Damage In Concrete Structures Research Articles

Continuous damage of concrete structures due to reinforcement corrosion: A micromechanical and multi-physics based analysis

Deterioration of concrete and composite structures due to the corrosion of embedded steel components (rebar, structural steel components, and steel fiber, etc.) is a self-accelerating process involving multiple chemophysical processes that occur concurrently in the heterogeneous structure of concrete. The multiple-inclusion matrix of concrete and the multi-chemophysics nature of steel corrosion synergistically challenge an accurate analysis and evaluation of the deterioration process using homogeneity-based continuum mechanics. This study presents a three-dimensional micromechanical model developed for quantifying the continuous deterioration of concrete by steel corrosion. Continuous damage theory of solid-state materials was applied to a steel-concrete sample that consists of mortar, aggregate, and steel phases as were reconstructed from high-resolution X-ray CT images. With due consideration of the variations in environmental temperature and moisture, the complex chemophysical processes involved in corrosion-induced deterioration of concrete were solved concurrently using finite element analysis. Results of the study indicate that capillary suction in a variably saturated concrete plays an important role in steel corrosion and that removal of chloride by an externally applied electrical field can effectively mitigate steel corrosion and the incurred concrete deterioration.

Shear strengthening of damaged reinforced concrete beams with iron-based shape memory alloy (Fe-SMA) strips: numerical and parametric analysis

Shape memory alloys (SMAs) are metallic materials that are characterized by their ability to restore their original shape after large deformation when activated by heating. This unique property renders SMAs appealing for various civil engineering applications. Iron-based SMAs (Fe-SMAs), including alloys like Fe–Mn–Si, stand out due to their cost-effectiveness and high strength. The primary focus of this research lies in the computational modeling of Fe-SMA strips utilized to reinforce damaged concrete structures. To achieve this, details from an experimental test are leveraged for the computational simulation of real-scale reinforced concrete beams that were first loaded to some level of damage, then released and strengthened, and subsequently retested. The strengthening approach involves the application of external Fe-SMA strips wrapping around the beams. This paper presents an original computational modeling setup that incorporates a switch option for the Fe-SMA material. This feature enables one to use a single simulation platform for the whole process. The significance of this method originates from its capacity to ensure a robust analysis that includes all simulation steps-testing unstrengthened beams, installing and heating Fe-SMA strips, and testing both damaged and strengthened beams—in a single, multi-step analysis. The computational simulation results were compared with the outcomes of the experimental test, revealing an acceptable level of agreement. The findings indicate a substantial increase in both shear strength and ductility as a result of the application of Fe-SMA strips. Additionally, parametric and mesh sensitivity studies were conducted. These aimed to investigate the mesh dependency of the model and to identify the optimal mesh size. Furthermore, variations in the details of the Fe-SMA strips, including thickness, width, quantity, and effect of applied temperature were explored to compare the outcomes of different applications of these strips.

Manta-ray-inspired robot for detecting damages in underwater concrete structures

Manta-ray-inspired robot for detecting damages in underwater concrete structures

A Sustainable Steel-GFRP Composite Bars Reinforced Concrete Structure: Investigation of the Bonding Performance

