Temperature Stress Testing Machine (TSTM) is a universal testing tool for many properties relevant to early-age cracking of cementitious materials. However, the complexity of TSTMs require heavy lab work and thus hinders a more thorough parametric study on a range of cementitious materials. This study presents the development and validation of a Mini-TSTM for efficiently testing the autogenous deformation (AD), viscoelastic properties, and their combined results, the early-age stress (EAS). The setup was validated through systematic tests of EAS, AD, elastic modulus, and creep. Besides, the heating/cooling capability of the setup was examined by tests of coefficient of thermal expansion by temperature cycles. The results of EAS correspond well to that of AD, which qualitatively validates the developed setup. To quantitatively validate the setup, a classical viscoelastic model was built, based on the scenario of a 1-D uniaxial restraint test, to predict the EAS results with the tested AD, elastic modulus, and creep of the same cementitious material as the input. The predicted EAS matched the testing results of Mini-TSTM with good accuracy in 6 different cases. The viscoelastic model also provided quantitative explanations for why variations in early AD do not influence the EAS results. The testing and modelling results together validate the developed Mini-TSTM setup as an efficient tool for studying early-age cracking of cementitious materials. At the end, the potential limitations of the Mini-TSTM are discussed and its applicability for concrete with aggregate size up to 22 mm is demonstrated.

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Vascular self-healing concrete (SHC) has great potential to mitigate the environmental impact of the construction industry by increasing the durability of structures. Designing concrete with high initial mechanical properties by searching a specific arrangement of vascular structure is of great importance. Herein, an automatic optimization method is proposed to arrange vascular configuration for minimizing the adverse influence of vascular system through a reinforcement learning (RL) approach. A case study is carried out to optimize a concrete beam with 3 pores (representing a vascular network) positioned in the beam midspan within a design space of 40 possibilities. The optimization is performed by the interaction between RL agent and Abaqus simulation environment with the change of target properties as a reward signal. The results illustrates that the RL approach is able to automatically enhance the vascular arrangement of SHC given the fact that the 3-pore structures that have the maximum target mechanical property (i.e., peak load or fracture energy) are accessed for all of the independent runs. The RL optimization method is capable of identifying the structure with high fracture energy in the new optimization task for 4-pore concrete structure.

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We propose a new numerical method to analyze the early-age creep of 3D printed segments with the consideration of stress history. The integral creep strain evaluation formula is first expressed in a summation form using superposition principle. The experimentally derived creep compliance surface is then employed to calculate the creep strain in the lattice model with a combination of stored stress history. These strains are then converted into element forces and applied to the analyzed object. The entire numerical analysis consists of a sequence of linear analyses, and the viscosity is modelled using imposed local forces. The model is based on the incremental algorithm and one of the main advantages is the straightforward implementation of stress history consideration. The creep test with incremental compressive loading is utilized to validate this model. The modelling results are in good agreement with experimental data, demonstrating the feasibility of the lattice model in early-age creep analysis under incremental compressive loading. To understand the impact of early-age creep on structural viscoelastic deformation during the printing process, additional analyses of a printed segment are carried out. These simulation results highlight the need to consider creep for accurate prediction of viscoelastic deformation during the printing process.

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For some decades now, additive manufacturing has been a revolutionary technology which generates enormous interest in both industrial and academic applications. 3D concrete printing (3DCP), an automated construction method, is able to manufacture the computer-designed model through material deposition. This innovative technique can considerably accelerate the construction process, and make it economically and technically feasible to implement complex structural elements in practice. Although this technique shows a promising future, full adoption in construction sector is still far from possible due to the absence of fundamental knowledge about printable material and structural analysis during or after printing process....@en

This paper employs computer vision techniques to predict the micromechanical properties (i.e., elastic modulus and hardness) of cement paste based on an input of Backscattered Electron (BSE) images. A dataset comprising 40,000 nanoindentation tests and 40,000 BSE micrographs was built by express nanoindentation test and Scanning Electron Microscopy (SEM). A Residual Convolutional Neural Network (Res-Net) model, which differs from a typical Convolutional Neural Network (CNN) architecture by a shortcut connection, was employed and compared with a simple table model. The models were trained, tuned, and tested over a training, validation and testing set comprising 70%, 15% and 15% of the 40,000 data pairs, respectively. The following conclusions were drawn: 1) Express nanoindentation tests can provide reliable information for cement paste. Deconvolution based on Gaussian Mixture Model (GMM) can obtain almost invariant statistics for each phase; 2) Based on averaged greyscale values of each BSE image, a table model can predict the elastic modulus and hardness with R² of 0.80 and 0.83, respectively; 3) Based on the intensity of each pixel as well as their patterns in each BSE image, the Res-Net model can predict the elastic modulus and hardness with a R² of 0.85 and 0.88, respectively. Deconvolution of the Res-Net prediction obtains similar invariant statistics as derived by the nanoindentation tests, which gives strong evidence of the applicability of the Res-Net model.

