Application of additive manufacturing in vascular self-healing cementitious materials

Self-healing concrete has great potential to enhance the durability of concrete structures without significantly increasing the initial costs. Among the self-healing approaches, vascular self-healing cementitious composite is capable of supplying healing agents to the cracked region in a continuous way or multiple times. However, the use of brittle materials as vascular makes it difficult to create vascular networks with complicated geometry. The recent development of additive manufacturing (AM, also known as 3D printing) promotes the fabrication of complicated vascular system for vascular self-healing materials. However, the application of AM in vascular self-healing cementitious materials is relatively rare. Therefore, this study focuses on understanding the behavior of 3D-printed vascular self-healing concrete with different printing parameters or vascular configurations.....Materials and Environmen

Optimization of vascular structure of self-healing concrete using deep neural network (DNN)

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.Materials and Environmen

On the use of machine learning models for prediction of compressive strength of concrete: Influence of dimensionality reduction on the model performance

Compressive strength is the most significant metric to evaluate the mechanical properties of concrete. Machine learning (ML) methods have shown promising results for predicting compres-sive strength of concrete. However, at present, no in-depth studies have been devoted to the influence of dimensionality reduction on the performance of different ML models for this application. In this work, four representative ML models, i.e., Linear Regression (LR), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), are trained and used to predict the compressive strength of concrete based on its mixture composition and cur-ing age. For each ML model, three kinds of features are used as input: the eight original features, six Principal Component Analysis (PCA)-selected features, and six manually selected features. The performance as well as the training speed of those four ML models with three different kinds of features is assessed and compared. Based on the obtained results, it is possible to make a relatively accurate prediction of concrete compressive strength using SVR, XGBoost, and ANN with an R-square of over 0.9. When using different features, the highest R-square of the test set occurs in the XGBoost model with manually selected features as inputs (R-square = 0.9339). The prediction accuracy of the SVR model with manually selected features (R-square = 0.9080) or PCA-selected features (R-square = 0.9134) is better than the model with original features (R-square = 0.9003) without dramatic running time change, indicating that dimensionality reduction has a positive influence on SVR model. For XGBoost, the model with PCA-selected features shows poorer performance (R-square = 0.8787) than XGBoost model with original features or manually selected features. A possible reason for this is that the PCA-selected features are not as distinguishable as the manually selected features in this study. In addition, the running time of XGBoost model with PCA-selected features is longer than XGBoost model with original features or manually selected features. In other words, dimensionality reduction by PCA seems to have an adverse effect both on the performance and the running time of XGBoost model. Dimensionality reduction has an adverse effect on the performance of LR model and ANN model because the R-squares on test set of those two models with manually selected features or PCA-selected features are lower than models with original features. Although the running time of ANN is much longer than the other three ML models (less than 1s) in three scenarios, dimensionality reduction has an obviously positive influence on running time without losing much prediction accuracy for ANN model.Materials and Environmen

Convolutional neural network for predicting crack pattern and stress-crack width curve of air-void structure in 3D printed concrete

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.Materials and Environmen

Automatic enhancement of vascular configuration for self-healing concrete through reinforcement learning approach

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.Materials and Environmen

Interpretable Ensemble-Machine-Learning models for predicting creep behavior of concrete

This study aims to provide an efficient and accurate machine learning (ML) approach for predicting the creep behavior of concrete. Three ensemble machine learning (EML) models are selected in this study: Random Forest (RF), Extreme Gradient Boosting Machine (XGBoost) and Light Gradient Boosting Machine (LGBM). Firstly, the creep data in Northwestern University (NU) database is preprocessed by a prebuilt XGBoost model and then split into a training set and a testing set. Then, by Bayesian Optimization and 5-fold cross validation, the 3 EML models are tuned to achieve high accuracy (R2 = 0.953, 0.947 and 0.946 for LGBM, XGBoost and RF, respectively). In the testing set, the EML models show significantly higher accuracy than the equation proposed by the fib Model Code 2010 (R2 = 0.377). Finally, the SHapley Additive exPlanations (SHAP), based on the cooperative game theories, are calculated to interpretate the predictions of the EML model. Five most influential parameters for concrete creep compliance are identified by the SHAP values of EML models as follows: time since loading, compressive strength, age when loads are applied, relative humidity during the test and temperature during the test. The patterns captured by the three EML models are consistent with theoretical understanding of factors that influence concrete creep, which proves that the proposed EML models show reasonable predictions.Materials and Environmen

Early-age creep of 3D printable mortar: Experiments and analytical modelling

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.Materials and Environmen

Microstructure-informed deep convolutional neural network for predicting short-term creep modulus of cement paste

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.Materials and Environmen

Towards understanding deformation and fracture in cementitious lattice materials: Insights from multiscale experiments and simulations

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.Materials and Environmen

Microstructural characterization of crack-healing enabled by bacteria-embedded polylactic acid (PLA) capsules

The current study investigates short-term and long-term crack-healing behaviour of mortars embedded with bacteria-based poly-lactic acid (PLA) capsules under both ideal and realistic environmental conditions. Two sets of specimens were prepared and subjected to different healing regimes, with the first set kept in a mist room for varying short durations (i.e., 1 week, 2 weeks, 3 weeks and 8 weeks) and the second set placed in an unsheltered outdoor environment for a long-term healing process (i.e., 1 year). Alteration of microstructure because of self-healing was characterized by backscattered electron (BSE) imaging and energy dispersive X-ray spectroscopy (EDS) via crack cross-sections. Results show that visible crack healing enabled by bacteria began after 2 weeks in a humid environment. The healing products initially precipitated at crack mouths and gradually moved deeper into cracks, with the precipitated calcium carbonate crystals growing larger over time. After 8 weeks, healing products can be found even a few millimetres deep inside cracks. Observations of crack healing in a realistic environment revealed significant differences compared to healing under controlled conditions. While no healing products can be found at crack mouths, a substantial healing process was observed throughout the entire crack depth. It is likely that the environmental actions such as rainfall and/or freeze and thaw cycles may have worn away the healing products at crack mouths and thus led to a deeper ingress of oxygen into cracks, which promoted the activation of healing agents and associated calcium carbonate precipitation deep inside a crack.Materials and Environmen