Concrete Structural Elements Research Articles

Detection of corrosion effects on prestressed concrete bridge deck slabs from the champlain bridge through non‐destructive testing techniques

AbstractAs aging infrastructures raise public concerns, evaluating their performance is crucial for maintaining structural integrity, especially for corroding prestressed concrete members. These structures may experience substantial tendon cross‐sectional area loss before any visible deterioration becomes detectable. While various non‐destructive techniques (NDT) have proven effective in labs, correlating corrosion‐induced damage in field members remains a challenge. Establishing these correlations is key for understanding the overall performance of aging structural concrete elements and ensuring their continued safe operation through non‐invasive means. This paper investigates various NDTs on a concrete bridge deck, aiming to correlate results. Visual inspection, Schmidt rebound hammer, Ultrasonic Pulse Velocity (UPV), corrosion detection techniques, Ground Penetrating Radar (GPR), Ultrasonic Pulse Echo (UPE), and Impact Echo (IE) methods are evaluated for detecting concrete deck damage. Results show the methods' capabilities in detecting defects to a certain extent, highlighting their potential in assessing aging concrete infrastructures.

Embodied carbon saving of reusing concrete elements in new buildings: A Swedish pilot study

Reusing the building elements is the highest possible level of circularity for buildings that must be demolished, potentially slowing down climate change. This study explores the embodied carbon reduction of construction of a pilot building with structural elements of reused concrete. The assessment focuses on applying different methodological approaches and discussing the upscaling opportunities of reusing concrete elements from a global warming potential perspective. The assessment shows large embodied carbon savings compared to conventional building practices like recycling the concrete and building with new low-carbon and prefabricated elements. Embodied carbon saving is also obvious when applying alternative system modelling, future market projection and different allocation approaches of the production emissions of the elements. Finally, the study emphasises the need for further research in evaluating the benefits of reusing structural concrete elements broadly, like including the deconstruction impact related to elements for reuse, to be able to draw general conclusions.

Fiber orientation and orientation factors in steel fiber‐reinforced concrete beams with hybrid fibers: A critical review

AbstractFiber orientation is of paramount importance for the design of fiber‐reinforced concrete (FRC) structural elements, because it markedly influences the postcracking properties of such material. For this reason, structural codes introduce orientation factors which aim to correlate the real mechanical properties of the structural element with the ones determined from standard beams. Although the need of considering fiber orientation in design codes is commonly accepted, the orientation factors are still based on a limited number of research studies, raising the need to better determine fiber orientation to improve the current standards and support the design process of FRC elements. In this research, a steel fiber‐reinforced concrete (SFRC) with a hybrid system of macro and microfibers is steered into a broad range of fiber orientations and cast into standard beams. Besides measuring the mechanical performance of these SFRC beams, three different methods for assessing fiber orientation are employed, namely electromagnetic induction, image analysis, and micro‐computed tomography. The comparison between the outcomes of the different methods provides detailed information about the accuracy and suitability of each method, considering the corresponding domain of applicability at structural level. Finally, a critical review of the most common 2D and 3D orientation parameters found in literature is performed, and the equations are adapted to account for the hybrid system of fibers.

Experimental investigation of quarry rock dust incorporated fly ash and slag based fiber reinforced geopolymer concrete circular columns

Manufacturing ordinary Portland cement (OPC) poses significant challenges for sustainable construction practices. OPC manufacturing emits substantial greenhouse gases into the atmosphere and demands extensive raw materials. In pursuit of greener alternatives, researchers explore geopolymer concrete (GPC), a revolutionary material that entirely replaces OPC, comprising industrial wastes/by-products activated through an alkaline solution. The study aims to investigate the feasibility of incorporating quarry rock dust (QRD) into GPC production for environmentally sustainable structural applications. Circular columns (200 mm diameter, 1000 mm length) were formulated using GPC blends with fly ash, slag (SG), and QRD as a partial SG replacement. The structural performance of these columns, with and without steel fiber reinforcement, was evaluated under varied loading conditions. Results show that QRD is a valuable ingredient in GPC for structural concrete elements, offering performance comparable to traditional OPC concrete. Furthermore, the incorporation of steel fibers significantly enhances the peak axial loads, displacement response, and overall performance of GPC columns with or without QRD. Fiber-reinforced GPC columns demonstrated approximately 8–10% higher ultimate load capacity than equivalent OPC columns. Eccentricity was found to significantly reduce ductility, but fiber reinforcement offers substantial ductility improvements (25–55%).

