Concrete Structures Research Articles

A data-driven prediction for concrete crack propagation path based on deep learning method

Accurate and efficient prediction of concrete crack propagation path facilitates timely maintenance. To avoid errors arising from assumptions in physical models and parameter measurements, a data-driven prediction method for concrete crack propagation path is proposed. The spatio-temporal prediction approach is presented by unifying the conditional Generative Adversarial Network and Long Short-Term Memory Network (UcGLS-Net). The UcGLS-Net has the advantage of enabling temporal (by Long Short-Term Memory Network, LSTM) and spatial information (by the conditional Generative Adversarial Network, cGAN) in the concrete crack propagation process to be treated simultaneously and distinctly thus improving prediction accuracy. Notably, results demonstrate that UcGLS-Net still works in crack propagation path prediction even with unknown aggregate distribution. Moreover, the effects of historical input quantity and prediction intervals on prediction accuracy of the UcGLS-Net are studied and the network framework is optimized. Due to high efficiency, this study provides a new adaptable and real-time prediction method for concrete crack propagation path, offering support for the design, damage prediction, assessment, and maintenance of concrete structures.

Graphene reinforced cement-based triboelectric nanogenerator for efficient energy harvesting in civil infrastructure

This paper investigated a graphene reinforced cement-based triboelectric nanogenerator (TENG) aimed at harvesting mechanical energies in infrastructure, such as pedestrians, vehicles, human-induced vibrations, and natural stimulus like wind and earthquakes. The triboelectric layers of the cement-based TENG consisted of a fully cured graphene modified cement-based plate and a polytetrafluoroethylene (PTFE) film, which were tested under a contact-separation mode. Microstructural analysis indicated that the graphene was well-dispersed in the cementitious matrix, and the graphene-cement composites achieved excellent compressive and flexural strengths of 53.0 and 3.5 MPa, respectively. The electrical characteristics of the graphene-cement composites, specifically their resistivity and impedance, showed that they did not reach the percolation threshold, making them ideal dielectric materials with a dielectric constant of 100 at 1 kHz. The performance of the cement-based triboelectric nanogenerator (TENG) varied depending on the amplitude and frequency of the contact-separation cycle. At a frequency of 10 Hz and under a force of 100 N, the short-circuit current and open-circuit voltage peaked at 3.62 µA and 279.4 V, respectively, achieving a maximum power density of 95 mW/m2 with a 100 MΩ resistor. In practical applications, this TENG charged a 10 μF capacitor to 3.1 V within one minute and to 57.2 V in one hour. Additionally, manual operation of the TENG enabled the lighting of 29 LEDs with one minute of hand pressure. By utilizing triboelectric effects, the results provide the feasibility of self-powering concrete structures and pavements for future smart cities.

Efficacy of sustainable cementitious materials on concrete porosity for enhancing the durability of building materials

Abstract The degradation of concrete structures is significantly influenced by water penetration since water serves as the primary vehicle for the movement of harmful compounds. The process of capillary water absorption is widely recognized as a crucial indicator of durability for unsaturated concrete, as it allows dangerous substances to enter the composite material. The water absorption capacity of concrete is intricately linked to its pore structure, as concrete is inherently porous. The main goal of this work is to create an innovative predictive tool that assesses the porosity of concrete by analyzing its components using a machine-learning (ML) framework. Seven distinct batch design variables were included in the generated database: fly ash, superplasticizer, water-to-binder ratio, curing time, ground granulated blast furnace slag, binder, and coarse-to-fine aggregate ratio. Four distant ML algorithms, including AdaBoost, linear regression (LR), decision tree (DT), and support vector machine (SVM), are utilized to infer the generalization capabilities of ML algorithms to estimate concrete porosity accurately. The RReliefF algorithm was implemented to calculate the significant features influencing porosity. This study concludes that in comparison to the alternative techniques, the AdaBoost method demonstrated superior performance with an R 2 score of 0.914, followed by SVM (0.870), DT (0.838), and LR (0.763). The results of the evaluation of RReliefF indicated that the binder possesses a remarkable influence on the porosity of concrete.

