Concrete Conditions Research Articles

Impact of aggressive environments on the mechanical and physico-chemical properties of concrete

This comprehensive study examines the durability of concrete exposed to aggressive environments, a critical issue for the longevity of modern infrastructure. Our research analyzes the influence of concrete composition parameters and environmental conditions on its long-term durability. A series of rigorous experiments were conducted to evaluate the effects of various chemical attacks on the mechanical and physico-chemical properties of concrete, focusing on particularly aggressive environments such as seawater, acidic solutions, and wastewater. We studied two types of concrete: ordinary concrete and concrete with additives, to compare their performance in these hostile environments. The results show significant variations in the properties of concrete depending on environmental conditions and composition parameters. Compressive strength, water absorption, and microstructure were systematically analyzed at different time intervals, revealing distinct degradation trends for each type of concrete and environment. The collected data highlights the critical importance of concrete formulation in resisting chemical attacks. Concrete with additives demonstrated superior performance in most aggressive environments, underscoring the essential role of additives in enhancing durability. The study also identified key degradation mechanisms, including leaching, carbonation, and sulfate attack, providing valuable insights for the development of more resilient concrete. This research makes a significant contribution to the understanding of the complex interactions between concrete composition and aggressive environments. The results have important implications for the design and maintenance of concrete structures exposed to severe environmental conditions, paving the way for strategies to improve the long-term durability of infrastructure.

Integrating AI and statistical methods for enhancing civil structural practices: current trends, practical issues, and future direction

The integration of artificial intelligence (AI) and statistical methods has revolutionized civil engineering by enhancing accuracy, efficiency, and reliability in various processes. This review systematically examines how advanced optimization techniques, including artificial neural networks (ANNs), Design of Experiments (DOE), and fuzzy logic (FL), are transforming civil engineering practices. It emphasizes the significant roles these methods play in addressing modern challenges such as structural health monitoring, damage detection, seismic design optimization, and concrete condition assessment. The review delves into case studies and real-world applications, showcasing the potential of these methods to create more resilient, sustainable, and cost-effective infrastructures. It critically examines the limitations and scalability of these techniques, identifying gaps in current research and practical challenges in real-world applications. The investigation also highlights the need for substantial computational resources, data privacy, security, and software interoperability. By addressing these issues, the review not only shows advancements in optimization techniques but also outlines future research directions, aiming to bridge the gap between theoretical developments and practical applications in civil engineering. This review serves as an essential resource for researchers, professionals, and policymakers interested in leveraging optimization techniques to advance civil engineering practices.

A Mesoscopic Approach for the Numerical Simulation of a Mass Concrete Structure Construction Using Post-Cooling Systems

This study introduces an innovative numerical approach to simulate the construction of large concrete structures incorporating post-cooling systems employing the finite element method (FEM). The proposed methodology integrates critical construction parameters, including temperature control mechanisms, while accounting for concrete hydration and environmental conditions. Compared to traditional discrete models, this approach provides similar accuracy with substantially reduced computational costs, enhancing predictive capabilities in the thermal analysis of mass concrete. The method was applied to simulate the construction of a water intake pillar at the Tocoma hydroelectric plant in Venezuela, where the simulated results closely matched in situ temperature measurements. The findings highlight the method’s efficiency and accuracy in simulating post-cooling systems, offering a practical solution for improving the safety and cost-effectiveness in large-scale concrete construction projects.

An Assessment of the Impact of Protective Lifeline Safety Systems on Formwork Systems

Work related to the installation of formwork and the pouring of concrete involves the need to move on the formwork walls. There is a high risk of falling off the wall and falling during work. It is therefore necessary to use systems that allow for safe work at heights, but also the safety of the formwork systems themselves. This article presents the results of tests under real conditions of a person falling from the formwork. Two safety systems were tested: one based on a pole–rope system with a flexible joint and one based on climbing arrester hooks. The test results showed that the additional forces occurring in the system do not exceed 6.4 kN and that the stresses reach the highest value only at the site of installation of the system and do not exceed the yield strength of the steel used in the formwork structure. The results obtained indicate that during a fall, the fall energy is absorbed so much that there is no damage to the formwork elements, and the highest effort is observed for the formwork arrester hook and reaches 87% of the structure effort. In the case of systems with flexible posts, the maximum load values do not exceed 30% of the structure. The presented results may be useful in designing and planning assembly works and using formwork systems in concrete pouring conditions.