A multiscale steel–concrete interface model for structural applications

In this paper, a new multiscale macro-element formulation for the steel–concrete interface modeling is proposed. This element allows for the representation of the behavior of steel and the interface zone surrounding it. It can also model the interfacial bond stresses in between. Compared to conventional interface models of the literature, which employ separate mesh elements for the steel and the interface, utilizing a macro-element to model both components simplifies the creation of a reinforced concrete structure mesh. Moreover, the macro-element equilibrium is solved using a sub-structuring method that aims to reduce the computational cost. At the global level, it is considered as a four-node element linked to two-dimensional and three-dimensional concrete elements. At the local level, an assembly of multiple three-node bar elements with bond stresses is performed. An inner mesh discretization is therefore possible at the local level independently of the global level. The coupling between the two modeling scales is done using a static condensation technique. The formulation of the macro-element is presented in this paper. A selection of numerical examples is provided. The presented applications demonstrate the robustness of the proposed interface model and its capacity to reproduce the experimental behavior of reinforced concrete structural elements.

Experimental assessment of FRP repairing in concrete cylinders exposed high temperatures

This study presents an experimental evaluation of fiber-reinforced polymer (FRP) strengthening in concrete specimens damaged by exposure to high temperatures. For this purpose, sixty cylindrical concrete specimens were produced. Specimens exposed to temperatures of 200 °C, 400 °C, and 600 °C were strengthened using carbon and glass FRP fabrics in two or three layers. Compression tests were conducted to assess the strength of the specimens. The impact of temperature, fabric type, and the number of fabric layers on the concrete's strength was thoroughly investigated. Digital microscopy was employed to explore the formation of cracks due to temperature changes, followed by Scanning Electron Microscopy (SEM) analyses to examine the microscopic structural changes in the concrete samples. The study highlights the importance of considering changes in both strength and behavior models together when evaluating the strengthening contributions provided by FRP. The results indicate that FRP applications on fire-damaged concrete structures can provide effective strengthening up to 400 °C but may be insufficient at temperatures of 600 °C and above. Although an effective increase in strength was achieved at 600 °C, low levels of ductility were observed, which is undesirable for an engineering structure. Therefore, it has been determined that FRP-strengthening applications may not be effective in reinforced concrete (RC) structural elements exposed to temperatures of 600°C and higher.

Reinforcement of Concrete Structures with High-Performance Fiber-Reinforced Concrete (HPFRC)

The incorporation of high quality fibers into very high strength concrete matrices leads to materials that are especially suitable to withstand extreme loads. For this reason, these materials are adequate for reinforcement and durability improvement of buildings and infrastructure constructions. The general objective of this work is to numerically study the flexural behavior of concrete structural elements reinforced with HPFRC. This general goal includes the analysis of the effect of the different variables, including the content and type of fibers, as well as the thickness of the reinforcement. The results of this analysis can contribute to improving the efficiency of the intervention techniques. For this purpose, numerical models are calibrated to reproduce the behavior observed in experimental tests carried out on HPFRC elements. Then the models are used to reproduce reinforced concrete beams reinforced with HPFRC under flexural loading. Finally, the effect of the aforementioned design variables on the behavior of reinforced beams is studied using the calibrated models. The results show the efficiency of the intervention increases with the fiber content and thickness of the reinforcement layer. However, the definition of the optimal design should also consider other criteria, such as constructional limitations and cost analysis.

