Deformation control constitutes one of the main technological challenges in three dimensional (3D) concrete printing, and it presents a challenge that must be addressed to achieve a precise and reliable construction process. Model-based information of the expected deformations and stresses is required to optimize the construction process in association with the specific properties of the concrete mix. In this work, a novel thermodynamically consistent finite strain constitutive model for fresh and early-age 3D-printable concrete is proposed. The model is then used to simulate the 3D concrete printing process to assess layer shapes, deformations, forces acting on substrate layers and prognoses of possible structural collapse during the layer-by-layer buildup. The constitutive formulation is based on a multiplicative split of the deformation gradient into elastic, aging and viscoplastic parts, in combination with a hyperelastic potential and considering evolving material properties to account for structural buildup or aging. One advantage of this model is the stress-update-scheme, which is similar to that of small strain plasticity and therefore enables an efficient integration with existing material routines. The constitutive model uses the particle finite element method, which serves as the simulation framework, allowing for modeling of the evolving free surfaces during the extrusion process. Computational analyses of three printed layers are used to create deformation plots, which can then be used to control the deformations during 3D concrete printing. This study offers further investigations, on the structural level, focusing on the potential structural collapse of a 3D printed concrete wall. The capability of the proposed model to simulate 3D concrete printing processes across the scales—from a few printed layers to the scale of the whole printed structure—in a unified fashion with one constitutive formulation, is demonstrated.
Identifying crack and predicting crack propagation are critical processes for the risk assessment of engineering structures. Most traditional approaches to crack modeling are faced with issues of high computational costs and excessive computing time. To address this issue, we explore the potential of deep learning (DL) to increase the efficiency of crack detection and forecasting crack growth. However, there is no single algorithm that can fit all data sets well or can apply in all cases since specific tasks vary. In the paper, we present DL models for identifying cracks, especially on concrete surface images, and for predicting crack propagation. Firstly, SegNet and U-Net networks are used to identify concrete cracks. Stochastic gradient descent (SGD) and adaptive moment estimation (Adam) algorithms are applied to minimize loss function during iterations. Secondly, time series algorithms including gated recurrent unit (GRU) and long short-term memory (LSTM) are used to predict crack propagation. The experimental findings indicate that the U-Net is more robust and efficient than the SegNet for identifying crack segmentation and achieves the most outstanding results. For evaluation of crack propagation, GRU and LSTM are used as DL models and results show good agreement with the experimental data.
In engineering applications, concrete crack monitoring is very important. Traditional methods are of low efficiency, low accuracy, have poor timeliness, and are applicable in only a limited number of scenarios. Therefore, more comprehensive detection of concrete damage under different scenarios is of high value for practical engineering applications. Digital image correlation (DIC) technology can provide a large amount of experimental data, and neural network (NN) can process very rich data. Therefore, NN, including convolutional neural networks (CNN) and back propagation neural networks (BP), can be combined with DIC technology to analyze experimental data of three-point bending of plain concrete and four-point bending of reinforced concrete. In addition, strain parameters can be used for training, and displacement parameters can be added for comprehensive consideration. The data obtained by DIC technology are grouped for training, and the recognition results of NN show that the combination of strain and displacement parameters, i.e., the response of specimen surface and whole body, can make results more objective and comprehensive. The identification results obtained by CNN and BP show that these technologies can accurately identify cracks. The identification results for reinforced concrete specimens are less affected by noise than those of plain concrete specimens. CNN is more convenient because it can identify some features directly from images, recognizing the cracks formed by macro development. BP can issue early warning of the microscopic cracks, but it requires a large amount of data and computation. It can be seen that CNN is more intuitive and efficient in image processing, and is suitable when low accuracy is adequate, while BP is suitable for occasions with greater accuracy requirements. The two tools have advantages in different situations, and together they can play an important role in engineering monitoring.
Reinforced concrete (RC) flat slabs, a popular choice in construction due to their flexibility, are susceptible to sudden and brittle punching shear failure. Existing design methods often exhibit significant bias and variability. Accurate estimation of punching shear strength in RC flat slabs is crucial for effective concrete structure design and management. This study introduces a novel computation method, the jellyfish-least square support vector machine (JS-LSSVR) hybrid model, to predict punching shear strength. By combining machine learning (LSSVR) with jellyfish swarm (JS) intelligence, this hybrid model ensures precise and reliable predictions. The model’s development utilizes a real-world experimental data set. Comparison with seven established optimizers, including artificial bee colony (ABC), differential evolution (DE), genetic algorithm (GA), and others, as well as existing machine learning (ML)-based models and design codes, validates the superiority of the JS-LSSVR hybrid model. This innovative approach significantly enhances prediction accuracy, providing valuable support for civil engineers in estimating RC flat slab punching shear strength.
The derivation and validation of analytical equations for predicting the tensile initial stiffness of thread-fixed one-side bolts (TOBs), connected to enclosed rectangular hollow section (RHS) columns, is presented in this paper. Two unknown stiffness components are considered: the TOBs connection and the enclosed RHS face. First, the trapezoidal thread of TOB, as an equivalent cantilevered beam subjected to uniformly distributed loads, is analyzed to determine the associated deformations. Based on the findings, the thread-shank serial-parallel stiffness model of TOB connection is proposed. For analysis of the tensile stiffness of the enclosed RHS face due to two bolt forces, the four sidewalls are treated as rotation constraints, thus reducing the problem to a two-dimensional plate analysis. According to the load superposition method, the deflection of the face plate is resolved into three components under various boundary and load conditions. Referring to the plate deflection theory of Timoshenko, the analytical solutions for the three deflections are derived in terms of the variables of bolt spacing, RHS thickness, height to width ratio, etc. Finally, the validity of the above stiffness equations is verified by a series of finite element (FE) models of T-stub substructures. The proposed component stiffness equations are an effective supplement to the component-based method.