Synergising expertise towards sustainability and robustness of cement-based materials and concrete structures

Synergising expertise towards sustainability and robustness of cement-based materials and concrete structures

Seismic experiment and performance analysis on embedded optimized steel plate-reinforced concrete composite shear wall under multi-dimensional loading

In order to investigate the seismic performance of reinforced concrete shear walls under multi-dimensional loading mode and its effect on performance improvement, an embedded optimized steel plate-reinforced concrete composite shear wall is proposed. Based on the design principle that the shear wall could adequately bear multi-dimensional loading and the embedded steel plate could reach the full stress state, the X-shaped optimized steel plate (for in-plane loading mode) and the triangular optimized steel plate (for out-of-plane loading mode) are determined using different optimization methods. The combination scheme of these two plates is utilized in the oblique loading mode. Quasi-static loading tests are conducted on eight typical shear wall specimens, and performance parameters such as hysteresis curve, skeleton curve, ductility, stiffness degradation, strain evolution, and damage evaluation of the specimens are compared and analyzed. In addition, variable parameter analysis is performed using finite element software to compare the strain distribution state of each steel plate. The results indicate that the embedded optimized steel plate-reinforced concrete composite shear wall structure exhibits higher bearing capacity, greater deformation capacity, and superior energy dissipation capacity under different loading angles compared to the tradition reinforced concrete shear walls. This composite structure can provide greater lateral stiffness, and the optimized steel plate can reach the full stress state at all loading angles, effectively reducing the damage of steel bars and concrete. These findings offer a foundation for the study of seismic performance and performance improvement methods for shear wall structures under multi-dimensional earthquake action.

Editorial: High-performance and sustainable concrete materials and structures

Editorial: High-performance and sustainable concrete materials and structures

A Review on Research Progress of Corrosion Resistance of Alkali-Activated Slag Cement Concrete.

China is the largest producer and user of Ordinary Silicate Cement (OPC), and rapid infrastructure development requires more sustainable building materials for concrete structures. Portland cement emits large amounts of CO2 in production. Given proposals for "carbon peaking and carbon neutralization", it is extremely important to study alternative low-carbon cementitious materials to reduce emissions. Alkali-activated slag (AAS) cement, a new green cementitious material, has high application potential. The chemical corrosion resistance of AAS concrete is important for ensuring durability and prolonging service life. This paper reviews the hydration mechanism of AAS concrete and discusses the composition of hydration products on this basis, examines the corrosion mechanism of AAS concrete in acid, sulfate, and seawater environments, and reviews the impact of its performance due to the corrosion of AAS concrete in different solutions. Further in-depth understanding of its impact on the performance of concrete can provide an important theoretical basis for its use in different environments and provides an important theoretical basis for the application of AAS concrete, so that we can have a certain understanding of the durability of AAS concrete.

Concrete's fire resistance improvement with waste glass and ceramic aggregates

AbstractEven though concrete structures are safer than steel structures in terms of fire resistance, the risk exists in concrete structures by spalling or exploding, especially in high-strength concrete. This study aims to produce a particular type of concrete using waste ceramics as fine aggregate and waste glass as coarse aggregate and compare data with normal aggregate concrete. Studies show that using waste ceramic and glass increases the fire resistance of concrete. After fire exposure in the control mix, the residual compressive strength was 10 MPa. The waste aggregate concrete was found to be 26.9 MPa after 800 centigrade exposures, which was an excellent result. Waste materials decreased construction costs and led to a clean environment.

