Structural Health Monitoring Solutions Research Articles

Automated manufacture and experimentation of composite overwrapped pressure vessel with embedded optical sensor

In recent years, the mobility (air/land) industry has been developing fuel-cell vehicles for mass adoption as hydrogen technology matures. Compared to conventional metallic design, the composite overwrapped pressure vessel without a metal or polymer liner results in a lightweight solution for gaseous hydrogen storage which further improves fuel efficiencies. Since the capabilities of automated fiber placement for making multi-stiffened structures are distinct from other automated manufacturing techniques, this study aimed to evaluate the feasibility of implementing AFP into the manufacture of a small scale COPV with a partially composite liner. The manufacturing related challenges are identified and discussed. During the manufacturing process, Distributed fiber optic sensor was embedded between the plies, as a structural health monitoring solution, to monitor the mechanical performance of the pressurized tank. In addition, finite element study was done to predict the internal strains for different pressure levels and the measured experimental strains at the sensor's location are found to be in close agreement with the finite element results. It is shown that such sensors can be integrated into automated fiber placement manufactured tanks for internal strain monitoring.

Thermal compensation of monolithic distributed fibre optic sensors: From the lab to the field

Distributed fibre optic sensing (DFOS) is gaining popularity as a diagnostic solution for short-term non-destructive testing (NDT) and long-term structural health monitoring (SHM). However, there are still challenges to be overcome, particularly with regard to long-term measurements under real field conditions, taking into account, among other things, temperature changes. These changes affect strain measurements through both thermal expansion and optical effects, and appropriate correction depends on the internal design of the sensor and the installation methods. Therefore, despite the fact that thermal compensation is one of the key prerequisites for correct data interpretation, there are no universal and clear procedures for DFOS. The lack of sufficient guidelines and proven applications is the main motivation for undertaking studies. The scope and novelty of the presented research lies in 1) the theoretical explanation of the thermal performance of monolithic DFOS sensors compared to the layered sensing cables; 2) the analysis and correction of strain data measured with monolithic sensors placed in a thermal chamber; 3) the verification of the laboratory results and the proposed approach in real field conditions: in a concrete footbridge deck and in a steel underground gas pipeline. All presented measurements were performed with high spatial resolution (mm order) Rayleigh-based interrogators under the control of conventional reference spot thermistors. The research demonstrated the linear thermal response of the monolithic sensors and confirmed their usefulness for distinguishing between length- and stress-dependent strains. Finally, the thermal compensation approach was proposed and verified in real operating conditions.

Sensitivity-matrix-independent electrical impedance tomography: Damage detection for composite materials

Carbon fiber reinforced polymer (CFRP) is widely utilized due to their superior properties. The structural health monitoring (SHM) for composite materials is crucial to prevent catastrophic failures. Electrical impedance tomography (EIT), which allows for the visualization of the conductivity distribution through the measurement of electrical parameters on the boundary, presents a potential solution for SHM. Existing studies applying EIT to CFRP concentrate on traditional linearization methods that rely on sensitivity matrix, which ignore challenges posed by non-uniform sensitivity. This paper introduces an EIT image reconstruction method that is independent of the sensitivity matrix, offering a novel way for rapid damage detection and real-time SHM. Furthermore, the domain shape transformation is achieved by conformal mapping. Simulations and experiments indicate that the sensitivity-matrix-independent EIT can avoid issues such as blurring in the central region and artifacts near the boundary caused by non-uniform distribution of the sensitivity in the CFRP damage detection.

Enhancing Wireless Sensor Network in Structural Health Monitoring through TCP/IP Socket Programming-Based Mimic Broadcasting: Experimental Validation