Investigation Of Strength Development In Conventional And High Strength Concrete Under Different Curing Conditions

The composite material obtained by blending cement, aggregate, water and, when necessary, an additive, placing a mixture with carefully adjusted proportions into molds of the desired shape and size without gaps and hardening under appropriate maintenance conditions, is called concrete or traditional concrete. In the 1960s, concretes with compressive strengths of 41 MPa and 52 MPa were used on the market in the USA. In the recent past, concretes with compressive strengths varying between 80 MPa and 100 MPa have been commercially applied in structures made with in-situ concrete and prestressed concrete structural elements. Strength developments of traditional and high-strength concretes under different curing conditions will be examined. For this purpose, central pressure tests were carried out on cube samples produced. For concrete production; For each aggregate class, cement, saturation water, mixed water and high strength concretes; It was prepared by weighing silica fume and superplasticizer. The prepared samples were stored in standard or cold water for 3 days, 7 days, 28 days and 90 days. In this study, the strength development of traditional and high-strength concretes under different curing conditions was examined experimentally. In accordance with the experimental work plan determined for this investigation, concrete cube samples were produced and these samples were subjected to the central pressure test. The concretes produced are in C55, C20 and C12 classes. As a result, although similar behaviors were observed in high-strength and conventional concretes cured in different environments, it was determined that the difference in curing temperature was more effective on the strength development than the inadequacy of curing in water. It should be noted here that it would be beneficial to conduct similar studies on different types of concrete.

An experimental study of some mechanical properties and absorption for polymer-modified cement mortar modified with superplasticizer

Abstract Polymer-modified concrete and mortars are widely used with different purposes in precast concrete and prestressed concrete structural elements, bridge decks, buildings, and repair requirements because of their significant behavior under different conditions, such as durability against freezing and thawing, ability to absorb impact loads and good mechanical properties compared to ordinary concretes. This study aims to improve some mechanical properties of cement mortar by using styrene–butadiene rubber (SBR) latex, a superplasticizer, and also decreasing the absorption level to lower values by using these two admixtures. The study shows that using 10% SBR by weight of cement increases the compressive strength from 28.6 to 30.8 MPa and decreases the absorption from 2.3 to 1.7% which gives durability for cement mortar. This study also shows the development in strength achieved by using SBR superplasticizer simultaneously, and absorption decreases to only 0.76% for 10% SBR mixes. In comparison, it was 1.75% for mixes with SBR only.

Implementation and validation of robot-enabled embedded sensors for structural health monitoring

In the past decades, structural health monitoring (SHM) has matured into a viable supplement to regular inspections, allowing to execute repair and maintenance work at early stages of structural damage, thus resulting in relatively low rehabilitation costs. With the advent of wireless technologies and advancements in information and communication technologies, civil infrastructure has been increasingly instrumented with wireless sensor nodes to record, analyze, and communicate structural data relevant to SHM. However, in harsh environments, wireless sensor nodes may be damaged and fail to operate. A promising alternative to wireless sensor nodes is to embed sensors directly into concrete. In this paper, a sensor system for embedment into concrete is proposed, able to self-assess the structural data recorded from the concrete. Moreover, the data and information provided by the sensors are collected by robotic systems via RFID data transfer in an attempt to reduce the amount of human interaction and to overcome the limitations with respect to communication range and power supply. In laboratory experiments, the sensor systems are embedded into a concrete structural element, validating the capabilities of the sensors to record and to analyze the structural data. Furthermore, as will be shown in the paper, power will be supplied on-demand by legged robots that are deployed to collect the data and information, which is provided by the sensors for persistent storage. In summary, this study corroborates that the embedded sensors represent a crucial step towards intelligent, self-assessing concrete.

Intelligent carbon-based TRC as self-strain sensor

Abstract: This study investigates the monitoring capabilities of a carbon yarn embedded in a cementitious matrix as an intelligent strain sensor by means of a gauge factor (GF). In the proposed configuration, the yarn simultaneously functions as the structural system and as the sensory platform, yielding efficient and intelligent concrete elements. The concept of the smart self-sensory system is based on monitoring changes in the electrical properties of the carbon yarn, namely: resistance, inductance, or impedance, and correlating them to the structural health. These capabilities were investigated in carbon yarns that were inserted within textile-reinforced concrete (TRC) structural elements, in which the correlation yielded important information in the macro-structural level. The integrative measurements were used to estimate the structural state associated with cracking and straining along the element. Yet, to fully understand the structural-electrical mechanism and to further enhance the development of the smart carbon-based sensor, it is essential to explore the electrical-micro-structural mechanism at the component level. Therefore, the study offers to explore the correlation between straining and cracking at the component level by means of GF. Separately investigating strained or cracked zones at the yarn’s level will provide vital information on the monitoring capabilities of the carbon yarn. To answer these goals, the study will perform an experimental investigation of carbon-based TRC elements subjected to a designated pull-out test based on a uniaxial tensile setup. The proposed setup aims to correlate between changes in the electrical properties characterizing the carbon yarn and the micro-structural mechanism associated with the degradation of the structural health associated with the propagation of the crack. The outcomes of this study aim to enhance the understanding of the structural-electrical correlation, which paves the way for the development of intelligent strain sensors for TRC structures.