Steel-fiber reinforced polymer (FRP) composite bars (SFCBs) can enhance the controllability of damage in concrete structures; thus, studying the interfacial bonding between them is fundamental and a prerequisite for achieving deformation coordination and collaboration. However, research on the interfacial bonding performance between SFCBs and concrete remains inadequate. This study conducted central pullout tests on SFCB-concrete specimens with different concrete strengths (C30, C50, and C70), bar diameters (12, 16 and 20 mm), and hoop reinforcement constraints, analyzing variations in failure modes, bond-slip curves, bond strength, etc. Additionally, finite element simulations were performed using ABAQUS software to further validate the bonding mechanism of SFCB-concrete. The results showed that the failure mode of the specimens was related to the confinement effect on the bars. Insufficient concrete cover and lack of hoop restraint led to splitting failure, whereas pullout failure occurred otherwise. For the specimens with pullout failure, the interfacial damage between the SFCB and concrete was mainly caused by the surface fibers wear of the bar and the shear of the concrete lugs, which indicated that the bond of the SFCB-concrete interface consisted mainly of mechanical interlocking forces. In addition, the variation of concrete strength as well as bar diameter did not affect the bond-slip relationship of SFCB-concrete. However, the bond strength of SFCB-concrete increased with the increase of concrete strength. For example, compared with C30 concrete, when the concrete strength was increased to C70, the bond strength of the specimens under the same conditions was increased to 50–101.6%. In contrast, the bond strength of the specimens decreased by 13.29–28.71% when the bar diameter was increased from 12 to 14 mm. These discoveries serve as valuable references for the implementation of sustainable SFCB-reinforced concrete structures.

Collapse of concrete target subjected to embedded explosion of shelled explosive

This paper broadens previous research concerning spalling damage in concrete structures under buried explosions, by additionally examining the effects of shell thickness and shell head length of the warhead experimentally and numerically. In the experiment, concrete targets were subjected to the embedded explosion of cylindrical shelled explosives with different charge masses, shell thicknesses, and shell head lengths. The results reveal that thicker charge shells and longer shell heads decrease the surface area and the depth of spalling damage on the rear surface of concrete structures. In addition, to investigate the damage mechanism and predict the critical collapse thickness (CCT), two- and three-dimensional finite-element models were developed and correlated well with the experimental results. Parametric analyses are also applied to examine how the embedded depth, length-to-diameter ratio, shell thickness, and shell head length affect the CCT for a buried explosion. Finally, an empirical formula is proposed to estimate the CCT based on these factors.

Experimental and theoretical investigations on the damage evolution of the basalt fiber reinforced concrete under freeze-thaw cycles

Damage and defects in concrete structures are unavoidable due to their complex composition and challenging manufacturing process, which can lead to fatal collapse in service. In the present study, we have conducted a comprehensive investigation on the damage evolution of the basalt fibre-reinforced concrete (BFC) under freeze-thaw cycles, based on experiments and theoretical modelling. We have performed the freeze-thaw cycle experiment on the BFC and measured the curve of the damage-cycling number. Based on the thermodynamic principle, we have derived the expression of the relationship between the damage and the freeze-thaw number. Then, using the attenuation theory as a foundation, we have built another damage model. The experimental data is then fitted using the two established models, and the corresponding parameters are identified. Furthermore, the detailed mechanism of the free-thaw-induced damage has been analysed by the numerical simulation of the thermal stress inside the BFC. These findings cast new light on the deep understanding of the failure of concrete materials, which are beneficial to engineering new materials and new structures in many fields.

Assessing the stress-transfer capability of mineral impregnated PBO yarns in a limestone calcined clay cement-based (LC3) matrix

Technical textiles made of poly(p-phenylene-2,6-benzobisoxazole) (PBO) represent attractive candidates for strengthening and repairing damaged concrete and masonry structures, due to the outstanding durability and mechanical performance of PBO fibres. Similarly to their aramid counterparts, PBO fibres have proved very effective against dynamic and impact loading. In this contribution, the pull-out behaviour of PBO multifilament yarns embedded into a blended cement-based matrix is investigated, with particular reference to its stress-transfer capacity. In addition to the as-received PBO yarns, impregnation with a cement-based suspension, which can fully preserve the inorganic nature of the composite system, is also evaluated. Experimental results are presented and interpreted using a one-dimensional mechanical model. The findings indicate that mineral impregnation of the yarns provides a 40% increase in the stress-transfer capacity with the matrix, corresponding to a halving of the anchoring length. These performance gains are also supported by a transition in the failure mechanism which shifts from friction-based pull-out to fibre rupture.

