Mechanical Properties And Durability Performance Of POFA-Based Engineered Alkali-Activated Cementitious Composites

Palm oil fuel ash (POFA) has been partially used over the years in developing concrete and mortar. However, to save the environment of POFA waste from dumpsites causing health issues and to reduce the consumption of Portland cement, 100% POFA was used in this study. The properties and performance of POFA alkali-activated mortar and POFA engineered alkali-activated cementitious composite (POFA-EACC) have been studied. The aim of the research is to develop POFA-EACC with good mechanical strength and better matrix durability properties. This aim was achieved through the utilization of POFA as the only base material for the POFA-EACC. The study encompasses the suitability evaluation of POFA alkali-activated mortar matrix (without polyvinyl alcohol (PVA) fibers) for POFA-EACC, the durability performance of the alkali-activated mortar matrix and the composite mechanical performance of the POFA-EACC (with PVA fibers). The alkali-activated mortar mixtures were prepared with different NaOH molarities (10, 12 and 14M), Na2SiO3(NS)+NaOH(NH)/POFA ratios of 0.4, 0.5 and 0.6, NS/NH ratios of 2.5, 2, 1 and 0.5, constant sand/POFA ratio of 1.5 and 2% volume fraction for PVA fibres (for POFA-EACC) were used. The results revealed a huge potential in the use of POFA as base material for alkaline activation. The optimum mixture consists of design parameters: NS/NH = 2.5, (NS+NH)/POFA = 0.4 and NaOH molarity = 10M. The highest compressive strength of 23.47 MPa and approximately 25 MPa were recorded after just three days and 28 days of curing respectively. The developed POFA alkali-activated binder has as its main products, N-A-S-H gel with some C-(A)-S-H gel forming in tandem within the alkali-activated mortar microstructure. Attempt were also made to improve the compressive strength of low-alumina POFA alkali-activated binder through the addition of different percentages of Al(OH)3, but results showed some losses in strength. The compressive strength of the optimum mixture reduced from approximately 25 MPa to 18 MPa, the lowest with the Al(OH)3 addition. In addition, the POFA alkali-activated mortar performed excellently in durability with H2SO4 and MgSO4 exposures in comparison to Na2SO4, HCl and HNO3. The obtained results were in agreement with the SEM/EDS, XRD and FTIR analyses. In addition, the tensile and flexural properties of POFA-EACC is very much dependent on the properties of POFA alkali-activated mortar and the interfacial bond between the POFA mortar matrix and PVA fibres. The uniaxial compressive strength of POFA-EACC reduced in comparison with those without PVA fibers due to the fibre-POFA matrix interface weakness. Although the POFA-EACC showed appreciable ductility as revealed in the tensile strain capacity results, the ultimate tensile and first cracking strength is generally low. Generally, the mixture parameters especially the NaOH molarity and water content greatly influenced all the properties of the POFA-EACC

Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming

The impact of microbial calcium carbonate on concrete strength has been extensively evaluated in the literature. However, there is no predicted equation for the compressive strength of concrete incorporating ureolytic bacteria. Therefore, in the present study, 69 experimental tests were taken into account to introduce a new predicted mathematical formula for compressive strength of bacterial concrete with different concentrations of calcium nitrate tetrahydrate, urea, yeast extract, bacterial cells and time using Gene Expression Programming (GEP) modelling. Based on the results, statistical indicators (MAE, RAE, RMSE, RRSE, R and R2) proved the capability of the GEP 2 model to predict compressive strength in which minimum error and high correlation were achieved. Moreover, both predicted and actual results indicated that compressive strength decreased with the increase in nutrient concentration. In contrast, the compressive strength increased with increased bacterial cells concentration. It could be concluded that GEP2 were found to be reliable and accurate compared to that of the experimental results

Compressive Strength Estimation of Fly Ash/Slag Based Green Concrete by Deploying Artificial Intelligence Models

