Advances in Materials Science and Engineering

Cement manufacturing and utilization is one of the majorly responsible factors for global CO₂ emissions. In light of sustainability and climate change concerns, it is essential to find alternative solutions to reduce the carbon footprint of cement. Secondary cementitious materials (SCMs) are helpful in reducing carbon emissions from concrete. One such solution is the use of agricultural waste as SCMs to reduce carbon emissions from concrete. Especially rice husk ash (RHA) is a silica-rich, globally available agricultural waste material. The compressive strength (CS) of concrete is important and is used to evaluate the material’s strength and durability. Predicting CS using a laboratory method is a costly, time-consuming, and complex process. ML-based prediction models are the modern solution to these problems. In this study, a total of 407 datasets are used to develop an ML-based model by using the ANN algorithm to predict the CS of concrete containing RHA. Cement, coarse aggregates, fine aggregates, water, rice husk ash, superplasticizer, and type of sample are used as input parameters to predict CS at 28 days. Various statistical parameters including correlation coefficient (R), root means square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash-Sutcliffe (NS), and the a20-index have been used to assess the performance of the developed ANN model. The R and RMSE values of training, validation, and testing samples are 0.9928, 0.9864, and 0.9545, and 1.6471 MPa, 2.7149 MPa, and 4.4334 MPa, respectively. The results obtained from this study have been found to be promising and enrich the available literature. This work will nudge civil engineering and material science researchers toward opting for sustainable computing techniques. However, the study’s limitations include the need for additional research into the material’s long-term behaviour as well as the consideration of other characteristics that may affect its strength, such as environmental conditions like temperature and humidity.

Kaolin mineral is a commercially solid powder with a comparatively low level of purity and is regularly used for a variety of applications, including filler, paints, ceramics, adsorbents, and paper. In Ethiopia, the kaolin clay mineral is significant for financial growth as the raw material used in the industry sector. However, slight consideration was given to the chemical, physical, mineralogical, and morphological properties of kaolin. In this study, the property of kaolin is investigated by using advanced instruments such as X-ray diffraction (XRD), X-ray fluorescence analysis (XRF), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and differential thermogravimetric analysis (DTA). Based on the XRF test, the main component of kaolin clay contains SiO₂ (58.73%), Al₂O₃ (24.35%), K₂O (5.36%), and other impurities, including Fe₂O₃ (2.06%) and TiO₂ (0.13%). The FTIR spectra displayed the functional groups Si-O, Al-OH, Al-O, and Si-O-Al. The XRD diffractogram identified kaolin clay as the main mineral phase in the existence of quartz, halloysite, and chlorite.

Mechanical properties are important indicators for evaluating the quality of strips. This paper proposes a mechanical performance prediction model based on the Gray Wolf Optimization (GWO) algorithm and the Extreme Learning Machine (ELM) algorithm. In the modeling process, GWO is used to determine the optimal weights and deviations of ELM and experiments are used to determine the model’s key parameters. The model effectively avoids manual intervention and significantly improves aluminum alloy strips’ mechanical property prediction accuracy. This paper uses processed data from the aluminum alloy production plant of Shandong Nanshan Aluminum Co., Ltd. as experimental data. When the prediction deviation is controlled within ±10%, the GWO-ELM model can achieve a correct rate of 100% for tensile strength, 97.5% for yield strength, and 77.5% for elongation on the test set. The RMSE of the tensile strength, yield strength, and elongation of the GWO-ELM model was 5.365, 11.881, and 1.268, respectively. The experimental results show that the GWO-ELM model has higher accuracy and stability in predicting aluminum alloy strips’ tensile strength, yield strength, and elongation. The GWO-ELM model effectively avoids the defects of the traditional model. It has a special guiding significance for producing aluminum alloy strips.

This paper aims to address the issue of disturbance caused by excavation tunnel construction on nearby dense pipelines. Relying on the actual project of Nanchang Metro, the three-dimensional finite element numerical simulation method is used to establish a multi-condition numerical model. At the same time, key influencing factors such as the clear distance, intersection angle, surrounding rock parameter, and construction method are considered. The mechanical response of the dense pipeline adjacent to the dug tunnel was systematically studied, and the influence of key factors on the mechanical behavior of the pipeline was analyzed. The results show that when using the CRD method for construction and grouting reinforcement of the surrounding strata of the tunnel, the maximum settlement of the vault is located at the vault of the right guide tunnel, and the maximum settlement value is located at the bottom of the rainwater pipe directly above the tunnel. The vertical displacement, maximum principal stress, and minimum principal stress of the pipeline are arranged in descending order for the full-face, stepped method, and CRD method. The greater the disturbance to the soil caused by the construction method, the more unfavorable the effect on the pipeline.

Modelling of engineered barrier systems (EBS) in deep geological disposals of spent nuclear fuel requires sophisticated approaches that include highly nonlinear constitutive models. The Barcelona Basic Model for Expansive soils (BBMEx) is an extension for highly expansive unsaturated soils of the Barcelona Basic Model (BBM) for slightly-to-moderately expansive unsaturated soils. In this extension, the parameter (logarithmic compliance with respect to changes in net pressure) of the BBM is extended to a function of suction and a set of parameters, and the parameter (logarithmic compliance with respect to changes in suction) of the BBM is extended to a function of net pressure, suction, and a set of parameters. On these functions, four conditions which are satisfied in the BBM are considered. For each condition, two results are obtained: (1) the ranges of the values of the parameters such that the condition is satisfied for all positive net pressures and all non-negative suctions and (2) for given values of the parameters, the maximum net pressure and the maximum suction such that the condition is satisfied for positive net pressures and non-negative suctions. The results should help prevent unrealistic predictions of the BBMEx. The extension was used for carrying out the simulation of an infiltration test in two different bentonites and the BBMEx model parameters analysed.

The paper proposes a deep hybrid forest regression-based modeling method for measuring the compressive strength (CS) of concrete. Then, the reduced feature vector is used as input to train multiple subforest models (SFM), the predicted values are selected from multiple subforests via the KNN (K-nearest neighbor) method to combine them to obtain the layer regression vector (LRV), and it is combined with the reduced feature vector to obtain the improved LRV, then the output of this layer is taken; second, the regression vector (RV) of the input layer enhancement layer is used as input to obtain the output of the second layer FM, and the steps are repeated until the output of the input layer FM is complete. Finally, the output of the FM of the first layer is obtained. Several SFMs are trained and the result is obtained. The final prognosis is obtained by arithmetically averaging the forecast results of the SFMs of this layer.