Influence of Gypsum on the Residual Properties of Fly Ash-Slag-Based Alkali-Activated Concrete

High-temperature exposures of concrete lead to serious damage in concrete structures, resulting in the significant decay of mechanical properties and spalling of concrete. Alkali-activated concretes (AAC) of blended aluminosilicate precursors and activators have been proven to have higher thermal endurance than conventional portland cement concrete. Incorporation of gypsum (GY) in alkali-activated systems has proven to positively impact the mechanical properties when adopted in controlled amounts. GY releases SO42- to the binder system, which helps in the formation of ettringites, along with Ca2+, which leads to the formation of hydrates. This causes a reduction in porosity and improves strength gain. Incorporation of GY into the fly ash-slag-based alkali-activated system further improves thermal endurance by retaining considerable residual strengths even after 800°C exposure. In the present study, the influence of GY on the residual mechanical properties of fly ash-slag-based AAC is investigated to explore the thermal endurance of the ternary mix at elevated temperatures. The mechanical properties of fly ash (FA), Ground Granulated Blast Furnace Slag (GGBS), and gypsum (GY) ternary blended AAC subjected to elevated temperatures are studied in comparison with conventional portland cement concrete (control mix). AAC design mixes with varying proportions of GY as a replacement to FA-GGBS precursor are tested for mechanical properties to obtain the optimum mix. The residual mechanical properties of the FA-GGBS-GY optimum ternary AAC mix are obtained after exposure to elevated temperatures up to 800°C. The morphology and microstructural characteristics of AAC are studied by Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectroscopy (EDS) analyses to investigate the influence of gypsum on the thermal endurance of concrete when exposed to elevated temperatures. Improved thermal endurance is observed for AAC when FA-GGBS precursors are replaced with 5% of GY as compared to the thermal endurance of conventional portland cement concrete (PCC) of the same compressive strength. Doi: 10.28991/CEJ-2024-010-03-017 Full Text: PDF

Behavioral assessment of intrinsically formed smart concrete using steel fibre and carbon black composite

In recent years, health monitoring consists of the periodic observation and analysis of existing systems to predict and avoid structural breakdown, thereby saving lives and significantly lowering the cost of structural maintenance and repair. Normally, non-destructive testing techniques and sensor technology are used to detect damage in concrete structures are expensive in nature. Self-diagnosing or smart concrete has emerged a new paradigm in concrete research for damage detection. Smart concrete was cast by blending functional fillers such as carbon black, and steel fibers with concrete to improve the performance. Under various load conditions, the mechanical properties of the proposed smart concrete were examined. The electrical resistance of smart concrete was measured using the Four Probe Method and the Arduino UNO software. SEM and XRD were used to investigate the microstructures of intrinsically smart concrete. Thermogravimetric analysis was employed as a Non-Destructive Testing method to observe the hydration process. Furthermore, the obtained data were linked with the electrical resistivity of the smart concrete to assess corrosion damage. The electrical resistivity method is also an economical method and effective method to monitor the rate of corrosion.

Detecting Multiple Damages in UHPFRC Beams through Modal Curvature Analysis

Curvature-based damage detection has been previously applied to identify damage in concrete structures, but little attention has been given to the capacity of this method to identify distributed damage in multiple damage zones. This study aims to apply for the first time an enhanced existing method based on modal curvature analysis combined with wavelet transform curvature (WTC) to identify zones and highlight the damage zones of a beam made of ultra-high-performance fiber-reinforced concrete (UHPFRC), a construction material that is emerging worldwide for its outstanding performance and durability. First, three beams with a 2 m span of UHPFRC material were cast, and damaged zones were created by sawing. A reference beam without cracks was also cast. The free vibration responses were measured by 12 accelerometers and calculated by operational modal analysis. Moreover, for the sake of comparison, a finite element model (FEM) was also applied to two identical beams to generate numerical acceleration without noise. Second, the modal curvature was calculated for different modes for both experimental and FEM-simulated acceleration after applying cubic spline interpolation. Finally, two damage identification methods were considered: (i) the damage index (DI), based on averaging the quadratic difference of the local curvature with respect to the reference beam, and (ii) the WTC method, applied to the quadratic difference of the local curvature with respect the reference beam. The results indicate that the developed coupled modal curvature WTC method can better identify the damaged zones of UHPFRC beams.