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This paper describes the development of a discrete lattice model for simulating structures formed from self-healing cementitious materials. In particular, a new approach is presented for simulating time dependent mechanical healing in lattice elements. The proposed formulation is designed to simulate the transient damage and healing behaviour of structures under a range of loading conditions. In addition, multiple and overlapping damage and healing events are considered. An illustrative example demonstrates the effects of varying the healing agent curing parameters on the computed mechanical response. The model is successfully validated using published experimental data from two series of tests on structural elements with an embedded autonomic self-healing system. The meso-scale model gives detailed information on the size and disposition of cracking and healing zones throughout an analysis time history. The model also provides an accurate means of determining the volume of healing agent required to achieve healing for all locations within a structural element. The importance of the information provided by the model for the design of self-healing cementitious material elements is highlighted.

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In this study, an experimental setup to characterize the early-age creep of 3D printable mortar was proposed. The testing protocol comprises quasi-static compressive loading-unloading cycles, with 180-s holding periods in between. An analytical model based on a double power law was used to predict creep compliance with hardening time and loading duration as inputs. Subsequently, this analytical model was validated by comparison to uniaxial compression tests in which loading is increased incrementally, i.e., in steps, showing a good quantitative agreement. Minor differences between the two results were noted, most notably at the beginning of the test. This is because the determination of creep compliance for 3D printable mortar at fresh stage depends on the load level. In the end, the volumetric strain of tested samples from uniaxial compressive test is used to explain why the compressive loading affects the creep deformation.

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In the current study, experiments and numerical simulations were carried out to investigate the cracking behavior of reinforced concrete beams consisting of a very thin layer (i.e., 1 cm in thickness) of SHCC in the concrete cover, tension zone. A novel type of SHCC/concrete interface that features a weakened chemical adhesion but an enhanced mechanical interlock bonding was developed to facilitate the activation of SHCC. The study involved testing hybrid SHCC/concrete beams that have various types of interfaces. The results were compared to the control reinforced concrete beams that do not have SHCC in the cover. Four-point bending tests were performed with the beams and Digital Image Correlation (DIC) was utilized to track the development of crack pattern and crack width. Results show that hybrid beams possessed similar load bearing capacity but exhibited a significantly improved cracking behavior as compared to the control beam. With a 1-cm-thick layer of SHCC, the maximum crack width of the best performing hybrid beam exceeded 0.3 mm at 53.3 kN load, whereas in the control beam the largest crack exceeded 0.3 mm at 32.5 kN load. The hybrid beam with the proposed new interface formed 10 times more cracks in SHCC than the hybrid beam with a simple smooth interface and had an average crack width less than 0.1 mm throughout the loading. The lattice model has successfully showcased its ability to predict and offer valuable insights into the fracture behavior of hybrid systems. The simulation results indicate that the presence of a weak interface bond, coupled with mechanical interlocking, can effectively facilitate the activation of SHCC, resulting in the formation of more cracks and a delayed progression towards the maximum crack width. As the volume ratio of SHCC used in the hybrid beams is only 6%, the current study highlights the strategic use of minimum amount of SHCC in the critical region to efficiently enhance the performance of hybrid structures.

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Herein, different kinds of methods for buildability quantification of 3D concrete printing are reviewed, including experimental approaches, analytical modelling, and numerical simulations. A brief introduction on printing process is first given. This discusses the material properties in different stages. Material printability, which encompasses pumpability, extrudability and buildability, is then discussed. Subsequently, a brief review of the experimental and analytical models for buildability quantification is presented and they're discussed. An overview on the numerical tools for 3DCP is then given. These numerical models can quantify structural buildability and optimize the printing parameters, therefore, providing a more economical solution for buildability quantification. In the end, a summary and discussion on the limitations of numerical tools for buildability quantification are provided, as well as recommendations for their improvement.

