Hongwei Song

Bio: Hongwei Song is an academic researcher from Minzu University of China. The author has contributed to research in topics: Aggregate (composite) & Mortar. The author has an hindex of 3, co-authored 4 publications receiving 13 citations.

Analyzing the Compressive Strength of Ceramic Waste-Based Concrete Using Experiment and Artificial Neural Network (ANN) Approach.

Abstract: In a fast-growing population of the world and regarding meeting consumer’s requirements, solid waste landfills will continue receiving a substantial amount of waste. The utilization of solid waste materials in concrete has gained the attention of the researchers. Ceramic waste powder (CWP) is considered to be one of the most harmful wastes for the environment, which may cause water, soil, and air pollution. The aim of this study was comprised of two phases. Phase one was based on the characterization of CWP with respect to its composition, material testing (coarse aggregate, fine aggregate, cement,) and evaluation of concrete properties both in fresh and hardened states (slump, 28 days compressive strength, and dry density). Concrete mixes were prepared in order to evaluate the compressive strength (CS) of the control mix, with partial replacement of the cement with CWP of 10 and 20% by mass of cement and 60 prepared mixes. However, phase two was based on the application of the artificial neural network (ANN) and decision tree (DT) approaches, which were used to predict the CS of concrete. The linear coefficient correlation (R2) value from the ANN model indicates better performance of the model. Moreover, the statistical check and k-fold cross validation methods were also applied for the performance confirmation of the model. The mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were evaluated to confirm the model’s precision.

An Overview on the Rheology, Mechanical Properties, Durability, 3D Printing, and Microstructural Performance of Nanomaterials in Cementitious Composites.

Abstract: The most active research area is nanotechnology in cementitious composites, which has a wide range of applications and has achieved popularity over the last three decades. Nanoparticles (NPs) have emerged as possible materials to be used in the field of civil engineering. Previous research has concentrated on evaluating the effect of different NPs in cementitious materials to alter material characteristics. In order to provide a broad understanding of how nanomaterials (NMs) can be used, this paper critically evaluates previous research on the influence of rheology, mechanical properties, durability, 3D printing, and microstructural performance on cementitious materials. The flow properties of fresh cementitious composites can be measured using rheology and slump. Mechanical properties such as compressive, flexural, and split tensile strength reveal hardened properties. The necessary tests for determining a NM's durability in concrete are shrinkage, pore structure and porosity, and permeability. The advent of modern 3D printing technologies is suitable for structural printing, such as contour crafting and binder jetting. Three-dimensional (3D) printing has opened up new avenues for the building and construction industry to become more digital. Regardless of the material science, a range of problems must be tackled, including developing smart cementitious composites suitable for 3D structural printing. According to the scanning electron microscopy results, the addition of NMs to cementitious materials results in a denser and improved microstructure with more hydration products. This paper provides valuable information and details about the rheology, mechanical properties, durability, 3D printing, and microstructural performance of cementitious materials with NMs and encourages further research.

Theoretical analysis on the thermal buckling behavior of material-filled truss-core sandwich plates with various boundary conditions

Abstract: Abstract This article presents a theoretical analysis of the thermal buckling behavior of material-filled truss-core sandwich panels, which have load-bearing capacities and multiple functions. The homogeneous stiffness matrix of the filled truss core is solved by the energy equilibrium method. The governing equations are derived based on Hamilton’s principle. The eigenvalue method is used to solve the governing equations, and the buckling matrix is determined. By simplifying this buckling matrix, buckling formula has been derived. A three-dimensional finite element model is developed to validate the theoretical results. Finally, the effects of geometrical parameters, material properties and boundary conditions on the thermal buckling temperature of material-filled sandwich panels are analyzed.

3D Concrete Printing: A Systematic Review of Rheology, Mix Designs, Mechanical, Microstructural, and Durability Characteristics

Abstract: This paper provides a state-of-the-art report on the up-to-date research on the emerging 3D concrete printing technology from the concrete materials perspective. It reviews the recent research focused on understanding and characterizing the rheological necessities of the concrete printing process and discusses how the researchers are tailoring compatible mix proportions for the 3D concrete printing process by using eco-friendly binders, waste aggregates, chemical admixtures, and nano-additives. This paper systematically evaluates anisotropic behavior in the mechanical properties of printed concrete and establishes an order for anisotropic behavior in the compressive, flexural, and tensile strengths along three different axes (X, Y, and Z axes) of printed concrete. It evaluates the ratio of flexural strength to the compressive strength of printed concrete along the above three axes. This article explains the influence of variation of printing process parameters on the mechanical properties and discusses reinforcement approaches used for increasing structural performance. The microstructure at the interface of adjacent layers and also at the interface of the reinforcement-cement matrix is discussed. The recent research on the durability performance of printed concrete is critically discussed and future research needs for 3D concrete printing are identified in this paper.

Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms

Abstract: Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R2), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model’s performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties.

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

Abstract: Geopolymers may be the best alternative to ordinary Portland cement because they are manufactured using waste materials enriched in aluminosilicate. Research on geopolymer composites is accelerating. However, considerable work, expense, and time are needed to cast, cure, and test specimens. The application of computational methods to the stated objective is critical for speedy and cost-effective research. In this study, supervised machine learning approaches were employed to predict the compressive strength of geopolymer composites. One individual machine learning approach, decision tree, and two ensembled machine learning approaches, AdaBoost and random forest, were used. The coefficient correlation (R2), statistical tests, and k-fold analysis were used to determine the validity and comparison of all models. It was discovered that ensembled machine learning techniques outperformed individual machine learning techniques in forecasting the compressive strength of geopolymer composites. However, the outcomes of the individual machine learning model were also within the acceptable limit. R2 values of 0.90, 0.90, and 0.83 were obtained for AdaBoost, random forest, and decision models, respectively. The models’ decreased error values, such as mean absolute error, mean absolute percentage error, and root-mean-square errors, further confirmed the ensembled machine learning techniques’ increased precision. Machine learning approaches will aid the building industry by providing quick and cost-effective methods for evaluating material properties.

Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms.

Abstract: The innovation of geopolymer concrete (GPC) plays a vital role not only in reducing the environmental threat but also as an exceptional material for sustainable development. The application of supervised machine learning (ML) algorithms to forecast the mechanical properties of concrete also has a significant role in developing the innovative environment in the field of civil engineering. This study was based on the use of the artificial neural network (ANN), boosting, and AdaBoost ML approaches, based on the python coding to predict the compressive strength (CS) of high calcium fly-ash-based GPC. The performance comparison of both the employed techniques in terms of prediction reveals that the ensemble ML approaches, AdaBoost, and boosting were more effective than the individual ML technique (ANN). The boosting indicates the highest value of R2 equals 0.96, and AdaBoost gives 0.93, while the ANN model was less accurate, indicating the coefficient of determination value equals 0.87. The lesser values of the errors, MAE, MSE, and RMSE of the boosting technique give 1.69 MPa, 4.16 MPa, and 2.04 MPa, respectively, indicating the high accuracy of the boosting algorithm. However, the statistical check of the errors (MAE, MSE, RMSE) and k-fold cross-validation method confirms the high precision of the boosting technique. In addition, the sensitivity analysis was also introduced to evaluate the contribution level of the input parameters towards the prediction of CS of GPC. The better accuracy can be achieved by incorporating other ensemble ML techniques such as AdaBoost, bagging, and gradient boosting.