Cement production is one of the major sources of decomposition of carbonates leading to the emission of carbon dioxide. Researchers have proven that incorporating industrial wastes is of paramount significance for producing green concrete due to the benefits of reducing cement production. The compressive strength of concrete is an imperative parameter to consider when designing concrete structures. Considering high prediction capabilities, artificial intelligence models are widely used to estimate the compressive strength of concrete mixtures. A variety of artificial intelligence models have been developed in the literature; however, evaluation of the modeling procedure and accuracy of the existing models suggests developing such models that manifest the detailed evaluation of setting parameters on the performance of models and enhance the accuracy compared to the existing models. In this study, the computational capabilities of the adaptive neurofuzzy inference system (ANFIS), gene expression programming (GEP), and gradient boosting tree (GBT) were employed to investigate the optimum ratio of ground-granulated blast furnace slag (GGBFS) and fly ash (FA) to the binder content. The training process of GEP modeling revealed 200 chromosomes, 5 genes, and 12 head sizes as the best hyperparameters. Similarly, ANFIS hybrid subclustering modeling with aspect ratios of 0.5, 0.1, 7, and 150; learning rate; maximal depth; and number of trees yielded the best performance in the GBT model. The accuracy of the developed models suggests that the GBT model is superior to the GEP, ANFIS, and other models that exist in the literature. The trained models were validated using 40% of the experimental data along with parametric and sensitivity analysis as second level validation. The GBT model yielded correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE), equaling 0.95, 3.07 MPa, and 4.80 MPa for training, whereas, for validation, these values were recorded as 0.95, 3.16 MPa, and 4.85 MPa, respectively. The sensitivity analysis revealed that the aging of the concrete was the most influential parameter, followed by the addition of GGBFS. The effect of the contributing parameters was observed, as corroborated in the literature

GPT models in construction industry: Opportunities, limitations, and a use case validation

Large Language Models (LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the opportunities and challenges of using LLMs in the construction industry. Thus, this study aims to assess GPT models in the construction industry. A critical review, expert discussion and case study validation are employed to achieve the study's objectives. The findings revealed opportunities for GPT models throughout the project lifecycle. The challenges of leveraging GPT models are highlighted and a use case prototype is developed for materials selection and optimization. The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry

Building energy loads prediction using bayesian-based metaheuristic optimized-explainable tree-based model

The study presents a sophisticated hybrid machine learning methodology tailored for predicting energy loads in occupied buildings. Leveraging eight pivotal input features—building compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution—we elucidate the intricate relationships between building characteristics and their corresponding heating load (HL) and cooling load (CL). We meticulously analyze these features across 12 diverse structural forms, each emblematic of unique architectural designs and building materials. Using a dataset encompassing 768 buildings, we demonstrate the prowess of our proposed models. Among the algorithms we employed, the extreme gradient boosting algorithm stands out, registering impressive accuracy metrics (HL: RSQ = 0.9986, RMSE = 0.3797, MAE = 0.2467 and MAPE = 1.1812; CL: RSQ = 0.9938, RMSE = 0.7578, MAE = 0.4546 and MAPE = 1.6365). We further integrate SHAP analysis, revealing that relative compactness positively influences both HL and CL the most, closely followed by surface area and glazing area. By merging an explainable extreme gradient boosting algorithm with a Bayesian-based metaheuristic optimization technique, we ensure both high predictive accuracy and interpretability. This study holds profound implications for enhancing building energy efficiency, curbing waste, and championing the shift to sustainable energy sources, aligning seamlessly with SDG 7

MXenes:Synthesis, properties, and applications for sustainable energy and environment

