The simulation of fracture in large-scale structures made of porous media remains a challenging task. Current techniques either assume a homogeneous model, disregarding the microstructure characteristics, or adopt a micro-mechanical model, which incurs an intractable computational cost due to its complex stochastic geometry and physical properties, as well as its nonlinear and multiscale features. In this study, we propose a multiscale analysis-based dual-variable-horizon peridynamics (PD) model to efficiently simulate macroscopic structural fracture. The influence of microstructures in porous media on macroscopic structural failure is represented by two PD parameters: the equivalent critical stretch and micro-modulus. The equivalent critical stretch is calculated using the microscale PD model, while the equivalent micro-modulus is obtained through the homogenization method and energy density equivalence between classical continuum mechanics and PD models. Numerical examples of porous media with various microstructures demonstrate the validity, accuracy, and efficiency of the proposed method.
Microbially induced carbonate precipitation (MICP) is a promising technique for the autonomous healing of concrete cracks. In this study, the effect of pH on MICP was investigated. The results indicate that the MICP process was inhibited when the pH was higher than 11. Both vaterite and calcite were produced when the pH was < 8, whereas only calcite was produced when the pH was > 8. Recycled concrete aggregates (RCA) coated with sodium silicate have been proposed as protective carriers for microbial healing agents. Although the presence of the coated RCA resulted in a loss of the splitting tension strength of the concrete, the loaded healing agents were highly efficient in self-healing cracks. Concrete incorporated with 20% RCA loaded with healing agents exhibited the best self-healing performance. When the initial crack widths were between 0.3 and 0.4 mm, the 7-d mean healing rate was approximately 90%. At 28 d, the crack area filling ratio was 86.4%, while its water tightness recovery ratio was 74.4% and 29.8%, respectively, for rapid and slow absorption. This study suggests that RCA coated with sodium silicate is an effective method for packaging microbial healing agents and has great potential for developing cost-effective self-healing concrete.
The utilization of recycled aggregates (RA) for concrete production has the potential to offer substantial environmental and economic advantages. However, RA concrete is plagued with considerable durability concerns, particularly carbonation. To advance the application of RA concrete, the establishment of a reliable model for predicting the carbonation is needed. On the one hand, concrete carbonation is a long and slow process and thus consumes a lot of time and energy to monitor. On the other hand, carbonation is influenced by many factors and is hard to predict. Regarding this, this paper proposes the use of machine learning techniques to establish accurate prediction models for the carbonation depth (CD) of RA concrete. Three types of regression techniques and meta-heuristic algorithms were employed to provide more alternative predictive tools. It was found that the best prediction performance was obtained from extreme gradient boosting-multi-universe optimizer (XGB-MVO) with R2 value of 0.9949 and 0.9398 for training and testing sets, respectively. XGB-MVO was used for evaluating physical laws of carbonation and it was found that the developed XGB-MVO model could provide reasonable predictions when new data were investigated. It also showed better generalization capabilities when compared with different models in the literature. Overall, this paper emphasizes the need for sustainable solutions in the construction industry to reduce its environmental impact and contribute to sustainable and low-carbon economies.
3D concrete printing has the potential to replace shotcrete for construction of linings of tunnels in hard rock. The shear strength of the interface between rock and printed concrete is vital, especially at super-early ages. However, traditional methods for testing the shear strength of the interface, e.g., the direct shear test, are time-consuming and result in a high variability for fast-hardening printed concrete. In this paper, a new fast bond shear test is proposed. Each test can be completed in 1 min, with another 2 min for preparing the next test. The influence of the matrix composition, the age of the printed matrices, and the interface roughness of the artificial rock substrate on the shear strength of the interface was experimentally studied. The tests were conducted at the age of the matrices at the 1st, the 4th, the 8th, the 16th, the 32nd, and the 64th min after its final setting. A dimensionless formula was established to calculate the shear strength, accounting for the age of the printed matrices, the interface roughness, and the shear failure modes. It was validated by comparing the calculated results and the experimental results of one group of samples.
Robotic-based technologies such as automated spraying or extrusion-based 3-dimensional (3D) concrete printing can be used to build tunnel linings, aiming at reducing labor and mitigating the associated safety issues, especially in the high-geothermal environment. Extrusion-based 3D concrete printing (3DCP) has additional advantages over automated sprayings, such as improved surface quality and no rebound. However, the effect of different temperatures on the adhesion performance of 3D-printed materials for tunnel linings has not been investigated. This study developed several alkali-activated slag mixtures with different activator modulus ratios to avoid the excessive use of Portland cement and enhance sustainability of 3D printable materials. The thermal responses of the mixtures at different temperatures of 20 and 40 °C were studied. The adhesion strength of the alkali-activated material was evaluated for both early and later ages. Furthermore, the structural evolution of the material exposed to different temperatures was measured. This was followed by microstructure characterization. Results indicate that elevated temperatures accelerate material reactions, resulting in improved early-age adhesion performance. Moreover, higher temperatures contribute to the development of a denser microstructure and enhanced mechanical strength in the hardened stage, particularly in mixtures with higher silicate content.