Concrete crack classification based on fourier image enhancement and convolutional neural network

This paper investigates the application of Fourier image enhancement combined with Convolutional Neural Networks (CNNs) for detecting cracks in concrete structures. Fourier enhancement is used to preprocess crack images, improving their clarity and reducing noise, which in turn enhances the performance of the CNN in accurately classifying cracks. The results demonstrate that this combination improves the classification accuracy, with the enhanced images achieving a higher accuracy compared to non-enhanced images. Additionally, the study examines the effects of this preprocessing on CNN training time. However, accuracy varies depending on the dataset used, with one dataset reaching a maximum accuracy of 95% after enhancement. These findings highlight the potential of using frequency-domain image enhancement techniques in conjunction with deep learning models for structural health monitoring.

Machine Learning Ensemble Methodologies for the Prediction of the Failure Mode of Reinforced Concrete Beam–Column Joints

One of the most critical aspects in the seismic behavior or reinforced concrete (RC) structures pertains to beam–column joints. Modern seismic design codes dictate that, if failure is to occur, then this should be the ductile yielding of the beam and not brittle shear failure of the joint, which can lead to sudden collapse and loss of human lives. To this end, it is imperative to be able to predict the failure mode of RC joints for a large number of structures in a building stock. In this research effort, various ensemble machine learning algorithms were employed to develop novel, robust classification models. A dataset comprising 486 measurements from real experiments was utilized. The performance of the employed classifiers was assessed using Precision, Recall, F1-Score, and overall Accuracy indices. N-fold cross-validation was employed to enhance generalization. Moreover, the obtained models were compared to the available engineering ones currently adopted by many international organizations and researchers. The novel ensemble models introduced in this research were proven to perform much better by improving the obtained accuracy by 12–18%. The obtained metrics also presented small variability among the examined failure modes, indicating unbiased models. Overall, the results indicate that the proposed methodologies can be confidently employed for the prediction of the failure mode of RC joints.

Evaluating the Performance of Ensemble Machine Learning Algorithms Over Traditional Machine Learning Algorithms for Predicting Fire Resistance in FRP Strengthened Concrete Beams

In recent years, fiber-reinforced polymers (FRP) have emerged as a highly effective solution for strengthening reinforced concrete (RC) structures. However, accurately assessing the fire resistance of FRP-strengthened members remains a significant challenge due to the limited guidance available in current building codes, often leading to conservative and cost-intensive evaluations. Experimental testing and numerical analysis required for such assessments are resource-demanding, highlighting the need for more efficient methods. This study investigates the application of machine learning (ML) techniques to predict the fire resistance of FRP-strengthened RC beams. Twelve ML models, including eight ensemble methods and four traditional approaches, were employed. The models were trained using a comprehensive dataset comprising over 21,000 data points obtained from numerical simulations and experimental tests. The dataset captured variations in geometric configurations, insulation strategies, loading conditions, and material properties. To enhance predictive accuracy, Bayesian optimization and k-fold cross-validation were applied for model tuning, while the Shapley Additive Explanations (SHAP) method was utilized to assess the relative importance of features influencing fire resistance. Among the models tested, Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Light Gradient Boosting (LGBoost), and Gradient Boosting (GRB) demonstrated superior performance, achieving accuracy rates exceeding 92%. Key factors identified as significantly affecting fire resistance included loading ratio, area of tensile reinforcement, insulation depth, concrete cover thickness, and FRP area. The findings underscore the potential of ensemble ML techniques over traditional methods for accurately predicting the fire resistance of FRP-strengthened RC beams, offering critical insights for optimizing design practices and enhancing structural fire safety.

Study on non-destructive visualization identification of concrete internal cracks based on SH array ultrasound

