Crack Monitoring Research Articles

Mock-up performance evaluation study for crack reduction of blast furnace slag powder concrete mixed with expansive and swelling admixtures

In this study, the drying shrinkage and crack reduction characteristics of blast furnace slag concrete mixed with expansive and swelling admixtures were investigated. Basic performance experiments were conducted using different mixtures of ground granulated blast-furnace slag (GGBS), with calcium sulfoaluminate as the expansive admixture and bentonite and hydroxypropyl methyl cellulose (HPMC) as swelling admixtures. Bentonite outperformed HPMC as a swelling admixture. Specimens were then prepared for mock-up tests to evaluate the drying shrinkage of blast furnace slag concrete with different combinations of bentonite, a hardening accelerator, and a self-healing agent. The addition of bentonite and calcium phosphate as a self-healing agent in small quantities reduced the drying shrinkage of the specimens, thereby reducing cracks. The cement mixture composed of 30% GGBS, 1% bentonite, and 1% calcium phosphate (30-E1-I1) showed the optimal performance among the specimens. Further, crack monitoring was performed in concrete made with ordinary Portland cement and optimal mixture 30-E1-I1. No cracks were observed for the optimal mixture. This shows that GGBS concrete can be used in practical and field applications, subject to mid- and long-term tests for cracking.

Cause, Stability Analysis, and Monitoring of Cracks in the Gate Storehouse of a Flood Diversion Sluice

The cracks in a gate storehouse for a flood-diversion sluice of the Wangfuzhou Water Conservancy Project were studied to diagnose the structural safety. The study was based on an integrated method that involved the analysis of the cause of cracks, an assessment of the crack stability, online safety monitoring and diagnosis, and real-time monitoring and control. The results demonstrated that two dominant factors leading to cracks were the non-uniform settlement of the foundation of this gate storehouse structure combined with temperature drops in winter. By comparing crack-tip stress intensity factors at different cracking depths with concrete fracture toughness, the results showed that cracks at the gate storehouse would not continue to propagate, although some time-dependent components separated from the measuring point did not converge but tended to be closed. Therefore, the diagnosis suggests that the cracks at the gate storehouse are currently in a stable state. Additionally, the crack opening index estimated using the small probability method can be used for future real-time monitoring of crack behavior.

Characterization of Fatigue Damage in Hadfield Steel Using Acoustic Emission and Machine Learning-Based Methods.

Structural health monitoring (SHM) of fatigue cracks is essential for ensuring the safe operation of engineering equipment. The acoustic emission (AE) technique is one of the SHM techniques that is capable of monitoring fatigue-crack growth (FCG) in real time. In this study, fatigue-damage evolution of Hadfield steel was characterized using acoustic emission (AE) and machine learning-based methods. The AE signals generated from the entire fatigue-load process were acquired and correlated with fatigue-damage evolution. The AE-source mechanisms were discussed based on waveform characteristics and bispectrum analysis. Moreover, multiple machine learning algorithms were used to classify fatigue sub-stages, and the results show the effectiveness of classification of fatigue sub-stages using machine learning algorithms. The novelty of this research lies in the use of machine learning algorithms for the classification of fatigue sub-stages, unlike the existing methodology, which requires prior knowledge of AE-loading history and calculation of ∆K.

Stress ratio effect on fatigue crack growth assessment via thermoelastic stress analysis

The fatigue performance of welded joint is strongly dependent on welding residual stresses as well as the local geometry of the weld toes. To ensure the structural integrity over time of such components, fine predictive models must be proposed together with efficient experimental methods for their assessment.This study introduces a model based on linear elastic fracture mechanics to forecast fatigue life in service of high strength steel welded joints. This model considers the effect of the local stress ratio (dependent on stabilized residual stresses) on the crack growth rate through the definition of an effective stress range. The use of Thermoelastic Stress Analysis (TSA) is used to perform in-situ monitoring of fatigue cracks on welded joints to experimentally characterize the crack-closure phenomenon at different load ratios. Indeed, under adiabatic conditions, the temperature's first harmonic at the surface of a structure submitted to cyclic loading is proportional to the stress tensor's first invariant's amplitude. As a result, the presence of a surface crack and the non-linearity induced by the alterative opening and closing within one loading cycle can be observed in the immediate temperature response. This phenomenon is used to evaluate a crack-opening rate that is related to the fatigue life of the welded joints.