This paper presents the implementation of a synchronous Structural Health Monitoring (SHM) framework utilizing wireless, low-cost, and off-the-shelf components. Vibration-based condition monitoring plays a crucial role in assessing the reliability of structural systems by detecting damage through changes in vibration parameters. The adoption of low-cost Micro-Electro-Mechanical Systems (MEMS) sensors in Wireless Sensor Networks (WSNs) has gained traction, emphasizing the need for precise time synchronization to schedule wake-up times of multiple sensor nodes for data collection. To address this challenge, our proposed method introduces a TCP/IP socket programming-based mimic broadcasting mechanism and a scalable sensing network controlled by a central gateway, leveraging the Raspberry Pi Python platform. The system operates using Internet of Things (IoT) concepts and adopts a star topology, where a packet is transmitted from the gateway to initiate measurements simultaneously on multiple sensor nodes. The sensor node comprises a MEMS accelerometer, a real time clock DS3231 module and Raspberry Pi Zero 2W (RPi0-2W), while the gateway employs a Raspberry Pi 4 (RPi4). To ensure accurate time synchronization, all Pi0-2W nodes were configured as Network Time Protocol (NTP) clients, synchronizing with an RPi4 server using chrony, the reliable implementation of the NTP. Through experimental evaluations, the system demonstrates its effectiveness and reliability in achieving initial time synchronization. This study addresses the challenge of achieving precise time alignment between sensor nodes through the utilization of the Dynamic Time Wrapping (DTW) method for Frequency Domain Decomposition (FDD) applications. The contribution of this research significantly enhances the field by improving the accuracy and reliability of time-aligned measurements, with a specific focus on utilizing low-cost sensors. By developing a practical and cost-effective SHM framework, this work advances the accessibility and scalability of structural health monitoring solutions, facilitating more widespread adoption and implementation in various engineering applications

Structural Health Monitoring for Prefabricated Building Envelope under Stress Tests

This paper details the comprehensive activities conducted in a laboratory setting to assess the structural health monitoring (SHM) of prefabricated building envelopes. Integrating sensors into building components like curtain wall facades poses challenges but offers opportunities for monitoring structural health, requiring compliance with regulatory standards. The research investigates the possibility of defining a kit of conventional and multi-parameter sensors integrated within the building envelope to monitor its behavior during the performance test conducted. The kit of sensors also includes Fiber Optic Sensors for effectively monitoring building envelope behavior and structural integrity. In this context, the European project InComEss (H2020-GA862597) aims to define a stand-alone solution for SHM using Piezoelectric Energy Harvesting Systems (PE-EHS) for façade monitoring through FBG/FOS system. After analyzing the main façade structural stress, a series of FBGs, accelerometers, and force washers were integrated within a 1:1 scale façade prototype and tested in a laboratory following the test sequence parameters required by the curtain wall standard EN 13830. The data collected were analyzed with the aim of monitoring the façade behavior before and after the tests. The results show that the façade’s performance passed the assessing test criteria without reporting any damages. In addition, the outcomes demonstrated the effectiveness of the defined kit of multi-parameter sensors for the building envelope’s SHM.

A Subpixel Concrete Crack Measurement Method Based on the Partial Area Effect

To improve the accuracy of concrete crack measurement with a machine vision method in structural health monitoring and in technical status evaluation, a subpixel crack measurement method based on the partial area effect was proposed. (1) First, a pixelwise crack image segmentation method was established through a multi-step process of multi-threshold fusion and morphology operation, and a novel pixel degree crack width calculation method was developed with the extraction of the middle points, the center line and its normal, and the intersection of the center line normal and crack edges. (2) Then, a subpixel algorithm based on the partial area effect was introduced to locate vertical, horizontal, and oblique cracks in subpixel crack edges, and the subpixel crack width could be calculated along the crack center line pixelwise. (3) Finally, the proposed method was verified by indoor concrete beam crack measurement tests with a digital microscope, and the results show that the maximum relative errors of the subpixel width of the horizontal, vertical, and oblique straight cracks measured by the proposed method were 3.06%, 8.97%, and 5.16%, respectively. The absolute error of the crack length was less than 0.30 mm, and the measurement accuracy could reach 0.01 pixels. The subpixel crack measurement method provides a novel possible solution for structural health monitoring.

Nanocarbon-based sensors for the structural health monitoring of smart biocomposites.