Flexural Behaviour and Environmental Impact of African Fan Palm Reinforced Concrete Beams under Symmetrical Loading Conditions

The paper presents the results of investigation on reinforced beams adopting eco-friendly materials for sustainable concrete. The use of African fan palm is explored to mitigate the environmental impact aimed at reducing carbon emissions associated with the production and use of steel reinforcement. The beams were tested under a four -point loading system with the ends simply supported. The test comprised twenty-three beams that were reinforced with African fan palm bars whilst two beams were reinforced with mild steel bars to serve as control beams. The study investigated the flexural strength and deformation characteristics of the beams under monotonic loading. Two concrete strengths of 15.34N/mm2 or 22.37N/mm2 were adopted and proposed optimal tensile ratios for an under reinforced fan palm section. The parameters considered included the tension bars ratio (ρ), concrete compressive strength, span-to-effective-depth ratio and loading conditions. A linear elastic behaviour of both fan palm and steel reinforced concrete beams was observed to the point of first crack load beyond which the beam stiffness continued to reduce until failure. A comparison of the experimental results with results of theoretical analysis of the beams revealed that a 1.22% increase in the tensile ratio of steel in the control beams increased the cracking moment (Mcr) to 36%, while the fan palm beam of the same size and same concrete strength with a higher tensile ratio of 9.69% exhibited a lower Mcr of 11.86%. This shows a significant effect of reinforcing steel bars on concrete cracking compared to the fan palm. The averages of experimental to theoretical failure loads of fan palm to steel were compared with failure loads of 36.52kN to 20.19kN and 32kN to 19.78kN respectively. These results of the study based on the failure mode, and failure loads, highlights the suitability of using African fan palm as substitute reinforcing material for low rise buildings. Furthermore, the study also proposed minimum and maximum tensile ratio of 0.76% and 5.60% respectively for fan palm reinforced concrete beams based on the Indian Standard and modulus of elasticity of steel.

Enhancing concrete self-healing capabilities of Bacillus sphaericus spores through the encapsulation in biopolymeric microcapsules

This study examined the encapsulation of Bacillus sphaericus LMG 22257 spores in biopolymeric microcapsules (MCs) for use in cement mortar, with a focus on enhancing self-healing properties. Biopolymers, including alginate (ALG), chitosan (CTS), carboxymethyl cellulose (CMC), and ALG/CMC blends at various ratios, were employed to fabricate the MCs through ionotropic gelation and freeze–drying. The physicochemical properties of MCs, including size, morphology, and swelling behavior under simulated concrete conditions, were assessed. Among these, ALG/CMC-MCs exhibited superior characteristics and demonstrated the highest urea hydrolysis activity when incorporated into the mortar, indicating optimal spore protection. Despite an initial decrease in compressive strength, the ALG/CMC blend with an ALG:CMC mass ratio of 6:4 achieved a crack healing efficiency of 96.7% over 28 days under cyclic wet-dry conditions. These findings highlight the potential of biopolymer encapsulation for embedding functional microorganisms in construction materials, contributing to a more durable and sustainable infrastructure.

Designing a reliable machine learning system for accurately estimating the ultimate condition of FRP-confined concrete