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Cementitious materials may exhibit significant creep at very early age. This is potentially important for concrete 3D printing, where the material is progressively loaded even before it sets. However, does creep actually affect the buildability of 3D printed concrete? Herein, the influence of early-age creep on the buildability of 3D printed concrete is studied numerically. Creep is considered using the “local-force method”, which was developed in our previous work. This 3D printing model be used to quantify the influence of early-age creep on typical failure modes, i.e., structural instability due to buckling and plastic collapse resulting from material yielding. The green strength and early-age creep experiments are conducted to characterize early-age visco-elastic-plastic behaviors. The model is then validated with the comparison to printing experiment about buildability quantification and failure mode prediction. Parametric analyses are subsequently performed to quantify the influence of early-age creep on various printing geometries in which different failure modes are dominant. The numerical results highlight the significance of initial printing time and material mix design for predicting the buildability of 3D printing of concrete. Finally, a discussion on how creep affects structural buildability is given from the perspective of localized damage and element strain.

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This paper investigates the influence of temperature on autogenous deformation and early-age stress (EAS) evolution in ordinary Portland cement paste using a recently developed Mini Temperature Stress Testing Machine (Mini-TSTM) and Mini Autogenous Deformation Testing Machine (Mini-ADTM). In the Mini-TSTM/ ADTM, CEM I 42.5 N paste with a water-cement ratio of 0.30 was tested under a curing temperature of 10, 15, 20, 25, 30, and 40 °C. X-Ray diffraction (XRD) tests were conducted to measure the amount of ettringite and calcium hydroxide, which reveals the micro-scale mechanisms of autogenous expansion. The applicability of the Maturity Concept (MC) for the prediction of autogenous deformation and relaxation modulus under different temperatures was also examined by the experimental data and the viscoelastic model. This paper leads to the following findings: 1) The autogenous deformation of ordinary Portland cement paste is a four-stage process comprising the initial shrinkage, autogenous expansion, plateau, and autogenous shrinkage; 2) Higher temperature leads to higher early-age cracking (EAC) risk because it accelerates the transitions through the first three stages and causes the autogenous shrinkage stage to start earlier. Moreover, higher temperatures also result in increased rates of autogenous shrinkage and EAS in the autogenous shrinkage stage; 3) Autogenous expansion and plateau are attributed to the crystallization pressure induced by CH. Temperature-dependent CH formation rates determine the duration of the plateau stage; 4) Low-temperature curing can delay but not completely prevent the EAC induced by autogenous deformation; 5) The MC cannot predict the autogenous deformation at different temperatures but can be used to calculate the relaxation modulus, which in turn aids in EAS prediction based on autogenous deformation data.

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In this paper, optimization of vascular structure of self-healing concrete is performed with deep neural network (DNN). An input representation method is proposed to effectively represent the concrete beams with 6 round pores in the middle span as well as benefit the optimization process. To investigate the feasibility of using DNN for vascular structure optimization (i.e., optimization of the spatial arrangement of the vascular network), structure optimization improving peak load and toughness is first carried out. Afterwards, a hybrid target is defined and used to optimize vascular structure for self-healing concrete, which needs to be healable without significantly compromising its mechanical properties. Based on the results, we found it feasible to optimize vascular structure by fixing the weights of the DNN model and training inputs with the data representation method. The average peak load, toughness and hybrid target of the ML-recommended concrete structure increase by 17.31%, 34.16% and 9.51%. The largest peak load, toughness and hybrid target of the concrete beam after optimization increase by 0.17%, 14.13%, and 3.45% compared with the original dataset. This work shows that the DNN model has great potential to be used for optimizing the design of vascular system for self-healing concrete.

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This study aims to experimentally investigate the autogenous deformation and the stress evolution in restrained high-volume ground granulated blast furnace slag (GGBFS) concrete. The Temperature Stress Testing Machine (TSTM) and Autogenous Deformation Testing Machine (ADTM) were used to study the macro-scale autogenous deformation and stress evolution of high-volume GGBFS concrete with w/b ratios of 0.35, 0.42, and 0.50. The early-age cracking (EAC) risk (quantified by stress-strength ratio) and stress relaxation were analyzed extensively based on ADTM and TSTM results. Furthermore, Environmental Scanning Electron Microscopy (ESEM), X-ray Diffraction (XRD), and Mercury Intrusion Porosimetry (MIP) were conducted to explore the micro-scale origin of the autogenous deformation of high-volume GGBFS concrete, which supports the observations on the macroscale measurement of TSTM/ ADTM tests. This study finds that the ettringite formation in the first two days results in autogenous expansion, which can delay the appearance of tensile stress. The magnitude of autogenous expansion depends on the compatibility of ettringite content and pore size. The w/b ratio of 0.42 turns out to be optimal because it produces the highest amount of ettringite and results in the highest autogenous expansion. In comparison, the w/b ratio of 0.35 introduces significant autogenous shrinkage after the expansion peak and therefore corresponds to a high early-age cracking risk.