The first exothermic peak of cement-based material occurs a few minutes after mixing, and the properties of three dimensional (3D) printed concrete, such as setting time, are very sensitive to this. Against this background, based on the classical Park cement exothermic model of hydration, we propose and construct a numerical model of the first exothermic peak, taking into account the proportions of C3S, C3A and quicklime in particular. The calculated parameters are calibrated by means of relevant published exothermic test data. It is found that this developed model offers a good simulation of the first exothermic peak of hydration for C3S and C3A proportions from 0 to 100% of cement clinker and reflects the effect of quicklime content at 8%–10%. The unique value of this research is provision of an important computational tool for applications that are sensitive to the first exothermic peak of hydration, such as 3D printing.
Steel structures are widely used; however, their traditional design method is a trial-and-error procedure which is neither efficient nor cost effective. Therefore, a multi-population particle swarm optimization (MPPSO) algorithm is developed to optimize the weight of steel frames according to standard design codes. Modifications are made to improve the algorithm performances including the constraint-based strategy, piecewise mean learning strategy and multi-population cooperative strategy. The proposed method is tested against the representative frame taken from American standards and against other steel frames matching Chinese design codes. The related parameter influences on optimization results are discussed. For the representative frame, MPPSO can achieve greater efficiency through reduction of the number of analyses by more than 65% and can obtain frame with the weight for at least 2.4% lighter. A similar trend can also be observed in cases subjected to Chinese design codes. In addition, a migration interval of 1 and the number of populations as 5 are recommended to obtain better MPPSO results. The purpose of the study is to propose a method with high efficiency and robustness that is not confined to structural scales and design codes. It aims to provide a reference for automatic structural optimization design problems even with dimensional complexity. The proposed method can be easily generalized to the optimization problem of other structural systems.
Single-layer reticulated shells (SLRSs) find widespread application in the roofs of crucial public structures, such as gymnasiums and exhibition center. In this paper, a new neural-network-based method for structural damage identification in SLRSs is proposed. First, a damage vector index, NDL, that is related only to the damage localization, is proposed for SLRSs, and a damage data set is constructed from NDL data. On the basis of visualization of the NDL damage data set, the structural damaged region locations are identified using convolutional neural networks (CNNs). By cross-dividing the damaged region locations and using parallel CNNs for each regional location, the damaged region locations can be quickly and efficiently identified and the undamaged region locations can be eliminated. Second, a damage vector index, DS, that is related to the damage location and damage degree, is proposed for SLRSs. Based on the damaged region identified previously, a fully connected neural network (FCNN) is constructed to identify the location and damage degree of members. The effectiveness and reliability of the proposed method are verified by considering a numerical case of a spherical SLRS. The calculation results showed that the proposed method can quickly eliminate candidate locations of potential damaged region locations and precisely determine the location and damage degree of members.
In relation to the Shifu Road Station project on Line 4 of the Shenyang Metro in China, a small-pipe roof-beam method for constructing subway stations is presented. First, a numerical simulation was performed to optimize the supporting parameters of the proposed method and determine the design scheme. Subsequently, the deformation of the pipe roof and surface settlement during the construction process were investigated. Finally, the surface settlement attributed to the excavation was studied through field monitoring, and the proposed method was compared with other methods. The results show that an increase in the pipe-roof spacing has little effect on the surface settlement and pipe-roof deformation. The bearing capacity of the pipe roof can be efficiently utilized once the flexural stiffness reaches 2EI, and the flexural stiffness is not the dominant factor controlling the deformation. The essential stages in controlling surface settlement are the excavations of the transverse pilot tunnels and the soil between them. The final settlement value of the ground was 24.1 mm, resulting in a reduction in the construction period by at least five months while satisfying the control requirements.
Segmental tunnel lining strengthened with steel plates is widely used worldwide to provide a permanent strengthening method. Most existing studies assume an ideal steel-concrete interface, ignoring discontinuous deformation characteristics, making it difficult to accurately analyze the strengthened structure’s failure mechanism. In this study, interfacial fracture mechanics of composite material was applied to the segmental tunnel lining strengthened with steel plates, and a numerical three-dimensional solid nonlinear model of the lining structure was established, combining the extended finite element method with a cohesive-zone model to account for the discontinuous deformation characteristics of the interface. The results accurately describe the crack propagation process, and are verified by full-scale testing. Next, dynamic simulations based on the calibrated model were conducted to analyze the sliding failure and cracking of the steel-concrete interface. Lastly, detailed location of the interface bonding failure are further verified by model test. The results show that, the cracking failure and bond failure of the interface are the decisive factors determining the instability and failure of the strengthened structure. The proposed numerical analysis is a major step forward in revealing the interface failure mechanism of strengthened composite material structures.