Supersensitive visual pressure sensor based on the exciton luminescence of a perovskite material.

Accurate, rapid, and remote detection of pressure, one of the fundamental physical parameters, is vital for scientific, industrial, and daily life purposes. However, due to the limited sensitivity of luminescent manometers, the optical pressure monitoring has been applied mainly in scientific studies. Here, we developed the first supersensitive optical pressure sensor based on the exciton-type luminescence of the Bi3+-doped, double perovskite material Cs2Ag0.6Na0.4InCl6. The designed luminescent manometer exhibits an extremely high sensitivity, i.e. dλ/dp = 112 nm GPa-1. It also allows multi-parameter sensing, using both blue-shift and rarely observed band narrowing with pressure. Importantly, this material has small temperature dependence for the manometric parameter used, i.e. spectral shift, allowing detection under extreme pressure and temperature conditions. The developed sensor operates in the visible range, and its emission shifts from orange to blue with pressure. This approach allowed us to demonstrate the real-world application of this sensor in detecting small changes in pressure with a designed uniaxial pressure device, with unprecedented resolution of the order of a few bars, demonstrating the technological potential of this sensor for remote, online monitoring of cracks and strains in heavy construction facilities.

Monitoring of a prestressed bridge girder with integrated distributed fiber optic sensors

Several research projects carry out experiments on a newly built 45 m long research bridge, called openLAB. This contribution showcases the synergetic collaboration of the two research projects IDA-KI and smart_tendon in a joint experimental investigation on one of the openLAB’s 15 m long precast and prestressed girders, which was tested in a 4-point bending test prior to its installation into the bridge. The girder was equipped with distributed fiber optic sensors (DFOS) prior to casting, as DFOS provide detailed insights into the structure’s inner with unprecedented spatial resolution. The suitability of different installation methods for structural health monitoring applications from the beginning of the structures’ existence was studied. DFOS were either embedded in the concrete matrix, glued to the longitudinal reinforcement, included in prestressing strands in direct bond or inside strands of a post tensioned tendon. Robust DFOS with a monolithic cross-section, integrated directly into the concrete matrix, enable reliable crack monitoring and anomaly detection. DFOS integrated into tendons allow for efficient construction progress without the need for additional trades. Strain profiles in the linear elastic state can be accurately mapped. A transition to the cracked state is indicated by a disproportionate strain increase.

Microseismic crack monitoring technology

Microseismic crack monitoring technology

Monitoring of fracture processes in reinforced concrete beams with DFOS: feasibility study

In this paper, we present the application of distributed fibre optic sensors (DFOS) for the detection of bending and shear cracks in reinforced concrete beam specimens loaded in three- and four-point bending in lab conditions. The main goal of this study is to prove the feasibility for future application of DFOS for monitoring of existing concrete structures such as bridges or tunnels. Each specimen was equipped with more optical fibres: most of them were externally attached in the grooves at the outer surface of the specimens and some of them were inside the specimens. Specimen geometries, arrangement of reinforcement bars and loading schemes of the specimens were determined by non-linear FEM before the tests, so that either dominant bending or dominant shear cracks appear during the loading. Some specimens were loaded step-by-step and some of them by continuous loading and the results were compared afterwards. The results showed, that DFOS can ensure exact crack monitoring in all loading stages of the reinforced concrete specimens, which is a good basis for future applications for monitoring of structures in the engineering practice.