Machine Learning Application for Concrete Surface Defects Automatic Damage Classification

Defects damage in concrete structures are an important measure of structural integrity and serviceability. In the context of investigating the condition of concrete surface that has defects, a visual inspection is usually performed. However, this method is subjective, tedious, time-consuming, and complicated, requiring access to many components of a large project design. Therefore, a Machine Learning classifier for concrete surface defect classification using the Discriminant Analysis Classifier was introduced to more accurately extract the types of concrete surface defects information from the digital images. The aim of this research is to increase the efficiency of concrete surface defect analysis in terms of quality, time and cost. 200 images were collected, with 50 images for each concrete defect (crack, corrosion, spalling, and no defect) serving as control data. The Gray Level Co-Occurrence Matrix (GLCM) is used to create an image processing and feature extraction algorithm. This model is trained using 80% of the image data and tested using another 20% of the image data. Thus, the model achieved 95% accuracy on the training data and 70% on the test data when using Quadratic Discriminant Analysis. These findings is very important to help engineers or construction inspectors in inspection activities.

A Review on Concrete Structural Properties and Damage Evolution Monitoring Techniques.

Concrete structures have emerged as some of the most extensively utilized materials in the construction industry due to their inherent plasticity and high-strength characteristics. However, due to the temperature fluctuations, humidity, and damage caused by human activities, challenges such as crack propagation and structural failures pose threats to the safety of people's lives and property. Meanwhile, conventional non-destructive testing methods are limited to defect detection and lack the capability to provide real-time monitoring and evaluating of concrete structural stability. Consequently, there is a growing emphasis on the development of effective techniques for monitoring the health of concrete structures, facilitating prompt repairs and mitigation of potential instabilities. This paper comprehensively presents traditional and novel methods for concrete structural properties and damage evolution monitoring, including emission techniques, electrical resistivity monitoring, electromagnetic radiation method, piezoelectric transducers, ultrasonic techniques, and the infrared thermography approach. Moreover, the fundamental principles, advantages, limitations, similarities and differences of each monitoring technique are extensively discussed, along with future research directions. Each method has its suitable monitoring scenarios, and in practical applications, several methods are often combined to achieve better monitoring results. The outcomes of this research provide valuable technical insights for future studies and advancements in the field of concrete structural health monitoring.

Integrating Image Processing and Machine Learning for the Non-Destructive Assessment of RC Beams Damage

Non-destructive testing (NDT) is a crucial method for detecting damages in concrete structures. Structural damage can lead to functional changes, necessitating a range of damage detection techniques. Non-destructive methods enable the pinpointing of the location of the damage without causing harm to the structure, thus saving both time and money. Damaged structures exhibit alterations in their static and dynamic properties, primarily stemming from a reduction in stiffness. Monitoring these changes allows for the determination of the failure location and severity, facilitating timely repairs and reinforcement before further deterioration occurs. A systematic approach to damage detection and assessment is pivotal for fortifying structures and preventing structural collapse, which can result in both financial and human losses. In this study, we employ image processing to categorize damaged beams based on their crack growth and propagation patterns. We also utilize support vector machine (SVM) and k-nearest neighbor (KNN) methods to detect the type, location, and extent of failures in reinforced concrete beams. To provide context and relevance for the laboratory specimens, we will compare our findings to the results from controlled experiments in a controlled laboratory setting.