Enhancing acoustic emission analysis for corrosion damage detection in reinforced concrete by k-nearest neighbors method

The Acoustic Emission (AE) method is considered a significant tool for detecting damage in reinforced concrete structures caused by reinforcement corrosion. However, processing a large amount of AE data using traditional analysis methods is time-consuming. Therefore, integrating machine learning (ML) techniques into this process offers a more logical and effective solution to expedite data analysis and enhance damage detection accuracy. This study aims to train an ML model for detecting corrosion damage in reinforced concrete structures using an AE dataset. In pursuit of this goal, it seeks to enhance source localization that contributes to identifying crack patterns through signal-based analysis methods. AE data obtained from a reinforced concrete structural element subjected to accelerated corrosion testing in laboratory conditions have undergone detailed analyses with the ML model. In this context, an ML model capable of learning signals with various wave characteristics and parameters has been successfully integrated into the source localization algorithm, significantly improving AE source localization outcomes. Furthermore, this method is critical for structural safety as it enables the early detection of reinforcement corrosion and precise localization of cracks. The integration of this machine learning model with AE data serves as a valuable tool developed for more effective monitoring of damage in structures.

Progressive Collapse Behavior of a Precast Reinforced Concrete Frame System with Layered Beams

A possible way to improve the structural safety and robustness of precast building structures is to develop effective precast frame systems with layered beams, which combine prefabricated parts with cast-in situ ordinary concrete, high-performance concrete, fiber concrete, or FRP. The paper provides a new type of precast reinforced concrete frame system with layered beams for rapidly erected multi-story buildings resistant to accidental actions. Using a combination of the variational method and two-level design schemes, a simplified analytical model has been developed for structural analysis of the precast reinforced concrete frame system, both for serviceable and ultimate limit states as well as for accidental actions. The proposed model allows for determining shear deformations and the formation and opening of longitudinal cracks in the intermediate contact zone between precast and monolithic parts of reinforced concrete structural elements of the frame, as well as the formation and opening of normal cracks because of the action of axial tensile force or bending moment in these elements. The design model was validated by comparing the calculated and experimental data obtained from testing scaled models of the precast reinforced concrete frame system with layered beams. The paper investigates and thoroughly analyzes the factors affecting the stiffness and bearing capacity of the intermediate contact zone, discusses the criteria for the formation of shear cracks along the contact zone of precast and monolithic concrete, and examines the change in the stiffness and dissipative properties of layered elements at different stages of their static–dynamic loading. The robustness of the experimental models of the structural system was not ensured under the specified load, section dimensions, and reinforcement scheme. Following an accidental action, longitudinal cracks were observed in the contact joint between the monolithic and prefabricated parts in the layered beams. This occurred almost simultaneously with the opening of normal cracks in adjacent sections. A comprehensive analysis of the results indicated a satisfactory degree of agreement between the proposed semi-analytical model and the test data.

Experimental and numerical investigation of the bending, shear, and punching shear behavior of recycled aggregate concrete precast/prestressed hollow core slabs

AbstractAlthough it has been shown that using recycled concrete aggregate in new structural concrete is economical and sustainable, the use of this material for such applications is still not widespread. One of the reasons is that manufacturers, designers, engineers, owners, and other market players are not familiar with specifying and utilizing this material—although standards are starting to incorporate provisions for recycled aggregate concrete, successful, practical example projects are needed. The current paper describes the results of a partnership between universities and a precast concrete manufacturer of hollow core slabs. Seven hollow core slabs with volumetric replacement ratios of natural aggregate with recycled aggregate from 0% to 60% were tested to failure in both bending and shear, and then undamaged portions of the slabs were subjected to punching shear until failure. The results showed only mild differences in strength, with different replacement percentages of recycled aggregate under the various loading scenarios. Numerical simulations performed in Abaqus demonstrated the feasibility of analyzing recycled aggregate concrete structural elements and provided important insights into their behavior.