Precisely forecasting how concrete reinforced with fiber-reinforced polymers (FRP) responds under compression is essential for fine-tuning structural designs, ensuring constructions fulfill safety criteria, avoiding overdesigning, and consequently minimizing material expenses and environmental impact. Therefore, this study explores the viability of gradient boosting regression tree (GBRT), random forest (RF), artificial neural network-multilayer perceptron (ANNMLP) and artificial neural network-radial basis function (ANNRBF) in predicting the compressive behavior of fiber-reinforced polymer (FRP)-confined concrete at ultimate. The accuracy of the proposed machine learning approaches was evaluated by comparing them with several empirical models concerning three different measures, including root mean square errors (RMSE), mean absolute errors (MAE), and determination coefficient (R2). In this study, the evaluations were conducted using a substantial collection of axial compression test data involving 765 circular specimens of FRP-confined concrete assembled from published sources. The results indicate that the proposed GBRT algorithm considerably enhances the performance of machine learning models and empirical approaches for predicting strength ratio of confinement (f′cc/f′co) by an average improvement in RMSE as 17.3%, 0.65%, 66.81%, 46.12%, 46.31%, 46.87% and 69.94% compared to RF, ANNMLP, ANNRBF, and four applied empirical models, respectively. It is also found that the proposed ANNMLP algorithm exhibits notable superiority compared to other models in terms of reducing RMSE values as 9.67%, 11.29%, 75.11%, 68.83%, 73.64%, 69.49% and 83.74% compared to GBRT, RF, ANNRBF and four applied empirical models for predicting strain ratio of confinement (εcc/εco), respectively. The superior performance of the GBRT and ANNMLP compared to other methods in predicting the strength and strain ratio confinements is important in evaluating structural integrity, guaranteeing secure functionality, and streamlining engineering plans for effective utilization of FRP confinement in building projects.

Report of RILEM TC 281-CCC: A critical review of the standardised testing methods to determine carbonation resistance of concrete

The chemical reaction between CO2 and a blended Portland cement concrete, referred to as carbonation, can lead to reduced performance, particularly when concrete is exposed to elevated levels of CO2 (i.e., accelerated carbonation conditions). When slight changes in concrete mix designs or testing conditions are adopted, conflicting carbonation results are often reported. The RILEM TC 281-CCC ‘Carbonation of Concrete with Supplementary Cementitious Materials’ has conducted a critical analysis of the standardised testing methodologies that are currently applied to determine carbonation resistance of concrete in different regions. There are at least 17 different standards or recommendations being actively used for this purpose, with significant differences in sample curing, pre-conditioning, carbonation exposure conditions, and methods used for determination of carbonation depth after exposure. These differences strongly influence the carbonation depths recorded and the carbonation coefficient values calculated. Considering the importance of accurately determining carbonation potential of concrete, not just for predicting their durability performance, but also for determining the amount of CO2 that concrete can re-absorb during or after its service life, it is imperative to recognise the applicability and limitations of the results obtained from different tests. This will enable researchers and practitioners to adopt the most appropriate testing methodologies to evaluate carbonation resistance, depending on the purpose of the conclusions derived from such testing (e. g. materials selection, service life prediction, CO2 capture potential).

Application of computer vision techniques to damage detection in underwater concrete structures

Underwater concrete structure crack detection and structural health condition assessment based on image processing is a challenging task. The complex underwater environment and severe image degradation seriously affect the accuracy of crack detection. To solve these problems, a monocular vision and image-enhanced fractal-based fractal science based on computer vision and image processing techniques are proposed to carry out a non-contact detection study of underwater concrete cracks. In this study, a four-level structural health condition was established to assist in underwater crack measurement and safety assessment. The box-counting method was used as a practical tool to calculate the fractal dimension. To verify the effective distance of the algorithm, three distances of 0.5 m,0.8 m, and 1.2 m were set. The results show that the method proposed in this study can effectively detect cracks in submerged concrete members within 0.6 m and help managers correctly determine the health of the structure using the fractal dimension.

The Material Politics of Graffiti Removal

While anti-graffiti policies have been examined in various places, the concrete conditions of graffiti removal remain largely unexplored. Based on ethnographic fieldwork, this article considers a generative dimension of graffiti removal techniques and highlights their contribution to the visual ordering of urban reality through two main actions: making differences and making similarities. Anything but automatic and simple, graffiti removal is a complex operation consisting of technical gestures and ontological re-specifications, which are accomplished within the material entanglements of the city’s surfaces. A matter of continuous transformations, graffiti removal participates, like graffiti writing, in the visual becoming of urban ecologies.