This paper introduces a machine learning approach to address the challenge of limited data resulting from costly and time-consuming fire experiments by enlarging small fire test data sets and predicting the fire resistance of reinforced concrete columns. Our approach begins by creating deep learning models, namely generative adversarial networks and variational autoencoders, to learn the spatial distribution of real fire tests. We then use these models to generate synthetic tabular samples that closely resemble realistic fire resistance values for reinforced concrete columns. The generated data are employed to train state-of-the-art machine learning techniques, including Extreme Gradient Boost, Light Gradient Boosting Machine, Categorical Boosting Algorithm, Support Vector Regression, Random Forest, Decision Tree, Multiple Linear Regression, Polynomial Regression, Support Vector Machine, Kernel Support Vector Machine, Naive Bayes, and K-Nearest Neighbors, which can predict the fire resistance of the columns through regression and classification. Machine learning analyses achieved highly accurate predictions of fire resistance values, outperforming traditional models that relied solely on limited experimental data. Our study highlights the potential for using machine learning and deep learning analyses to revolutionize the field of structural engineering by improving the accuracy and efficiency of fire resistance evaluations while reducing the reliance on costly and time-consuming experiments.
This paper presents an analytical approach for estimating frictional resistance to pipe movement at soil and external pipe surface of buried coated pressurized steel pipes relative to the internal thrust force. The proposed analytical method was developed based on 36 experiments, which involved three coating types (cement mortar (CM), polyurethane type-I (PT-I), prefabricated plastic tape (PPT)) on pipes’ surfaces, three different soils (pea-gravel (PG), sand (S), silty-clay (SC)), and four simulated over burden depths above the pipe’s crown. Investigation showed frictional resistance decreased with increasing over burden depth above the pipe’s crown. The degree of frictional resistance at the pipe-soil interface was found to be in the order of PG > SC > S for all coating variations and overburden depths. CM coated pipe buried in all three types of soil produced significantly higher frictional resistance as compared to other coating types. Based on experimental data, the developed analytical introduced a dimensionless factor “Z”, which included effects of types of coatings, soil, and overburden depths for simplified rapid calculation. Analysis showed that the method provided a better prediction of frictional resistance forces, in comparison to previous analytical methods, which were barely close in predicting friction resistance for different coating variations, soil types, and overburden depths. Friction resistance force values reported herein could be considered conservative.
The shear bond of interface between concrete and basalt fiber reinforced polymer (BFRP) bars during freeze–thaw (F–T) cycles is crucial for the application of BFRP bar-reinforced concrete structures in cold regions. In this study, 48 groups of pull-out specimens were designed to test the shear bond of the BFRP-concrete interface subjected to F–T cycles. The effects of concrete strength, diameter, and embedment length of BFRP rebar were investigated under numerous F–T cycles. Test results showed that a larger diameter or longer embedment length of BFRP rebar resulted in lower interfacial shear bond behavior, such as interfacial bond strength, initial stiffness, and energy absorption, after the interface goes through F–T cycles. However, higher concrete strength and fewer F–T cycles were beneficial for enhancing the interfacial bond behavior. Subsequently, a three-dimensional (3D) interfacial model based on the finite element method was developed, and the interfacial bond behavior of the specimens was analyzed in-depth. Finally, a degradation bond strength subjected to F–T cycles was predicted by a proposed mechanical model. The predictions were fully consistent with the tested results. The model demonstrated accuracy in describing the shear bond behavior of the interface under numerous F–T cycles.
In tunnel construction with tunnel boring machines (TBMs), accurate prediction of the remaining useful life (RUL) of disc cutters is critical for timely maintenance and replacement to avoid delays and cost overruns. This paper introduces a novel hybrid model, integrating fundamental and data-driven approaches, to enhance wear prediction of TBM disc cutters and enable accurate RUL estimation. The fundamental model is improved by incorporating composite wear mechanisms and load estimation techniques, showcasing superior prediction accuracy compared to single-mechanism models. Additionally, the hybrid model innovatively incorporates a data-driven supplementary residual term into the improved fundamental model, leading to a high-performance wear prediction model. Using actual field data from a highway tunnel project in Shenzhen, the performance of the hybrid model is rigorously tested and compared with pure fundamental and data-driven models. The hybrid model outperforms the other models, achieving the highest accuracy in predicting TBM disc cutter wear (mean absolute error (MAE) = 0.53, root mean square error (RMSE) = 0.64). Furthermore, this study thoroughly analyzes the hybrid model’s generalization capability, revealing significant impacts of geological conditions on prediction accuracy. The model’s generalization capability is also improved by expanding and updating the data sets. The RUL estimation results provided by the hybrid model are straightforward and effective, making it a valuable tool by which construction staff can monitor TBM disc cutters.
This paper proposes design charts for estimating imperative input parameters for continuum approach analysis of the nonlinear dynamic response of piles. Experimental and analytical studies using continuum approach have been conducted on single and 2 × 2 grouped piles under coupled and vertical modes of vibration, for different dynamic forces and pile depth. As these design charts are derived from model piles, the charts have been validated for prototype pile foundations using scaling law. The experimental responses of model piles are scaled up and these responses exhibit good agreement with analytical results. This study also extends to estimation of the errors in computing frequency–amplitude responses with an increase in pile length. It is found that, with an increase in pile length, the errors also increase. The effectiveness of the proposed design charts is also checked with data based on different field setups given in existing literature, and these charts are found to be valid. Thus, the developed design charts can be beneficial in estimating the input parameters for continuum approach analysis for determining the nonlinear responses of pile supported machine foundations.