Welding under service conditions – Monitoring, repair strategy and rehabilitation

Steel structures, such as bridges, must be continuously maintained. Many steel bridges are subject to various types of damage, such as cracking, due to increasing traffic loads, design and construction deficiencies. To address these problems, it is necessary to perform maintenance, typically by removing these cracks by re-welding. During this repair process, the affected bridge needs to be closed to traffic in order to prevent crack movement during the welding. Due to this closure, traffic is diverted to alternate routes, increasing the traffic loads on those routes, which, unfortunately leads to additional damage and subsequent welding work and costs for these alternate structures. Therefore, the primary goal is to establish a method that allows welding work on steel structures while traffic is running. In order to achieve this, measurements were initially taken at a representative steel bridge in order to ascertain the parameters of amplitude and frequency associated with the opening of the gap for the purposes of conducting welding tests under cyclic loading conditions. Subsequently the weld quality and the fatigue performance were quantified. The weld quality and the fatigue strength met the standard requirements, allowing for welding under service conditions, subject to certain conditions. In this paper, it will be presented which crack monitoring techniques can be used to quantify the gap opening. A repair strategy for butt joints is presented, along with data on the fatigue strengths achieved. It is also shown how this repair process can be documented in a digital twin framework.

Influence of high temperature during tensile test for stainless steel using acoustic emission

AbstractThis paper explores the application of acoustic emission (AE) monitoring in mechanical and materials studies, specifically focusing on the detection of plastic deformation, fracture, and crack growth. However, the influence of high temperature on AE signals and its effects on mechanical properties are not fully understood due to the limitations of sensors in high‐temperature environments. To address this gap, the study investigates the impact of high temperatures on the mechanical properties of stainless steel SS304 using AE techniques during tensile testing. Two temperature conditions were tested: 600 and 700°C. AE sensors connected to specimens captured AE activity using a designated waveguide, and a Continuous Creep Monitoring Instrument (CCMi) recorded the responses. Sensitivity testing using the pencil‐lead break (PLB) technique ensured the sensor's capability. The proposed frequency‐based analysis method, fast Fourier transform amplitude (FFTA) analysis, was employed to effectively analyze the AE signals. The results revealed significant AE features and frequency distributions associated with elastic deformation, plastic deformation, and fracture regions. These findings establish a foundation for utilizing AE techniques in crack detection and monitoring under high‐temperature loading, contributing to equipment reliability and structural integrity.

Structural health monitoring of stainless-steel nuclear fuel storage canister using acoustic emission

Nuclear power generation constitutes a significant component of the energy infrastructure within the United States. The dry cask storage system canister, used for storing highly radioactive spent fuel, demands vigilant structural health monitoring. This paper explores the feasibility of monitoring of stress corrosion cracking in large-scale dry cask storage system canisters using acoustic emission sensors. In this paper, a 304H stainless steel plate, reflecting a typical canister, was subjected to conditions inducing stress corrosion cracking by exposure to a potassium tetrathionate solution and tensile stresses. Analysis of the captured acoustic emission signals showed that the sensors were able to detect this corrosion even at considerable distances from the cracks. Moreover, a finite element model was designed to simulate the acoustic emission signals, thus providing a preliminary understanding of the signal profiles at different sensor locations and potentially providing guidance on the optimal placement of the sensors during field monitoring.

Semi-automatic 3D crack map generation and width evaluation for structural monitoring of reinforced concrete structures

Bridge inspection is a time-consuming, expensive, but indispensable task. In this work, a new semi-automatic workflow for a concrete bridge condition assessment system is developed and discussed. The workflow consists of three main parts merged in the new methodology. The elements are the data acquisition with cameras, the automated damage detection and localization using a neural network, and the resulting engineering condition assessment. Furthermore, a CAD model serves as a base for the later calculations for the condition assessment. Camera images are used for both sub-millimeter crack detection using semantic segmentation by an artificial neural network and a crack localization based on a combination of a photogrammetric workflow including structure from motion (SfM) and the projection as imprinted points directly onto the as-planned CAD mesh. Moreover, an approach for crack width derivation is given. The captured crack width, crack position, and the date of detection represent the input values for subsequent crack monitoring. Thereby, this new concept is proposed as an essential step towards a time-efficient and objective life-cycle assessment of reinforced concrete structures.