Automatic assessment of freeze-thaw damage in concrete structures using piezoelectric-based active sensing approach and deep learning technique

Concrete structures in cold climates are susceptible to freeze-thaw (f-t) damage, which could lead to structural deterioration and stability issues. Accurate assessment of such damage is crucial for effective maintenance and repair strategies. However, current methods for evaluating concrete f-t damage rely on tedious testing procedures that lack automation and real-time capabilities, potentially delaying the repair process. This study proposes an automatic method to assess f-t damage in concrete by combining piezoelectric-based active sensing and deep learning (DL). Firstly, concrete beams with two different force states were prepared and subjected to different f-t cycles, during which measurements including surface characteristics, mass loss, dynamic elastic modulus and stress wave were performed. The relationship between concrete f-t damage evolution and stress wave signal variation was investigated. The collected signals were then converted into time-frequency maps using continuous wavelet transform (CWT), thereby establishing a CWT image-based dataset. More importantly, an innovative DL model (DSC-ACGRU) that integrates depth-wise separable convolution (DSC), convolutional gated recurrent unit (ConvGRU) and attention mechanism (AM) was developed to automatically extract damage-sensitive feature from CWT images and ultimately predict the degree of concrete f-t damage. Finally, the performances of the proposed DSC-ACGRU model were experimentally evaluated and compared with five machine/deep learning methods. The results show that the proposed model can rapidly and accurately assess the f-t damage degree in concrete and outperforms other learning algorithms, which is significant for identifying potential hazards and repairing damaged concrete.

Assessing and Forecasting ISR-Affected Critical Infrastructure: State-of-the-Art and Challenges.

Internal swelling reactions (ISRs) are among the most critical deterioration mechanisms affecting infrastructure's durability worldwide. While preventative measures for new structures have been extensively explored, effective protocols for diagnosing and prognosing ISR-affected structures, especially at their early stages, are still required. Therefore, through a comprehensive bibliometric analysis, this study focuses on exploring the evolution and current methods for assessing and forecasting ISR damage in concrete structures. For diagnosis, a shift from concrete petrography and non-destructive techniques (NDTs) towards more comprehensive methods (i.e., multi-level assessment) with the stiffness damage test (SDT) and damage rating index (DRI) is observed. Moreover, it identifies the valuable inputs from residual expansion and pore solution analysis as relevant parameters for prognosis. Based on these findings, a structured management framework is proposed aiming to refine the diagnosis and prognosis processes of ISR-affected infrastructure, ultimately contributing to improved long-term structural health and maintenance strategies.

Early age cracking relevant to mass concrete dam structures during the construction schedule

Managing the early-age thermal behavior of mass concrete structures like dams, dykes, bridge piers, thick raft foundations, etc., is the major concern during their construction. The heat of hydration (HOH) of the fresh concrete pour does not get easily dissipated to ambiance since concrete is predominantly a poor conductor of heat. Eventually, in the presence of external boundary conditions (thermal or mechanical), the slower rate of heat dissipation may at times induce a substantial thermal gradient within the mass concrete which further may induce thermal stresses leading to the formation of cracks. The article attempts to develop a thermo-mechanical numerical model based on the finite element (FE) methodology to mimic the early age behavior of mass concrete that accounts for time and temperature-dependent heat generation during the hydration process with the evolution of thermal and mechanical properties over time. A two-stage sequential thermo-mechanical model is developed consisting of a thermal model to assess the thermal history of a particular concrete mix which is experimentally validated by performing thermal monitoring of a fresh concrete pour for 10 days, and a mechanical model to assess the stress and the cracking states, the model utilizes the concrete damage plasticity (CDP) model based on the principle of continuum damage mechanics which is characterized by experimental results. Later on, the methodology so developed is employed over a practical scenario of the dam construction process considering the relevant boundary conditions to arrive at a suitable optimum lift height and pouring frequencies.