Structural health monitoring (SHM) is a critical aspect of ensuring the safety and durability of smart biocomposite materials used as multifunctional materials. Smart biocomposites are composed of renewable or biodegradable materials and have emerged as eco-friendly alternatives of traditional non-biodegradable glass fiber-based composite materials. Although biocomposites exhibit fascinating properties and many desirable traits, real-time and early stage SHM is the most challenging issue to enable their long-term use. Smart biocomposites are integrated with sensors for in situ identification of the progress of damage and composite failure. The sensitivity of such smart biocomposites is a key functionality, which can be tuned by the introduction of an appropriate filler. In particular, nanocarbons hold promising potential to be incorporated in SHM applications of biocomposites. This review focused on the potential applications of nanocarbons in SHM of biocomposites. The aspects related to fabrication techniques and working mechanism of sensors are comprehensively discussed. Furthermore, their unique mechanical and electrical properties and sustainable nature ensure seamless integration into biocomposites, allowing for real-time monitoring without compromising the material's properties. These sensors offer multi-parameter sensing capabilities, such as strain, pressure, humidity, temperature, and chemical exposure, allowing a comprehensive assessment of biocomposite health. Additionally, their durability and longevity in harsh conditions, along with wireless connectivity options, provide cost-effective and sustainable SHM solutions. As research in this field advances, ongoing efforts seek to enhance the sensitivity and selectivity of these sensors, optimizing their performance for real-world applications. This review highlights the significant advances, ongoing efforts to enhance the sensitivity and selectivity, and performance optimization of nanocarbon-based sensors along with their working mechanism in the field of SHM for smart biocomposites. The key challenges and future research perspectives facing the conversion of nanocarbons to smart biocomposites are also displayed.

Acoustic emission characteristics of stainless steel reinforced geopolymer coral concrete beams under four-point bending

The fabrication of geopolymer coral aggregate concrete beams (GCACB) can effectively address the challenges associated with constrained concrete raw materials and structural erosion in island construction. Acoustic emission technology (AE) presents an appealing solution for non-destructive evaluation and structural health monitoring. In this study, 6 specimens were conducted to examine the flexural behavior of GCACB accounting for variation in the reinforcement ratios under the four-point bending test, simultaneously recording the corresponding AE data. Then, the effect of the AE parameters on crack development was analyzed. By utilizing the GMM algorithm, the crack pattern classification was investigated. Finally, the GCACB damage was evaluated based on two different AE b-value analysis methods. The findings indicate that the cracks within GCACB predominantly occur along the coral coarse aggregate with low strength and high brittleness. The presence of reinforcement ratio has an enhancement on both the flexural capacity and deformation capacity of GCACB while impeding the crack width. It seems that a positive correlation between increasing crack damage and cumulative hits and AE energy occurs. Based on the enhanced GMM model, the cracks are categorized into tensile crack, shear crack, and mixed crack, with tensile crack predominating. A probabilistic division line is established for the RA-AF relationship that progressively leans towards the AF axis as the reinforcement ratio increases. In contrast to the b-value obtained from the GBR method, the Aki method demonstrates enhanced predictive capability for GCACB damage.

Experimental Study on Monitoring Damage Progression of Basalt-FRP Reinforced Concrete Slabs Using Acoustic Emission and Machine Learning.

Basalt fiber-reinforced polymer (BFRP) reinforced concrete is a new alternative to conventional steel-reinforced concrete due to its high tensile strength and corrosion resistance characteristics. However, as BFRP is a brittle material, unexpected failure of concrete structures reinforced with BFRP may occur. In this study, the damage initiation and progression of BFRP-reinforced concrete slabs were monitored using the acoustic emission (AE) method as a structural health monitoring (SHM) solution. Two simply supported slabs were instrumented with an array of AE sensors in addition to a high-resolution camera, strain, and displacement sensors and then loaded until failure. The dominant damage mechanism was concrete cracking due to the over-reinforced design and adequate BFRP bar-concrete bonding. The AE method was evaluated in terms of identifying the damage initiation, progression from tensile to shear cracks, and the evolution of crack width. Unsupervised machine learning was applied to the AE data obtained from the first slab testing to develop the clusters of the damage mechanisms. The cluster results were validated using the k-means supervised learning model applied to the data obtained from the second slab. The accuracy of the K-NN model trained on the first slab was 99.2% in predicting three clusters (tensile crack, shear crack, and noise). Due to the limitation of a single indicator to characterize complex damage properties, a Statistical SHapley Additive exPlanation (SHAP) analysis was conducted to quantify the contribution of each AE feature to crack width. Based on the SHAP analysis, the AE duration had the highest correlation with the crack width. The cumulative duration of the AE sensor near the crack had close to 100% accuracy to track the crack width. It was concluded that the AE sensors positioned at the mid-span of slabs can be used as an effective SHM solution to monitor the initiation of tensile cracks, sudden changes in structural response due to major damage, damage evolution from tensile to shear cracks, and the progression of crack width.