Mapping of Atmospheric Corrosion of Steel in Southeast India Using Inverse Distance Interpolation Technique

Reinforced concrete and steel structural elements undergo premature degradation and lose strength primarily due to corrosion. Corrosion is an electrochemical phenomenon and its severity depends on several environmental factors. Experimental data on the corrosion of steel is important for making engineering decisions toward improving the service life of civil infrastructures. No recent dataset on atmospheric steel corrosion in an Indian coastal environment is found to be available in the literature, and hence this research attempts to address some of the literature gap. This paper presents an experimental study conducted to determine the corrosion rate of TMT, high-chromium steel, and stainless steel rebars exposed in actual coastal and inland regions for a period of 1 y. The site locations were located in southeastern parts of Tamil Nadu state in India. Based on the first-year corrosion rate, the corrosion rate after extended exposure (10 y) was determined based on ISO 9224 recommendations. The atmospheric steel corrosion map of Tamil Nadu state was developed using inverse distance interpolation technique. Microstructural studies indicated the formation of lepidocrocite (γ-FeOOH) phase composition in the rust products collected from coastal regions.

Investigation of Linear and Nonlinear Behaviour of Reinforced Concrete Column Exposed to Corrosion Effect for Different Damage Limit Levels

The high performance of reinforced concrete structural elements under the effects of lateral loads such as earthquakes is of great importance in terms of minimizing the loss of life and property that may occur due to earthquakes. One of the parameters affecting the earthquake behavior of a reinforced concrete structure is the corrosion effect. The aim of this study is to investigate the behavior of reinforced concrete columns exposed to different corrosion levels for different durations within the damage limits. In this direction, it is aimed to perform linear and nonlinear analysis of reinforced concrete columns in 5 different corrosion scenarios. In the study, nonlinear analyzes of the column were made using ANSYS software, the data obtained were determined for different damage limit levels and compared with the section analysis. Because of the study, moment-curvature relations, lateral load-horizontal displacement relations, bending ductility and plastic rotation capacity of the reinforced concrete column were determined for each corrosion scenario.

Hybrid Machine learning Techniques-Aided design of corroded reinforced concrete beams

Shear strength (SS) is an essential component in the design of reinforced concrete structural elements, particularly in severe settings where reinforcements can occur and cause a loss in SS. The purpose of this work is to develop a framework for predicting the SS and optimal design of corroded reinforced concrete (CRCo) beams by using a machine learning (ML) approach. The model employed was Gradient Boosting (CGB), optimized using three metaheuristic algorithms. By optimizing the CGB model with the Hunger Games Search (HGS) algorithm, the study achieved the best ML model for predicting the SS of CRCo beams, with an R2 value of 0.996. The proposed model outperformed five other empirical SS models for CRCo beams, and sensitivity, partial dependence analyses were also conducted to explore the impact of various variables on SS. Finally, this work provided guidance on selecting appropriate beam sizes and corrosion rates for optimal CRCo beams design solutions.

Machine Learning-Based Framework for Failure Forecast and Shear Strength Estimation of Non-Conforming RC Shear Walls