Ultrasonic nondestructive testing (NDT) technology was one of the most frequently used and fastest-developing NDT techniques in detection field. This method had high detection sensitivity, more accurate defect localization, and was harmless to the human body. With the development of ultrasonic testing technology, modern ultrasonic imaging technology generally had automatic data acquisition and processing functions, which allowed for intuitive and clear recognition of internal defects by the human eyes. The overall purpose of this study was to visualize the cracks in concrete by synthetic aperture focusing technique (SAFT) and full-waveform inversion (FWI) algorithms on the basis of ultrasonic nondestructive testing technology. At present, there have been studies on the use of ultrasonic instruments to detect the internal defects of concrete. However, the accuracy and authenticity of this method have not been compared in previous studies. Therefore, in this experiment, four concrete specimens with different buried depths, angles, shapes, and thicknesses of cracks were designed. Based on SH ultrasonic wave NDT technology, two types of operations were performed on the collected ultrasonic wave data: one was the application of SAFT method, and the other was the first application of FWI method for detecting internal cracks in concrete. The study found that both algorithms were most accurate in determining the burial depth of cracks, with error results not exceeding 10 %. They also showed good recognition effects for the angle, shape, and thickness of the buried cracks. This proved that both identification methods had the capability to recognize internal cracks. The Introview software bundled with the ultrasonic instrument could identify cracks using SAFT directly and quickly. When FWI was used with MATLAB computation, the results were more precise and intuitive. The overall purpose of this study was to detect cracks in concrete by SAFT and FWI algorithms on the basis of ultrasonic nondestructive testing technology. Combining the two crack identification techniques could be of great significance for ensuring the long-term safety and stability of concrete structures, providing strong support for structural maintenance and reinforcement.

Study on the chloride ion binding rate of sulfoaluminate cement mortars containing different mineral admixtures

In this study, the chloride ion (Cl−) binding rate of sulfoaluminate cement (SAC) mortars containing different mineral admixtures was investigated. This is essential to improve the durability of concrete structures in high Cl− environments, especially where they are susceptible to Cl− attack such as coastlines and marine structures. The effects of Cl− concentration, curing age, and the type and amount of mineral admixture on the Cl− binding rate of SAC mortars were analyzed. It was found that the content of water-soluble Cl− in SAC mortars decreased with the increase of curing age, while the Cl− binding ratio increased accordingly, indicating that its resistance to internal Cl− permeation increased. The addition of fly ash (FA) and ground granulated blast furnace slag (GGBS) can significantly improve the Cl− binding rate of SAC mortars, and the Cl− binding rate increases to 46.6% with 20% of FA and 38.7% with 40% of GGBS. The effects of mineral admixtures on the microstructure and phase composition of SAC mortars were further investigated by X-ray diffraction (XRD) analysis. The results showed that the addition of FA and GGBS promoted the formation of C-S-H (calcium silicate hydrate) gels and improved the resistance of SAC mortars to Cl− penetration. On the other hand, the excessive addition of silica fume (SF) decreased the Cl− binding rate, whereas a moderate amount of limestone powder (LP) improved the Cl− binding rate. The study of the Cl− binding rate of SAC mortars can help to evaluate their resistance to Cl− erosion in real projects, thus guiding the optimization of concrete formulations and the improvement of durability.

Reduction in Floor Impact Noise Using Resilient Pads Composed of Machining Scraps.

Floor impact noise is a significant social concern to secure a quiescent living space for multi-story building residents in South Korea. The floating floor, consisting of a concrete structure on resilient pads, is a specifically designed system to minimize noise transmission. This floating structure employs polymeric pads as the resilient materials. In this study, we investigated the utilization of helically shaped machining scraps as a resilient material for an alternative approach to floor noise reduction. The dynamic elastic modulus and loss factor of the scrap pads were measured using the vibration test method. The scrap pads exhibited a low dynamic elastic modulus and a high loss factor compared to the polymeric pads. Heavyweight impact sound experiments in an actual building were conducted to evaluate the noise reduction performance. The proposed pads showed excellent performance on the reduction in the structure-borne vibration of the concrete slab and resulting sound generation. The analytical model was used to simulate the response of the floating floor structure, enabling a parametric study to examine the effects of the resilient layer viscoelastic properties. Both experimental and analytical evidence confirmed that the proposed scrap pads contribute to the development of sustainable solutions for the minimization of floor impact noise.