Crack monitoring of tension members with distributed fiber optic sensor considering substrate strain redistribution and coating/fiber interfacial slip

An analytical model is proposed to describe the strain transfer from crack opening displacement (COD) of a tension member substrate to the distributed fiber optic sensor (DFOS), considering the DFOS/fiber coating interfacial slip and strain redistribution of the cracked structural substrate. The DFOS/fiber coating interfacial bond-slip relation is simplified by using a three-phase linear model, and the analytical expression of the substrate strain distribution of a tensile plate with different depth of crack is derived. The effect of the COD and crack depth on the strain distribution of the DFOS is obtained analytically, and tensile tests were also conducted on aluminum plates with different depth of precut crack to validate the developed model. Analytical results showed that the strain distribution of the DFOS is dependent on the COD and the crack depth, and the effect of the COD on the strain distribution of the DFOS is more significant than the crack depth. The analytical results of the developed model are in good agreement with those of the tensile tests.

Crack monitoring on concrete structures with distributed fiber optic sensors—Toward automated data evaluation and assessment

AbstractThe ability to measure strains quasi‐continuously with high spatial resolution makes distributed fiber optic sensing a promising technology for structural health monitoring as it allows to locate and measure damages in concrete structures, such as cracks. Depending on whether the distributed fiber optic sensor (DFOS) is embedded into the concrete matrix or bonded to the reinforcement, different approaches for crack width calculation exist. The high spatial resolution of DFOS quickly leads to a large amount of data, especially with time continuous monitoring. Scalable, automated analysis approaches are required to handle such big data and to derive a gain in knowledge from the measurements. Thus, in a first step, the Python framework fosanalysis is presented and made available to other researchers or monitoring specialists as free software. The most important input parameters for crack width calculation are discussed for concrete strain and steel strain DFOS. Accurate crack monitoring for a 4 m long reinforced concrete beam is demonstrated by using fosanalysis. The calculated crack widths are in good agreement with digital image correlation measurements.

Insight into the crack characteristics and mechanisms of retrogressive slope failures: A large-scale model test

Cracks in the crown area are one of the important indicators in the instability of the landslide headscarp, potentially offering early warnings for landslide hazards. Due to the complex causes of crack formation and numerous influencing factors, the understanding of crack development characteristics is still insufficient. In this paper, a large-scale soil slope model test is carried out through a large tilting platform to simulate the whole process of retrogressive slope failures including the first-time failure and the enlargement of the failure. The characteristics of crack development, the relations between internal slope parameters (e.g., internal strain and earth pressure) and cracks, and the evolutionary mechanisms of retrogressive failures are focused. The model test is monitored by high-tech equipment, including optical fibres, pressure gauges, and cameras. The results show that crack developments are divided into four stages based on crack location, quantity, and size. Optical fibre monitoring shows that the areas of tension crack development fall within the negative strain area in the slope model. In addition, the failure mode of the retrogressive landslides can be summarized in three stages: a deformation stage with sparse local cracks, a destabilizing stage with main cracks, and a retrogressive failure stage with dense secondary cracks. This study offers preliminary insights into the relations between crack development and the mechanisms in soil multiple retrogressive failures, which can be beneficial for better identifying and interpreting landslides at sites through crack monitoring.

Self-powered retractable reel sensor for crack monitoring and warning in civil infrastructures

Crack occurrence and propagation are among the critical factors that affect the performance and lifespan of civil infrastructure such as bridges, buildings, etc. Therefore, numerous crack detection and characterization techniques have been developed over the past decades. In this study, we proposed the self-powered with the merits of cost-affordable, high resolution, and wide monitoring range to monitor small cracks in infrastructure. The self-powered retractable reel sensor includes two triboelectric nanogenerator (TENG) sensing units for low and high speed fracture monitoring, respectively. By using the principle of retractable reel structure, the linear motion of the crack is converted into the rotation of the TENG. Through peak extraction and pulse counting, the testing of crack velocity and displacement was realized simultaneously. A real resolution of 0.222 mm are achieved through the structural design and electrical performance characterization. With the help of the LabVIEW platform and multi-channel data acquisition program, its application in crack monitoring and early warning is successfully demonstrated. This work presents a novel self-powered sensor for crack monitoring and early warning, such a strategy is potentially available for distributed sensor construction towards structural health monitoring (SHM) of the infrastructure.

