforced concrete beam subjected to fatigue loads, the structural stiffness and bearing capacity will gradually undergo irreversible degeneration, leading to damage. Moreover, there is an inherent relationship between the stiffness and bearing capacity degradation and fatigue damage. In this study, a series of fatigue tests are performed to examine the degradation law of the stiffness and bearing capacity. The results pertaining to the stiffness show that the stiffness degradation of a reinforced concrete beam exhibits a very clear monotonic decreasing “S” curve, i.e., the stiffness of the beam decreases significantly at the start of the fatigue loading, it undergoes a linear decline phase in the middle for a long loading period, and before the failure, the bearing capacity decreases drastically again. The relationship between the residual stiffness and residual bearing capacity is determined based on the assumption that the residual stiffness and residual bearing capacity depend on the same damage state, and then, the bearing capacity degradation model of the reinforced concrete beam is established based on the fatigue stiffness. Through the established model and under the premise of the known residual stiffness degradation law, the degradation law of the bearing capacity is determined by using at least one residual bearing capacity test data, for which the parameters of the stiffness degradation function are considered as material constants. The results of the bearing capacity show that the bearing capacity degradation of the reinforced concrete beam also exhibits a very clear monotonic decreasing “S” curve, which is consistent with the stiffness degradation process and in good agreement with the experiment. In this study, the stiffness and bearing capacity degradation expressions are used to quantitatively describe their occurrence in reinforced concrete beams. In particular, the expression of the bearing capacity degradation can mitigate numerous destructive tests and save cost. The stiffness and bearing capacity degradation expressions for a reinforced concrete beam can be used to predict the deformation and bearing capacity of a structure during the service process and determine the structural fatigue damage and degree of degradation.