Interpretable machine‐learning models for predicting creep recovery of concrete

AbstractCreep recovery of concrete is essential for accurately assessing the performance of concrete structures over service time. Existing creep recovery models exhibit low accuracy, and the influencing factors of creep recovery remain inadequately elucidated. In this paper, interpretable machine learning (ML) techniques were employed to develop a prediction model for concrete creep recovery. Several ML techniques were selected including random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost) and light gradient boosting machine (LGBM). In order to maximize the sample size of the dataset, 109 sets of creep recovery data were collected from existing literatures for model training. Feature selection is utilized to determine the input parameters for ML models, and 12 input variables were selected. The model is fine‐tuned using Bayesian optimization techniques. To ensure the reliability of ML models, 10‐fold cross‐validation and random data splitting were implemented. The results indicate that the ML models exhibited higher accuracy compared to the existing creep recovery model. Among these ML models, LGBM demonstrated superior accuracy, efficiency and stability (with R2 = 0.993, 0.978, and 0.973 for the training, testing, and validation sets, respectively). Shapley additive explanations (SHAP) were employed to interpret the significance of each input parameter on ML model prediction. Duration after unloading, stress magnitude, and ambient relative humidity were the main feature variables influencing concrete creep recovery. Upon comparing the influencing factors, it was discerned that there exists a distinct difference between creep and creep recovery of concrete.

Long-Term Comparative Life Cycle Assessment, Cost, and Comfort Analysis of Heavyweight vs. Lightweight Construction Systems in a Mediterranean Climate

Massive construction systems have always characterized traditional architecture and are currently the most prevalent, straightforward, and cost-effective in many Mediterranean countries. However, in recent years, the construction industry has gradually shifted towards using lightweight, dry construction techniques. This study aims to assess the effects on energy consumption, comfort levels, and environmental sustainability resulting from the adoption of five high-performance construction systems in a multi-family residential building: (i) reinforced concrete structure with low-transmittance thermal block infill; (ii) reinforced concrete structure with light-clay bricks and outer thermal insulation; (iii) steel frame; (iv) cross-laminated timber (CLT); (v) timber-steel hybrid structure. To achieve this goal, a multidisciplinary approach was employed, including the analysis of thermal parameters, the evaluation of indoor comfort through the adaptive model and Fanger’s PMV, and the quantification of environmental and economic impacts through life cycle assessment and life cycle cost applied in a long-term analysis (ranging from 30 to 100 years). The results highlight that heavyweight construction systems are the most effective in terms of comfort, cost, and long-term environmental impact (100 years), while lightweight construction systems generally have higher construction costs, provide lower short-term environmental impacts (30 years), and offer intermediate comfort depending on the thermal mass.

Modeling of temperature deformations on the Dnister HPP dam (Ukraine)

Abstract The extended operation of Ukrainian hydroelectric power plants has caused spatial deformations and aging of hydrostructures. This can lead to man-made disasters, especially after explosions resulting from missile attacks. As a result, various defects can appear in the concrete structures of the dam. Continuous structural monitoring of hydropower plants is essential to prevent man-made accidents. The research paper also demonstrates that changes in water temperature can affect the dam’s vertical and horizontal displacements. A model was developed and analysed to determine the spatial deformations of the Dnister HPP based on water temperature and distance from the dam’s edge. It was determined that horizontal displacement rates range from −2.2 to 2.7 mm/month, and vertical displacement rates range from −2 to 1.3 mm/month. They are also seasonal in nature. The studies conducted enable the prediction and identification of seasonal spatial deformations of the Dnister HPP dam. The research also helps to find their variations due to water temperature at the definite depth. If the temperature deformation model deviates from the measurement results, the structure in those areas should be more thoroughly inspected and analysed. This may indicate the structure’s weakening due to natural aging processes or construction-related deficiencies. Deformations in the dam’s structure can lead to cracks, compromising its stability and watertightness. Dams can vary in shape, design features, depth, volume, and water temperature. So, it is important to customize the displacement model for each dam. Any deviation from the specified model may suggest defects due to abnormal temperature distribution and spatial displacements.