ABSTRACT Earthquake reconnaissance after the recent Kahramanmaras, Turkey, earthquake sequence in February 6, 2023 has shown that the majority of the reinforced concrete (RC) structural elements do not comply with the Turkish Building Earthquake Code (TBEC 2018) requirements. Accurate estimation of the actual strength of existing non-compliant RC structural members is critical to accomplish reliable earthquake performance assessments. This study aims to propose a machine learning-based framework to establish a realistic failure forecast as well as an accurate shear strength estimation depending on the expected failure modes of such walls. To achieve this, shear wall design properties are assigned as inputs whereas failure mode and shear strength were designated as outputs for the corresponding classification and regression problems, respectively. Widely used machine learning methods, namely: Ridge Linear Regression, Logistic Regression, Multi-layer Perceptron, Support Vector Machine, Random Forest, XGBoost, LightGBM, and CatBoost are employed using a database consisting of 432 non-conforming shear walls. The data is randomly split to train and test sets (80% and 20%, respectively) to develop the predictive models and to evaluate model performances, respectively. Performance metrics (e.g. R 2 , RMSE) are evaluated over a hundred random splits to ensure robustness. The proposed CatBoost-based models achieve overall accuracies above 90% for both failure prediction and shear strength estimation. The proposed framework is believed to be a promising tool for earthquake engineers for practical and accurate failure mode forecast and shear strength estimation of non-compliant RC shear walls.

A Meso-scale study to predict high-temperature behavior of concrete structures in a hygral-thermal-chemical-mechanical framework

Fire has become one of the common hazards that a concrete structure faces in today’s world. Considering the heterogeneity of concrete and the contrasting nature of the constituents at high temperatures (cement paste dehydrates and shrinks while aggregate expands in volume), the use of a homogeneous macroscopic model to predict concrete's thermo-mechanical performance is questionable. Therefore, a more realistic description of concrete (in terms of explicit consideration of coarse aggregate and cement mortar rather than homogenized concrete medium) is needed to characterize the behavior of a concrete structure under such severe loading conditions (e.g., fire). In fact, the performance of a macro-scale (meter size or higher) concrete structure is the coupled effect of the various physico-chemical processes that occur within its constituents at meso-scale, a scale (mm-cm scale size) where coarse aggregates are randomly distributed in a mortar paste medium. Therefore, it is required to develop a model that explicitly considers the constituents' contrasting nature and the different thermo-hygral-mechanical processes at the scale of occurrence of these processes. Hence, in this contribution, a meso-scale based model is developed in a coupled hygro-thermo-chemo-mechanical (HTCM) framework to investigate the effect of high temperature on the thermo-mechanical performance numerically (e.g., in terms of spalling, deformation, residual capacity, etc.) of fire-exposed plain and reinforced concrete structural elements (e.g., slab, beam, etc.). Simulated results and validation with experimental data (taken from the literature) highlight several crucial aspects related to obtaining a more precise residual capacity of a concrete structure, which is impossible to reproduce with a homogenized macroscopic model. The study highlights that the failing of random concrete parts at different times during high-temperature exposure can not be simulated with a homogenized assumption. Further, a mesoscopic model does not require transient creep strain to be specified explicitly. The primary influencing factors behind this transient creep strain, such as the different thermo-chemical and thermo-mechanical damage of the concrete constituents (cement paste, aggregate), thermal-incompatibility of the components and associated damages, etc., are implicitly taken into account in a meso-scale model.

Study of pathological manifestations in concrete structures in the municipality of Carlos Chagas: case study

Concrete is the main material used in the construction industry. However, even with technological advances in its production, many structures perform unsatisfactorily due to errors in design, implementation, maintenance, repairs, poor quality materials, lack of maintenance and other factors of a physical, chemical or mechanical nature that contribute to structural deterioration. Given this scenario, it is crucial to explore contemporary solutions to improve the durability and performance of concrete structures in construction. Therefore, greater knowledge of structures and materials is needed, through studies and analysis of pathological manifestations, as this makes it possible to evolve the quality of constructions, which allows for better habitability and durability. The main objective of this study is to investigate the main causes of pathological manifestations in reinforced concrete structural elements in a building in the municipality of Carlos Chagas-MG, as well as methods of preventing and repairing them. To this end, the work was divided into two stages: a literature review to provide a theoretical basis for finding out about published studies dealing with the proposed topic, as well as defining an appropriate analysis methodology, and a case study. In addition, on-site visits were made to assess the state of the building and record pathologies. The results showed various pathologies such as exposure to corrosion of the reinforcement, segregation of the concrete, cracks and fissures, and efflorescence. Thus, intervention proposals were made as described in the literature to provide safety, stability, aesthetics and comfort for residents.