Multi-Damage Detection in Composite Space Structures via Deep Learning.

The diagnostics of environmentally induced damages in composite structures plays a critical role for ensuring the operational safety of space platforms. Recently, spacecraft have been equipped with lightweight and very large substructures, such as antennas and solar panels, to meet the performance demands of modern payloads and scientific instruments. Due to their large surface, these components are more susceptible to impacts from orbital debris compared to other satellite locations. However, the detection of debris-induced damages still proves challenging in large structures due to minimal alterations in the spacecraft global dynamics and calls for advanced structural health monitoring solutions. To address this issue, a data-driven methodology using Long Short-Term Memory (LSTM) networks is applied here to the case of damaged solar arrays. Finite element models of the solar panels are used to reproduce damage locations, which are selected based on the most critical risk areas in the structures. The modal parameters of the healthy and damaged arrays are extracted to build the governing equations of the flexible spacecraft. Standard attitude manoeuvres are simulated to generate two datasets, one including local accelerations and the other consisting of piezoelectric voltages, both measured in specific locations of the structure. The LSTM architecture is then trained by associating each sensed time series with the corresponding damage label. The performance of the deep learning approach is assessed, and a comparison is presented between the accuracy of the two distinct sets of sensors: accelerometers and piezoelectric patches. In both cases, the framework proved effective in promptly identifying the location of damaged elements within limited measured time samples.

Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications

Structural deterioration is a primary long-term concern resulting from material wear and tear, events, solicitations, and disasters that can progressively compromise the integrity of a cement-based structure until it suddenly collapses, becoming a potential and latent danger to the public. For many years, manual visual inspection has been the only viable structural health monitoring (SHM) solution. Technological advances have led to the development of sensors and devices suitable for the early detection of changes in structures and materials using automated or semi-automated approaches. Recently, solutions based on computer vision, imaging, and video signal analysis have gained momentum in SHM due to increased processing and storage performance, the ability to easily monitor inaccessible areas (e.g., through drones and robots), and recent progress in artificial intelligence fueling automated recognition and classification processes. This paper summarizes the most recent studies (2018–2022) that have proposed solutions for the SHM of infrastructures based on optical devices, computer vision, and image processing approaches. The preliminary analysis revealed an initial subdivision into two macro-categories: studies that implemented vision systems and studies that accessed image datasets. Each study was then analyzed in more detail to present a qualitative description related to the target structures, type of monitoring, instrumentation and data source, methodological approach, and main results, thus providing a more comprehensive overview of the recent applications in SHM and facilitating comparisons between the studies.

High-Sensitivity RFID Sensor for Structural Health Monitoring.

Structural health monitoring (SHM) is crucial for ensuring operational safety in applications like pipelines, tanks, aircraft, ships, and vehicles. Traditional embedded sensors have limitations due to expense and potential structural damage. A novel technology using radio frequency identification devices (RFID) offers wireless transmission of highly sensitive strain measurement data. The system features a thin, flexible sensor based on an inductance-capacitance (LC) circuit with a parallel-plate capacitance sensing unit. By incorporating tailored cracks in the capacitor electrodes, the sensor's capacitor electrodes become highly piezoresistive, modifying electromagnetic wave penetration. This unconventional change in capacitance shifts the resonance frequency, resulting in a wireless strain sensor with a gauge factor of 50 for strains under 1%. The frequency shift is passively detected through an external readout system using simple frequency sweeping. This wire-free, power-free design allows easy integration into composites without compromising structural integrity. Experimental results demonstrate the cracked wireless strain sensor's ability to detect small strains within composites. This technology offers a cost-effective, non-destructive solution for accurate structural health monitoring.

Guided wave propagation and skew effects in anisotropic carbon fiber reinforced laminates

Guided ultrasonic waves provide a promising structural health monitoring (SHM) solution for composite structures as they are able to propagate relatively long distances with low attenuation. However, the material anisotropy results in directionally dependent phase and group velocities, in addition to energy focusing, wave skewing, and beam spreading phenomena. These effects could lead to inaccurate damage localization if not accounted for. In this contribution, the guided wave propagation behavior (A0 mode) for a highly anisotropic, unidirectional carbon fiber reinforced polymer laminate is systematically investigated through both finite element analysis and non-contact laser measurements and compared to theoretical predictions. The directional dependency of phase and group velocity measured for a point and line source shows good agreement with theoretical predictions, once a correction for wave skew effects is applied. Wave skew angles were evaluated from the experimental and numerical wave propagation in multiple directions and matched theoretical predictions based on the phase slowness curve. Significant guided wave beam spreading from a line source was observed and quantified from both experiments and simulations and compared with theoretical predictions using the anisotropy factor. The impact of anisotropic guided wave propagation behavior on SHM is discussed.