A solution for the automatic detection of expansion joints in dam stilling pools using underwater robots

This research addresses the critical challenges of detecting and measuring underwater concrete structure expansion joints (CSEJ), with a particular focus on dam stilling basin environments. A full set of algorithms for detecting expansion joints with underwater robots is presented by the research, enabling real-time and offline analysis along with visualization. These algorithms introduce the use of Convolutional Neural Networks (CNNs) for image feature extraction and segmentation in CSEJ detection, significantly improving accuracy and adaptability over traditional methods. An automated approach known as the Underwater Dam Crack Detection Network (UDCD Network) was introduced by this research. And advanced attention gates and hybrid dilated convolutions to enhance its detection capabilities are incorporated in this system. Additionally, a three-phase deep transfer learning strategy during training was utilized in this research, which significantly improves the model's performance. The research suggests an underwater image restoration algorithm that uses a dark channel prior and dynamic white balance correction to improve image clarity. Furthermore, unique underwater environmental challenges were tackled by this research, such as non-contact measurement of expansion joint widths using a view-geometry-based algorithm and comprehensive detection of dam wall surfaces. Experimental validation demonstrates that the advanced imaging technologies of the proposed underwater robots hold significant potential for autonomous detection of expansion joints in dam stilling basins.

Integrated Estimation of Stress and Damage in Concrete Structure Using 2D Convolutional Neural Network Model Learned Impedance Responses of Capsule-like Smart Aggregate Sensor.

Stress and damage estimation is essential to ensure the safety and performance of concrete structures. The capsule-like smart aggregate (CSA) technique has demonstrated its potential for detecting early-stage internal damage. In this study, a 2 dimensional convolutional neural network (2D CNN) model that learned the EMI responses of a CSA sensor to integrally estimate stress and damage in concrete structures is proposed. Firstly, the overall scheme of this study is described. The CSA-based EMI damage technique method is theoretically presented by describing the behaviors of a CSA sensor embedded in a concrete structure under compressive loadings. The 2D CNN model is designed to learn and extract damage-sensitive features from a CSA's EMI responses to estimate stress and identify damage levels in a concrete structure. Secondly, a compression experiment on a CSA-embedded concrete cylinder is carried out, and the stress-damage EMI responses of a cylinder are recorded under different applied stress levels. Finally, the feasibility of the developed model is further investigated under the effect of noises and untrained data cases. The obtained results indicate that the developed 2D CNN model can simultaneously estimate stress and damage status in the concrete structure.

Exploring the potential of rigid polyurethane foam waste in structural lightweight concrete

The structural lightweight concrete is gaining significant interest in the research community due to its reduced dead load. The reduction in the self-weight not only lowers the gravity load but can also lower the seismic demands for the structural members and hence can potentially reduce the overall cost. This study examines the feasibility of developing a sustainable structural lightweight concrete by utilizing rigid polyurethane foam waste as coarse aggregates (5–10 mm). The silica fume was incorporated as a cement replacement to enhance the compressive strength of the mix. A total of three composite mix formulations consisting of polyurethane foam, cement-coated polyurethane foam, and pumice (control specimens) as coarse aggregates were tested. The testing was performed for workability, compressive strength, flexural strength, elastic modulus, absorption, permeable voids, chloride-ion penetration, freeze and thaw resistance, and drying shrinkage. The results achieved for elastic modulus, chloride ion penetrability, and drying shrinkage, were meeting the minimum requirements of the standards making this composite suitable for the structural application. The results revealed that uncoated polyurethane based lightweight aggregate concrete resulted in an air-dry density of 1829 kg/m3 with the corresponding compressive strength of 19.5 MPa and flexural strength of 2.78 MPa at 28 days. Besides, the polyurethane-based lightweight concrete also showed lower internal structure damage against freeze and thaw action, along with improved thermal conductivity.