Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques

There has been a persistent drive for sustainable development in the concrete industry. While there are series of encouraging experimental research outputs, yet the research field requires a standard framework for the material development. In this study, the strength characteristics of geopolymer self-compacting concrete made by addition of mineral admixtures, have been modelled with both genetic programming (GEP) and the artificial neural networks (ANN) techniques. The study adopts a 12M sodium hydroxide and sodium silicate alkaline solution of ratio to fly ash at 0.33 for geopolymer reaction. In addition to the conventional material (river sand), fly ash was partially replaced with silica fume and granulated blast furnace slag. Various properties of the concrete, filler ability and passing ability of fresh mixtures, and compressive, split-tensile and flexural strength of hardened concrete were determined. The model developmentinvolved using raw materials and fresh mix properties as predictors, and strength properties as response. Results shows that the use of the admixtures enhanced both the fresh and hardened properties of the concrete. Both GEP and ANN methods exhibited good prediction of the experimental data, with minimal errors. However, GEP models can be preferred as simple equations are developed from the process, while ANN is only a predictor

Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques

tThere has been a persistent drive for sustainable development in the concrete industry.While there are series of encouraging experimental research outputs, yet the research fieldrequires a standard framework for the material development. In this study, the strengthcharacteristics of geopolymer self-compacting concrete made by addition of mineral admix-tures, have been modelled with both genetic programming (GEP) and the artificial neuralnetworks (ANN) techniques. The study adopts a 12M sodium hydroxide and sodium sili-cate alkaline solution of ratio to fly ash at 0.33 for geopolymer reaction. In addition to theconventional material (river sand), fly ash was partially replaced with silica fume and gran-ulated blast furnace slag. Various properties of the concrete, filler ability and passing abilityof fresh mixtures, and compressive, split-tensile and flexural strength of hardened concretewere determined. The model development involved using raw materials and fresh mix prop-erties as predictors, and strength properties as response. Results shows that the use of theadmixtures enhanced both the fresh and hardened properties of the concrete. Both GEP andANN methods exhibited good prediction of the experimental data, with minimal errors.However, GEP models can be preferred as simple equations are developed from the process,while ANN is only a predictor

Utilizing Artificial Neural Network and Multiple Linear Regression to Model the Compressive Strength of Recycled Geopolymer Concrete

Based on the heterogeneity nature of concrete constituents and variation in its compressive strength over several orders of magnitude for various types of concrete, predictive methods to evaluate its compressive strength has now been given an appropriate consideration. Therefore, this study investigates, the performance of Artificial Neural Network, ANN in forecasting the compressive strength of hybrid alkali-activated recycled concrete (HAARC) and compared with the traditional Multiple Linear Regression, MLR. The developed models utilized the experimental results where varying material quantities were used. The ANN and MLR models were developed using eight input variables namely; Ordinary Portland cement (OPC), Rice Husk Ash (RHA), Coal Fly Ash (CFA), Crushed granite (CG), Cupola Furnace Slag (CFS), Alkaline Solution (AS), Water-Binder Ratio (WB) and the Concrete Age (CA) while compressive strength was the only response (predicted) variable. The input data were trained, tested and validated using feedforward back-proportion and backward elimination approach for ANN and MLR, respectively. The most probable model architecture containing eight-input layer, thirteen-hidden layer, and one-output layer neurons was selected based on satisfactory performance in terms of means square error MSE, after several trials. MLR results revealed that only three input variables; CFA, CG and CA proves to be statistically significant with p-values below 0.05. Performance evaluations of the developed models using coefficient of determination, R2, Mean Square Error, MSE, Root Mean-Square Error, RMSE and Mean Absolute Percentage Error, MAPE, showed that ANN prediction accuracy is better than that of MLR with R2 = 0.9972, MSE = 0.4177, RMSE = 1.8201, MAPE = 2.2935 for ANN and R2 = 0.7410, MSE = 66.6308, RMSE = 290.4370, MAPE = 385.5221 for MLR

Artificial intelligence-based prediction of strengths of slag-ash-based geopolymer concrete using deep neural networks