Prospects on ultrasound measurement techniques with optical fibers

Ultrasound sensors have been widely used in medical imaging, as well as structural health monitoring (SHM) and non-destructive testing (NDT) in civil and mechanical structures. Covering entire structures and imaging large areas requires multiplexing of many ultrasound sensors with single readout instrument, which can be difficult for traditional piezoelectric transducers. Optical fiber-based sensors offer numerous advantages such as being lightweight, small, the ability to be embedded, immunity to electro-magnetic interference, and the ability to be multiplexed and distributed ultrasound sensors. Fiber ultrasound sensors are regarded as an ideal sensing solution for SHM and NDT, and even most recently for medical imaging due to its broadband ultrasound response and distributed capability. Micro and nanofibers are made smaller than telecom fibers using a wider selection of sensing materials with higher bending capability, which makes them ideal for high frequency (hundreds of MHz) ultrasound detection of micrometer cracks and imaging biological tissues. New optical materials and fabrication techniques are shaping the future with exceptionally small ultrasound sensors and actuators, extending the range of applications in SHM, NDT and medical imaging with higher accuracy and better precision over larger areas.

Design of a smart lime mortar with conductive micro and nano fillers for structural health monitoring

Structural health monitoring is an essential tool for assessing the performance of buildings and infrastructure, especially after critical events or the application of structural interventions. When dealing with architectural heritage structures, both structural health monitoring instrumentation and intervention materials need to be as inconspicuous and unintrusive as possible, both in terms of mechanical compatibility and aesthetics. Therefore, the design of smart sensors based on construction materials used in conservation engineering promises to provide an acceptable integrated structural health monitoring and upgrading solution for historic structures. In this paper an experimental investigation of smart intervention materials for historic masonry structures is presented. The materials consisted of natural hydraulic lime mortars modified through the inclusion of electrically conductive micro- and nanofillers: graphite, carbon nanotubes and carbon microfibres. The fillers provide multifunctionality to the matrix material based on an enhancement of its piezoresistive characteristics. Further, they result in an improvement of the mechanical properties of the intervention material without compromising its mechanical and chemical compatibility with the original structure. The resulting materials were evaluated based on mechanical property improvement, piezoresistivity enhancement and ease of production.

Electrically conductive and 3D-printable copolymer/MWCNT nanocomposites for strain sensing

Structural health monitoring (SHM) of safety-critical composite components is essential to ensure mechanical stasis and detect local damage before it can produce global failure. SHM technologies must also adapt to ever-evolving materials and geometries, but traditional piezoresistive strain sensors lack the ability for end-user customization and modifications throughout service-life. Additive manufacturing via fused filament fabrication (FFF) provides a practical pathway to overcome such sensor shortcomings. However, the electrical conductivity of conventional polymer feedstock is not sufficient for accurate strain measurements without compromise to melt viscosity and thus printability. Here we report the development of a new 3D-printable and electrically responsive nanocomposite by melt-mixing poly(ethylene-co-methacrylic acid) (EMAA) with multi-walled carbon nanotubes (MWCNT). Bulk electrical conductivity of 43.9 S m−1 is achieved at only 5 wt% loading – higher than comparable materials – where the nano-dispersion heterogeneity of MWCNT in EMAA is linked to the favorable conductivity while retaining molten flowability. FFF is employed to print thin (150μm) serpentine strain sensors onto the surface of a glass fiber-reinforced polymer composite, which exhibit strong adhesion and accurate piezoresistive sensing under cyclic flexural loading. Twenty consecutive cycles with converged sensor readings (i.e., < 1% variation in measured resistance) demonstrates reliable performance across a relevant service strain range (0.4 – 0.8 %) for such fiber-composites. This rapid fabrication and transferable sensing strategy, suitable for new and existing structures, thus provides a crosscutting SHM solution.