Corrosion Characteristics and Durability of Bio Concrete – A Review

Abstract Concrete, is a crucial construction material widely used in infrastructure, has inherent limitations despite its versatility. It exhibits constrained ductility, low resistance to cracking, and relatively weak tensile strength. Global research has continuously sought modifications to address these issues in conventional cement concrete. Periodic exposure to harmful substances can lead to concrete corrosion, potentially causing structural failures that impact long-term operational efficiency. Studies in the literature focus on enhancing corrosion resistance, with microbiologically induced calcite precipitation (MICP) being a notable outcome. Recent research highlights specific bacterial species, like Bacillus subtilis, capable of enhancing the durability and lifespan of concrete structures. However, the actual impact on strength and properties depends on factors such as concrete mix design, bacterial concentration, and environmental conditions. Ongoing research in Bacillus subtilis application is evolving, and actual data may vary based on specific experiments and conditions. For the latest information, referring to the current scientific literature is recommended. Microbial concrete holds promise in significantly extending infrastructure service life, reducing maintenance expenses, and enhancing structural safety. This study delves into the fundamental mechanism of microbiological concrete, offering insights into factors strengthening and prolonging concrete life while presenting limitations.

Influence of low vacuum and high temperature condition on moisture transport and dry shrinkage of mature concrete

Low vacuum and high temperature typically promote the evaporation of moisture and exacerbate concrete shrinkage cracking, which highlights the issue of volume stability for concrete materials in some engineering environments (e.g. the operational environment of ultra-high-speed train in the low-vacuum tube). To evaluate its impact on moisture transport and drying shrinkage of mature concrete, different air pressure and temperature conditions were selected and the correspondent experiments of investigating moisture loss, internal relative humidity, and drying shrinkage after exposure were conducted. And the evolution of different types of moisture, hydration degree, pore structure, and morphology of matrix was analyzed through microscopic testing methods. Experimental results present that compared to the standard drying condition, the moisture loss and drying shrinkage of concrete under low vacuum and high temperature condition increased by more than 48.5 % and 77.4 % respectively. There exists a strong correlation between moisture loss and drying shrinkage of matrix after low vacuum and high temperature exposure. Besides, compared to the sole effect of low vacuum, the coupling effect of low vacuum and high temperature has a more significant impact on the moisture transport and drying shrinkage of concrete. Among them, after prolonged exposure to low vacuum and high temperature, the internal relative humidity of matrix drops to 50 %∼60 %, and the corresponding critical aperture reduces to 1.0–1.5 nm, thereby significantly increasing the shrinkage stress of concrete. Furthermore, low vacuum and high temperature condition reduces the content of capillary water, gel water, and chemically combined water, and leads to the loss of moisture in smaller pores. In addition, for mature concrete, low vacuum and high temperature condition not only impedes cement hydration but also induces the coarsening of pore structure, resulting in an increase in the number of macropores and pores around 50 nm, which correlates highly with the evolution of drying shrinkage.

Computer Vision Method for Automatic Detection of Microstructure Defects of Concrete.

The search for structural and microstructural defects using simple human vision is associated with significant errors in determining voids, large pores, and violations of the integrity and compactness of particle packing in the micro- and macrostructure of concrete. Computer vision methods, in particular convolutional neural networks, have proven to be reliable tools for the automatic detection of defects during visual inspection of building structures. The study's objective is to create and compare computer vision algorithms that use convolutional neural networks to identify and analyze damaged sections in concrete samples from different structures. Networks of the following architectures were selected for operation: U-Net, LinkNet, and PSPNet. The analyzed images are photos of concrete samples obtained by laboratory tests to assess the quality in terms of the defection of the integrity and compactness of the structure. During the implementation process, changes in quality metrics such as macro-averaged precision, recall, and F1-score, as well as IoU (Jaccard coefficient) and accuracy, were monitored. The best metrics were demonstrated by the U-Net model, supplemented by the cellular automaton algorithm: precision = 0.91, recall = 0.90, F1 = 0.91, IoU = 0.84, and accuracy = 0.90. The developed segmentation algorithms are universal and show a high quality in highlighting areas of interest under any shooting conditions and different volumes of defective zones, regardless of their localization. The automatization of the process of calculating the damage area and a recommendation in the "critical/uncritical" format can be used to assess the condition of concrete of various types of structures, adjust the formulation, and change the technological parameters of production.