The construction and building industry, one of the greatest emitters of greenhouse gases, is under tremendous pressure because of the growing concern about global climate change and its detrimental effects on societies. Given the environmental problems connected to cement production, geopolymer concrete has become a viable alternative. In addition, if the concrete strength results failed to meet the specified strength after being cast, modifications are impossible. Thus, it is particularly desirable to predict strength prior to casting concrete. This study presents the first effort in applying deep neural networks (DNN) of AI techniques to predict the mechanical strengths (GGBFS) of geopolymer concrete (GPC) produced from corncob ash and ground granulated blast furnace slag. The mixes were activated with 12–16 M of alkali solutions at ambiently cured conditions for 7–90 days. Following that, back propagation learning algorithms were created for forecasting the concrete strengths based on concrete mix proportions. The mechanical strengths estimated by the DNN were verified by laboratory testing results. Results revealed that GGBFS, mix grade, curing days, and alkali precursor are variables that govern the mechanical strengths of the GGBFS-CCA-GPC. Forecasting the mechanical properties of GPC produced using DNN shows that the relationship between the input and output arguments could be most accurately predicted by a 10–20–20–20-1 network topology, evident by approximately 99% correlation coefficient between the actual and predictive values for compressive and flexural strengths. However, the 10–17–17–17-1 network architecture showed the best DNN for predicting split tensile strength, with a 97% correlation coefficient between the actual and projected values. This study demonstrated that the DNN techniques are efficient in predicting the mechanical strengths of GPC based on the mix proportions. Application of these techniques will greatly advance concrete quality assurance

Advances in Binders for Construction Materials

The global binder production for construction materials is approximately 7.5 billion tons per year, contributing ~6% to the global anthropogenic atmospheric CO2 emissions. Reducing this carbon footprint is a key aim of the construction industry, and current research focuses on developing new innovative ways to attain more sustainable binders and concrete/mortars as a real alternative to the current global demand for Portland cement.With this aim, several potential alternative binders are currently being investigated by scientists worldwide, based on calcium aluminate cement, calcium sulfoaluminate cement, alkali-activated binders, calcined clay limestone cements, nanomaterials, or supersulfated cements. This Special Issue presents contributions that address research and practical advances in i) alternative binder manufacturing processes; ii) chemical, microstructural, and structural characterization of unhydrated binders and of hydrated systems; iii) the properties and modelling of concrete and mortars; iv) applications and durability of concrete and mortars; and v) the conservation and repair of historic concrete/mortar structures using alternative binders.We believe this Special Issue will be of high interest in the binder industry and construction community, based upon the novelty and quality of the results and the real potential application of the findings to the practice and industry

Machine Learning Prediction of Mechanical and Durability Properties of Recycled Aggregates Concrete

Whilst recycled aggregate (RA) can alleviate the environmental footprint of concrete production and the landfilling of colossal amounts of demolition waste, there need for robust predictive tools for its effects on mechanical and durability properties. In this thesis, state-of-the-art machine learning (ML) models were deployed to predict properties of recycled aggregate concrete (RAC). A systematic review was performed to analyze pertinent ML techniques previously applied in the concrete technology field. Accordingly, three different ML methods were selected to determine the compressive strength of RAC and perform mixture proportioning optimization. Furthermore, a gradient boosting regression tree was used to study the effects of RA and several types of binders on the carbonation depth of RAC. The ML models developed in this study demonstrated robust performance to predict diverse properties of RAC

Recycled Materials in Civil and Environmental Engineering

The aim of this reprint was to report recent innovative studies based on the use of secondary raw materials for applications in civil and environmental engineering. To this purpose, papers were related to the preparation of innovative construction materials and to the treatment of wastes for environmental applications. The investigations were characterized by a common purpose, i.e., to find a way to reduce the amount of waste generated, thus reducing the need for landfilling and optimizing the values of these novel materials, which are an abundant resource that can be easily reused for different applications

Sustainable Structural Design for High-Performance Buildings and Infrastructures

Exceptional design loads on buildings and structures may have different causes, including high-strain natural hazards, man-made attacks and accidents, and extreme operational conditions. All of these aspects can be critical for specific structural typologies and/or materials that are particularly sensitive. Dedicated and refined methods are thus required for design, analysis, and maintenance under structures’ expected lifetimes. Major challenges are related to the structural typology and material properties. Further issues are related to the need for the mitigation or retrofitting of existing structures, or from the optimal and safe design of innovative materials/systems. Finally, in some cases, no design recommendations are available, and thus experimental investigations can have a key role in the overall process. For this SI, we have invited scientists to focus on the recent advancements and trends in the sustainable design of high-performance buildings and structures. Special attention has been given to materials and systems, but also to buildings and infrastructures that can be subjected to extreme design loads. This can be the case of exceptional natural events or unfavorable ambient conditions. The assessment of hazard and risk associated with structures and civil infrastructure systems is important for the preservation and protection of built environments. New procedures, methods, and more precise rules for safety design and the protection of sustainable structures are, however, needed