Towards in-situ acoustic emission-based health monitoring in bio-based composites structures: Does embedment of sensors affect the mechanical behaviour of flax/epoxy laminates?

Monitoring integrity of operating structures has become a crucial need in industrial applications while motivation for more sustainable materials pushes forward the development of natural fibre composites (NFC). The complexity and variability of their failure behaviour and mechanical properties still limit the exploitation of their full potential. Acoustic Emission (AE) has shown promising results to predict remaining service life of structures, allowing identification, localisation and assessment of damages in composite materials. Thus, embedment of AE sensors could be a reliable solution for real-time Structural Health Monitoring (SHM). This paper aims to quantify the effect of embedded millimetre-sized metallic inserts mimicking sensor integration on material integrity under tensile and bending loads by means of Digital Image Correlation , AE, and infrared thermography . Results show a limited effect on monotonic tensile properties , driven by the size of the sensor, while highlighting an increase of strain and stress at failure under three-point bending. Tensile-tensile fatigue resistance is only slightly affected by the embedment of sensors, with a small increase in the S–N curve slope. Overall results suggest that the embedment of miniature sensors in NFC is possible and could be a suitable solution for damage assessment and health monitoring in such sustainable structures.

Thermographic data analytics-based damage characterization in a large-scale composite structure under cyclic loading

Large-scale composite structures such as aircraft wings and wind turbine blades undergo cyclic loading in operation. In-service damage often generates excessive heat due to material frictions and it could be detected by thermography. This study develops a methodology to quantitatively analyze such structural damage based on thermographic data analytics. A full-scale composite wind turbine blade is inspected using passive thermography when it is subject to cyclic loads in laboratory. The damage region is identified from thermographic images and it is tracked automatically using image processing. The damage region is subsequently characterized on both overall and detailed levels. The change of the damage status versus fatigue cycle number is analyzed and the information regarding the growth of the damage area and the damage severity is provided. The initiation and the progress of damage are investigated based on the temperature and the enthalpy change in the damage region. This study provides a viable solution for efficient structural health monitoring and damage prognosis of large-scale composite structures under cyclic loading.

From Cognitive Bias Toward Advanced Computational Intelligence for Smart Infrastructure Monitoring.

Visual inspections have been typically used in condition assessment of infrastructure. However, they are based on human judgment and their interpretation of data can differ from acquired results. In psychology, this difference is called cognitive bias which directly affects Structural Health Monitoring (SHM)-based decision making. Besides, the confusion between condition state and safety of a bridge is another example of cognitive bias in bridge monitoring. Therefore, integrated computer-based approaches as powerful tools can be significantly applied in SHM systems. This paper explores the relationship between the use of advanced computational intelligence and the development of SHM solutions through conducting an infrastructure monitoring methodology. Artificial Intelligence (AI)-based algorithms, i.e., Artificial Neural Network (ANN), hybrid ANN-based Imperial Competitive Algorithm, and hybrid ANN-based Genetic Algorithm, are developed for damage assessment using a lab-scale composite bridge deck structure. Based on the comparison of the results, the employed evolutionary algorithms could improve the prediction error of the pre-developed network by enhancing the learning procedure of the ANN.

Cross tension and compression loading and large-scale testing of strain and damage sensing smart concrete

Multifunctional self-sensing smart concrete can provide a structural health monitoring (SHM) solution that is robust, reliable, and low-cost. Smart concrete, which includes coarse aggregates with 15 mm size, brass fibers, silica fume, superplasticizer and water, has been developed as a promising multifunctional material. The normal and cross compression and split tensile tests were conducted on 75 mm cube samples; large scale compression test was conducted on 15 × 15 × 30 cm prism; large scale bending tests were applied to prisms with sizes 15 × 15 × 75 cm and 15 × 30 × 150 cm. The normal and cross compressive strain gage factors were 54 and 59 while linearities were 6.6% and 7%, respectively. Normal and cross tensile strain gage factors were 3.0 and 2.9 while corresponding linearities were 6.6% and 13%. As the sample size increased, the electrical resistivity increased and strain sensitivity decreased due to obstruction of electrons by aggregates that revealed a “size effect” for piezoresistivity of smart concrete. Large scale bending test results verified the piezoresistivity of smart concrete while crack formation and propagation increased the electrical resistance dramatically. Smart concrete can be utilized to monitor both strain and damage.