Advances in Modeling Surface Chloride Concentrations in Concrete Serving in the Marine Environment: A Mini Review

Chloride corrosion is a key factor affecting the life of marine concrete, and surface chloride concentration is the main parameter for analyzing its durability. In this paper, we first introduce six erosion mechanism models for surface chloride ion concentration, reveal the convection effect in the diffusion behavior of chloride ions, and then introduce the corrosion mechanisms that occur in different marine exposure environments. On this basis, the analysis is carried out using empirical formulations and machine learning methods, which provides a clearer understanding of the research characteristics and differences between empirical formulas and emerging machine learning techniques. This paper summarizes the time-varying model and multifactor coupling model on the basis of empirical analysis. It is found that the exponential function and the reciprocal function are more consistent with the distribution law of chloride ion concentration, the multifactor model containing the time-varying law is the most effective, and the Chen model is the most reliable. Machine learning, as an emerging method, has been widely used in concrete durability research. It can make up for the shortcomings of the empirical formula method and solve the multifactor coupling problem of surface chloride ion concentration with strong prediction ability. In addition, the difficulty of data acquisition is also a major problem that restricts the development of machine learning and incorporating concrete maintenance conditions into machine learning is a future development direction. Through this study, researchers can systematically understand the characteristics and differences of different research methods and their respective models and choose appropriate techniques to explore the durability of concrete structures. Moreover, intelligent computing will certainly occupy an increasingly important position in marine concrete research.

Concrete aging factor prediction using machine learning

Accurate prediction of concrete aging factor is pivotal for performance-based durability reinforced concrete design. This study introduces an innovative method leveraging machine learning techniques, employing seven algorithms: Bagging, Random Forest, AdaBoost, Gradient Boosting, XGBoost, CatBoost, and LightGBM. The dataset comprises 130 instances with seven input features describing cement type, cement content, pozzolan type, pozzolan content, w/b ratio, exposure condition, and age of concrete. Seventy models were trained across five scenarios, categorized into two groups: Group I using all features of the raw dataset, and Group II incorporating engineered features. Model performance, assessed by mean-absolute error (MAE), mean-square error (MSE), root-mean-square error (RMSE), and coefficient of determination (R²), reveals superior performance in Group II compared to Group I. Notably, the LightGBM algorithm in Scenario III outperforms all models with a remarkable MAE of 0.110, MSE of 0.018, RMSE of 0.133, and R² of 0.818. Subsequently, models from Scenarios V and IV exhibit strong performance. The implemented machine learning models demonstrate notable generalizability, effectively capturing feature interrelations without the need for resource-intensive experimental testing.

A Review on Utilization of recycled Aggregate in concrete: Examining the effects of incorporating recycled concrete aggregate or other recycled materials as substitute for natural aggregate in concrete mixtures

In developed countries, concrete is mostly observed as a valuable resource. Recycling efforts have highlighted the importance of maintaining the required compressive strength when using old concrete in new construction. Research shows that compressive strength depends on factors like adhered mortar, water absorption, abrasion resistance, aggregate size, original concrete strength, curing time, replacement ratio, transition zone between old and new concrete, moisture level, impurities, and environmental conditions. While some studies have suggested methods for adopting recycled aggregates into concrete mixes, there's a need for a simple and environmentally friendly approach that considers the percentage of adhered mortar. This project aims to review existing literature on using recycled concrete as aggregates, focusing on compressive strength, and propose a method for incorporating recycled concrete aggregate while maintaining strength. Keywords: Construction and demolished concrete, second generation concrete, Adhere, recycling, workability, recycle aggregate, compressive strength

Enhancing thermal performance of energy storage concrete through MPCM integration: An experimental study

Phase change materials melt and solidify at different temperature ranges called Phase change hysteresis. This phenomenon should be understood and evaluated in order to adequately design energy storage concretes for applications. Although many studies have analyzed the phase change process in pure - phase change materials such as paraffin. However, there is not enough information about the Phase change hysteresis of energy storage concrete. In order to realistically reproduce the working condition of concrete. This study explores phase change hysteresis in energy storage concrete slabs, focusing on the impact of microcapsule concentration and temperature change rate on thermal efficiency. Experiments were conducted to analyze the thermophysical properties, particularly observing the variations in specific heat capacity and phase transition temperatures. Results showed that increasing the microcapsule concentration enhances the specific heat capacity and latent heat of phase change, while faster temperature changes intensify hysteresis effects. Compared with the results of previous studies. Phase change hysteresis is more significant due to the pore and complex structure of energy storage concrete that impedes heat transfer. The experimental latent heat values were approximately 75 % of theoretical predictions, likely due to microcapsule damage and microstructural impacts on heat transfer. The study's findings are crucial for optimizing the thermal performance of energy storage concrete.