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In our former paper, based on a published 3D reactive transport model at microscale with the capability of simulating the chemical reactions involved in ASR, the location of expansive ASR gel related to the reactivity of aggregate, temperature, aggregate porosity and silica content in aggregate, is clarified. Based on the simulation results, in this paper, the cracking process at mesoscale in concrete induced by ASR in the early stage is investigated. The results show that the cracking process can be divided into four stages and three cracking routes are generalized with the behind chemical exposed environments specified. The cracking routes are found to be comparable with the experimental observed routes. For the first time, the cracking patterns induced by ASR in concrete at mesoscale is linked with the chemical reactions at microscale, which is the first step towards building a complete computational tool to predict ASR as realistic as possible.

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Early-age stress (EAS) is an important index for evaluating the early-age cracking risk of concrete. This paper encompasses a thermo-chemo-mechanical (TCM) model and active ensemble learning (AEL) for predicting the EAS evolution. The TCM model provides the data for the AEL model. First, based on Fourier's law, Arrhenius’ equation, and rate-type creep law, a TCM model is built to simulate the heat transfer, cement hydration, and viscoelasticity, which together determine the EAS evolution. Then, a material model composed of an eXtreme Gradient Boosting model and adjusted Model Code 2010 is built to allow for parametric study and database construction. Finally, an AEL framework is built, which incorporates principal component analysis (PCA), Gaussian process, and light gradient boosting machine (LGBM). This study resulted in the following findings: (1) The dimensionality of the 672-by-1 EAS vector can be effectively reduced by PCA, and the first principal component (PC) is a global index representing the magnitude of the EAS; (2) the mechanical field of the TCM model is validated by testing data. Correlation analysis on the first PC quantifies the influence of various input parameters of the TCM model, which is in accordance with common understandings of the EAS evolution process. (3) The AEL and one-shot ensemble learning (OSEL) both achieve high prediction performance in the testing set, whose R² reaches 0.961 and 0.948, respectively. Thanks to the uncertainty-based query procedure, comparing with OSEL, AEL shows advantages in prediction performance over the whole training history. (4) AEL can significantly reduce the number of samples required for training, which can be a major improvement in efficiency considering the computational cost of the TCM model.

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Stress evolution of restrained concrete is a significant direct index in early-age cracking (EAC) analysis of concrete. This study presents experiments and numerical modelling of the early-age stress evolution of Ground granulated blast furnace slag (GGBFS) concrete, considering the development of autogenous deformation and creep. Temperature Stress Testing Machine (TSTM) tests were conducted to obtain the autogenous deformation and stress evolution of restrained GGBFS concrete. By a self-defined material subroutine based on the Rate-type creep law, the FEM model for simulating the stress evolution in TSTM tests was established. By characterizing the creep compliance function with a 13-units continuous Kelvin chain, forward modelling was firstly conducted to predict the stress development. Then inverse modelling was conducted by Bayesian Optimization to efficiently modify the arbitrary assumption of the codes on the aging creep. The major findings of this study are as follows: 1) the high autogenous expansion of GGBFS induces compressive stress at first hours, but its value is low because of high relaxation and low elastic modulus; 2) The codes highly underestimated the early-age creep of GGBFS concrete. They performed well in prediction of stress after 200 h, but showed significant gaps in predictions of early-age stress evolution; 3) The proposed inverse modelling method with Bayesian Optimization can efficiently adjusted the aging terms which produced best modelling results. The adjusted creep compliance function of GGBFS showed a much faster aging speed at early ages than the one proposed by original codes.