Development of an Artificial Intelligence based Railway Crack Detection and Monitoring System using Internet of Things

This study suggests the concept of crack locating robot vehicle in the railway rails. India railways participates an important posture in asset with the necessary transportation to preserve needs of a rapidly emergent financial system. India now has the world's fourth-largest railway network. Therefore, travelling by train is risky. It does not work with the existing railway control system in India. Thus, robust, well-organized, and reliable new technology is required for both fracture detection in railway track and identification of objector items. This study proposes and clarifies a robust monitoring system to meet the limitations of the current rail surveillance system for detecting fractures of the railway tracks. Therefore, robust, well-organized, and reliable new technology is required for both fracture detection in railway track and the identification of objects or items. This study proposes and clarifies a robust monitoring system to meet the limitations of the current rail surveillance system for detecting fractures of the railway tracks. The goal of this study is to build and create an IoT-enabled robot that uses artificial intelligence (AI) to report to a railway controller any information it gathers concerning broken or cracked track. To solve this issue, we will be sending all data over wireless communication based devices and developing a corresponding application..

Pure (I and II) and mixed-mode (I+II) fracture characterisation of unidirectional AS4/Low-melt-PolyaryletherketoneTM polymer composites

This paper reports an experimental and numerical study on the mixed-mode I+II fracture characterisation of unidirectional AS4/low-melt-Polyaryletherketone (AS4/LMPAEKTM) composites. To address the fracture law characterising the material mixed-mode fracture behaviour, a suitable approach is used, simply applying two different loading conditions regarding five mechanical tests and specimen configurations. Scoping to characterise fracture under pure mode I loading and under mixed-mode I+II loading with mode I predominance, the peel test loading condition is used, respectively, for symmetric and asymmetric double-cantilever beam tests (DCB and ADCB). The three-point bending loading condition is used in order to characterise fracture under pure mode II loading by end-notch flexure test (ENF) and mixed-mode I+II with moderate and predominant mode II, respecting symmetric and asymmetric single-leg bending tests (SLB and ASLB). To diminish the influence of crack monitoring ambiguity on the crack length analysis during experiments, data reduction schemes based on the compliance-based beam method (CBBM) were used. Additionally, numerical finite element analyses were conducted in order to validate the data reduction method and the mode partition strategy based on cohesive zone modelling (CZM). Overall, a good agreement between numerical and experimental results was obtained, providing an adequate identification of the energy fracture criterion.

Antenna Sensor Array Design for Structural Strain and Crack Monitoring

Antenna Sensor Array Design for Structural Strain and Crack Monitoring

All-fiber photoacoustic system for large-area nondestructive testing

Nondestructive testing (NDT) is of paramount importance in ensuring the safe operation of equipment. Among various NDT techniques, ultrasonic NDT has garnered widespread attention due to its high sensitivity, fast speed, and accurate defect location. Photoacoustic NDT, a burgeoning field in ultrasonic NDT, is particularly attractive due to its immunity to electromagnetic interference. However, existing photoacoustic NDT systems suffer from inadequate excitation intensity and complex ultrasonic signal characteristics, impeding large-area NDT and accurate crack visualization. In this study, we present an all-fiber photoacoustic system for large-area NDT. To address the issues, we have developed a photoacoustic generator unit that can be optimized and controlled to generate stronger ultrasonic signals. Furthermore, we have employed mode decomposition to simplify the detected ultrasonic signals by mitigating the acoustic impedance mismatch-induced mode mixing problem in the system. As a result, the technology allows for large-area crack monitoring of up to 50*50 cm2 with an improved resolution of 1 mm. The present technology paves the way for high-resolution equipment crack monitoring with substantially enhanced accuracy in various environments.