Flexural and Bond Behavior of Concrete Beams Strengthened with Carbon Fiber-Reinforced Polymer Sheet

Concerns have been expressed regarding the issue of using conventional steel rebar in civil engineering structures. One contributing factor is the corrosion that occurred on the steel rebar. The corrosion process was not promptly detected due to the gradual reaction time exhibited by the steel, concrete, and environmental conditions. Corrosion problems often manifest when corrosion has progressed to a critical point and damaged the concrete structure. Consequently, to address this issue, this research proposed the application of carbon-fiber reinforced polymer sheet (CFRP). The aim is to measure the flexural load of conventional and FRP-reinforced concrete beams, to test the effects of cracking and deflection on concrete beams strengthened with FRP sheet, and to determine the degree to which these loads damaged these beams. A repair method was implemented by applying two distinct diameters of CFRP: 50 mm × 100 mm and 100 mm × 200 mm. The finding shows that the concrete beam's significantly higher flexural strength resistance was observed for CFRP of greater dimensions. This is mostly because the CFRP can disperse the applied stress more effectively. Alternative applications of fibre-reinforced polymer should be explored for further research to facilitate the development of substitutes for CFRP.

Detection of Destructive Processes and Assessment of Deformations in PP-Modified Concrete in an Air-Dry State and Exposed to Fire Temperatures Using the Acoustic Emission Method, Numerical Analysis and Digital Image Correlation.

This article presents the results of tests carried out to assess the condition of PP-modified concrete. The tests were carried out on samples previously stored at ambient temperature and exposed to temperatures corresponding to fire conditions-300 °C, 450 °C, and 600 °C. Axial compression tests of cube-shaped samples and three-point bending of beams were carried out. During strength tests, acoustic emission (AE) signals were recorded and the force and deformation were measured. Recorded AE events were clustered using the k-means algorithm. The analysis of the test results allowed for the identification of signals characteristic of the individual stages of the material destruction process. Differences in the methods of destruction of samples stored in ambient conditions and those exposed to fire temperatures were also indicated. While loading the samples, measurements were carried out using the digital image correlation (DIC) method, which enabled the determination of displacements. Based on the results of the laboratory tests, a numerical model was developed. The results obtained using different research methods (DIC and FEM) were compared. Tomographic examinations and observations of the microstructure of the tested materials were also carried out. The analyses carried out allowed for a reliable assessment of the possibility of using the acoustic emission method to detect destructive processes and assess the technical condition of PP-modified concrete. It was confirmed that the acoustic emission method, due to differences at low load levels, can be a useful technique for assessing the condition of PP-modified concrete after exposure to fire temperatures. So far, no research directions in a similar field have been identified.

Testing of Concrete Structures with Non-Destructive Test Method (NDT) Using Ultrasonic Pulse Velocity (UPV) at the Building on the Ancol Beach

The UPV test procedure with the PUNDIT device is set based on the concept of the wave flow speed passing through a solid object bound to the elastic properties of a tangible medium. When used properly and correctly, this tool will provide a lot of information about the condition of the surface or the inside of the concrete. Classification for pulse velocity results according to the speed criteria of concrete quality presented in BS 1881: Section 203:1986 as follows: 4500 m/s = excellent concrete condition, 3400 – 4500 m/s = good concrete conditions, 3000-3500 m/s = medium concrete situation, < 3000 m/ s = concrete problem condition (doubtful concrete condition). The pulse velocity test results are measured by three methods: direct transmission, semi-direct transmission and indirect or surface transmission. The test results for the structure of the beams, cylinders, tie beams, and floor plates of the office building on the edge of the anchor showed that it is in medium concrete condition. The average value of the estimated quality of concrete results of the test UPV Pundit for Beams was 25,29MPa, Slabs was 25,17MPa, Tie Beam was 25,06MPa, and Pilecap was 25,46MPa. The result has met the minimum requirement for concrete solid pressure of 21MPa for the quality of unique structures concrete according to SNI-2847-2019.