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Tailoring lattice structures is a commonly used method to develop lattice materials with desired mechanical properties. However, for cementitious lattice materials, besides the macroscopic lattice structure, the multi-phase microstructure of cement paste may have substantial impact on the mechanical responses. Therefore, this work proposes a multi-scale numerical modelling method to simulate the deformation and fracture behavior of cementitious lattice materials, such that the influence of cement paste microstructure can be properly captured. On the microscale, the load–displacement response of cement paste is numerically simulated then experimentally validated. In order to rationally investigate the role of cement paste microstructure, the obtained load–displacement response was then formulated to several types of model inputs reflecting different degree of brittleness. These inputs were then used for simulating the mechanical response of macroscale cementitious lattices. By comparing the simulation to experiment, multi-linear behavior (ML) was found to an appropriate method to include the realistic pre-critical cracking and post-peak softening of cement paste in the model. Compared to ideally brittle behavior, using ML as input, the discrepancy between simulated and experimentally tested fracture energy decreases from 37.4% to 12.4%. In addition, the influence of lattice structure on the strength of cementitious lattices was also accurately captured by the proposed model. Randomized cementitious lattice has 21.6% (22.0% from simulation) lower strength than regular lattice. Moreover, the influence of fracture criterion of the proposed model is discussed and elaborated. Owning to the high simulation accuracy, the proposed multi-scale method in this work could be helpful for tailoring the fracture cementitious lattice materials for future studies.

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Extrusion-based 3D concrete printing (3DCP) results in deposited materials with complex microstructures that have high porosity and distinct anisotropy. Due to the material heterogeneity and rapid growth of cracks, fracture analysis in these air-void structures is often complex, resulting in a high computational cost. This study proposes a convolutional neural network (CNN)-based methodology for fracture analysis using air-void structures as input. More specifically, the lattice fracture model is used to build a dataset that comprises input air-void structures as well as output fracture information, including the crack patterns and crack-width curves. To establish the relationship between crack morphology and associated microstructures, a U-net convolutional neural network is first presented. With the obtained crack pattern as input, the principal component analysis (PCA) and CNN are then integrated to predict the stress-crack width curves. The predicted results from the CNN model demonstrate a quantitative agreement with lattice numerical analyses, with 0.85 Intersection over Union for crack patterns prediction and 0.75 R2 for the stress-crack width curves prediction. This indicates that CNN models can be used as an alternative to traditional numerical analysis. The feature maps during the convolutional or deconvolutional process are given to explain why the proposed CNN models perform well on fracture analysis of the air-void system. Moreover, the model generalization is discussed using transfer learning with fine-tuning to show the model potential on microstructures expressing varied pore information. In the end, the microstructures cropped from XCT are created to explore the further application of CNN models on fracture analysis of 3D printed materials.@en

This research studies the impact of localized damage and deformed printing geometry on the structural failure of plastic collapse for 3D concrete printing (3DCP) using the lattice model. Two different approaches are utilized for buildability quantification: the (previously developed) load-unload method, which updates and relaxes the printing system after each analysis step and repeatedly applies the gravitational loading to the undeformed structure; and the incremental method, which keeps the load after each analysis step and applies the incremental loading to the deformed printing system. The former can consider the material yielding but cannot capture accurately the structural deformation during printing process. Compared to the load-unload method, the incremental method can not only consider deformed printing geometry but can also simulate the non-proportional loading conditions and disequilibrium force occurring during 3D printing. In this study, computational uniaxial compression tests are first conducted to compare two algorithms. The numerical results indicate the consideration of nonequilibrium force and deformed geometry affects the peak load and crack information for fracture analysis. Subsequently, the incremental method is incorporated into the lattice model to quantify buildability of 3DCP. The predictions are compared with previously published numerical results obtained using the load-unload method. The lattice model based on incremental method reproduces correct failure mode; better quantitative agreement about critical printing height also can be obtained. These numerical analyses demonstrate that the incremental solution is an approximate method for buildability quantification since it can account for the nonequilibrium force induced by the deformed printing geometry and localized damage.

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This study aims to provide an efficient alternative for predicting creep modulus of cement paste based on Deep Convolutional Neural Network (DCNN). First, a microscale lattice model for short-term creep is adopted to build a database that contains 18,920 samples. Then, 3 DCNNs with different consecutive convolutional layers are built to learn from the database. Finally, the performance of DCNNs is tested on unseen testing samples. The results show that the DCNNs can achieve high accuracy in the testing set, with the R² all higher than 0.96. The distribution of creep modulus predicted by the DCNNs coincides with that of the original data. Furthermore, through analyzing the feature maps, it is found that the DCNNs can correctly capture the local importance of different microstructural phases. The DCNN allows therefore prediction of the creep modulus based on microstructural input, which saves computational resources of segmentation procedure and multiple incremental FEM calculations.

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