Climate Change Effect on Freeze-thaw Load in Finland

Freeze-thaw damage is one of the major degradation mechanisms of climate exposed porous rock-based building materials in existing Finnish building stock. In recent studies, it has been shown that not only the amount of freeze-thaw cycles but especially the amount of wind-driven rain (WDR) before freeze-thaw cycles has a major effect on the rate of freeze-thaw damage in concrete structures. In this study, freeze-thaw cycles after a rain event, the average annual amount of WDR before freeze-thaw cycles and the average amount of WDR before each cycle is calculated in different locations in Finland. The calculations are made for present and projected 2050 and 2080 climates with three greenhouse gas scenarios: RCP2.6, RCP4,5 and RCP8.5. Based on the results, the freeze-thaw load is decreasing in coastal and southern areas and staying at current level or increasing in northern parts of Finland. In addition, the level of freeze-thaw load is getting less direction or geographically dependent. The results strongly indicate that the exposure level might be decreasing in coastal area and southern parts of Finland because both the freeze-thaw cycles and the annual amount of WDR before cycles are decreasing. However, an average amount of WDR before a freeze-thaw cycle is actually increasing and so, the intensity of freeze-thaw attack is not decreasing. Thus, for the future climates freeze-thaw durability of porous rock-based structures should be still emphasised.

A coupled thermo-hydro-mechanical framework for simulating the failure response of asphalt concrete under freezing conditions

Freezing-induced damage occurs in extremely cold regions in the form of sudden and simultaneous formation of cracks in dry and wet asphalt concrete. This work investigates the mechanical response, up to and including final failure, of dry and partially saturated asphalt concrete at freezing temperatures as low as −30° (−22 °F). A coupled thermo-hydro-mechanical constitutive framework is proposed within the context of finite element simulation. The framework contains nonlinear viscoelastic-viscodamage constitutive equations, the swelling strain effect associated with the freezing of water content, and the moisture damage constitutive equation. The stress induced in dry and partially saturated asphalt concrete samples due to reducing temperatures is simulated and compared with those of a studied experiment, and an excellent agreement is achieved in terms of the critical features, including the final brittle failure and the swelling effect. A parametric study is then performed to discover the effects of the microstructural constituents of asphalt concrete on its mechanical response under freezing conditions. This work provides a reliable computational framework that can predict deterioration and damage in asphalt concrete structures in extremely cold weather and further leads to the development of novel construction materials that can resist the negative effects of cold temperatures, e.g., in the Arctic.

Assessing the fatigue damage of concrete structures using automatically classified crack severity level information

The aim of this paper is to assess the fatigue damage of concrete structures using automatically classified crack severity level information. To solve the problem that the label data used for the automatic classification of regional complex crack severity levels can only be labeled by intuition, this paper proposes a quantitative index of regional complex crack severity levels that can consider the regional crack width, height, and density information. The testing set F1 score of the Swin Transformer classification model trained by the label data reaches 0.9903. To investigate the feasibility of assessing the concrete structure fatigue damage based on the crack change information, the paper adopted the scanning shooting method to acquire the concrete beam panoramas at different cyclic loading times in the fatigue test, and automatically classified the crack severity levels in different regions of the panoramas using the Swin transformer. The test results show that the information on the change in the number of regions with the same crack severity level on the concrete structure surface can be used to estimate approximately the fatigue life percentage interval that the concrete structure is currently in, which provides a reference for the development of maintenance measures and safety warnings.

A multi-scale model from microscopic cracks to macroscopic damage of concrete at elevated temperatures

A multi-scale model is established to describe the relationship between the macroscopic damage evolution and microscopic cracks behaviors of concrete at elevated temperatures. The evolution equation of the ideal microscopic crack system of concrete at elevated temperatures is deduced for construct the model, which can predict the microscopic crack density and macroscopic damage of concrete at elevated temperatures. The multi-scale model fuses some advantages of the traditional microscopic and macroscopic damage models. Finally, multi-scale damage of a concrete block under high temperature is predicted and compared with the corresponding experimental results, which is utilized to support the ability of the developed model. The results show that the developed multi-scale model can be used to evaluate fire damage of concrete structures in macro-scale as well as explain its physical mechanisms in micro-scale.