MXenes, the largest and most diverse group of emerging two-dimensional materials, have potentials across multiple applications. The increasing attention is driven by the fascinating tunable surface properties, and synergetic chemistry facilitated by the presence of multiple chemical bonds dominated by covalent and metallic bonds between transition metals (M) and non-metals, X (such as carbon, nitrogen, or both). Although the various available synthesis approaches offer opportunities for tuning MXenes for specific applications, their inherent environmental and toxicity risks as well as poor scale-up potential are currently hampering research and commercialization progress. Therefore, ongoing efforts are focused on developing less hazardous, scalable methodologies to limit the barriers. This review comprehensively surveys the literature from the seminal MXene paper to the present, offering insights into the factors, latest advancements, limitations, trends, and existing gaps in MXene synthesis, properties, and applications in the areas of environment and energy storage. Special emphasis is placed on the need to address environmental concerns associated with fluoride-based synthesis while an overview of safer, non-fluoride alternatives is provided. Furthermore, the most recent breakthroughs in scalable top-down dry selective etching (DSE), bottom-up solid-state direct synthesis, and structural editing protocol using chemical scissors are presented. In particular, this review critically examines the current state of knowledge and identifies key research progresses and areas that deserve intensive attention to facilitate safe and industrial-scale synthesis and application of MXenes for catalysis, environmental remediation, and energy storage. Addressing the identified gaps will accelerate the transformation of MXenes and their composites into the components of our everyday appliances

Strength and acid resistance of ceramic-based self-compacting alkali-activated concrete: Optimizing and predicting assessment

10.3390/ma14206208Materials1420620

Prediction of Strength and CBR Characteristics of Chemically Stabilized Coal Gangue: ANN and Random Forest Tree Approach

Coal mining waste in the form of coal gangue (CG) was established recently as a potential fill material in earthworks. To ascertain this potential, this study forecasts the strength and California Bearing Ratio (CBR) characteristics of chemically stabilized CG by deploying two widely used artificial intelligence approaches, i.e., artificial neural network (ANN) and random forest (RF) regression. In this research work, varied dosage levels of lime (2, 4, and 6%) and gypsum (0.5, 1, and 1.5%) were employed for determining the unconfined compression strength (UCS) and CBR of stabilized CG mixes. An experimental study comprising 384 datasets was conducted and the resulting database was used to develop the ANN and RF regression models. Lime content, gypsum dosage, and 28 d curing period were considered as three input attributes in obtaining three outputs (i.e., UCS, unsoaked CBR, and soaked CBR). While modelling with the ANN technique, different algorithms, hidden layers, and the number of neurons were studied while selecting the optimum model. In the case of RF regression modelling, optimal grid comprising maximal depth of tree, number of trees, confidence, random splits, enabled parallel execution, and guess subset ratio were investigated, alongside the variable number of folds, to obtain the best model. The optimum models obtained using the ANN approach manifested relatively better performance in terms of correlation coefficient values, equaling 0.993, 0.995, and 0.997 for UCS, unsoaked CBR and soaked CBR, respectively. Additionally, the MAE values were observed as 45.98 kPa, 1.41%, and 1.18% for UCS, unsoaked CBR, and soaked CBR, respectively. The models were also validated using 2-stage validation processes. In the first stage of validation of the model (using unseen 30% of the data), it was revealed that reliable performance of the models was attained, whereas in the second stage (parametric analysis), results were achieved which are corroborated with those in existing literature

Prediction Models for Estimating Compressive Strength of Concrete Made of Manufactured Sand Using Gene Expression Programming Model

The depletion of natural resources of river sand and its availability issues as a construction material compelled the researchers to use manufactured sand. This study investigates the compressive strength of concrete made of manufactured sand as a partial replacement of normal sand. The prediction model, i.e., gene expression programming (GEP), was used to estimate the compressive strength of manufactured sand concrete (MSC). A database comprising 275 experimental results based on 11 input variables and 1 target variable was used to train and validate the developed models. For this purpose, the compressive strength of cement, tensile strength of cement, curing age, Dmax of crushed stone, stone powder content, fineness modulus of the sand, water-to-binder ratio, water-to-cement ratio, water content, sand ratio, and slump were taken as input variables. The investigation of a varying number of genetic characteristics, such as chromosomal number, head size, and gene number, resulted in the creation of 11 alternative models (M1-M11). The M5 model outperformed other created models for the training and testing stages, with values of (4.538, 3.216, 0.919) and (4.953, 3.348, 0.906), respectively, according to the results of the accuracy evaluation parameters root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The R2 and error indices values revealed that the experimental and projected findings are in extremely close agreement. The best model has 200 chromosomes, 8 head sizes, and 3 genes. The mathematical expression achieved from the GEP model revealed that six parameters, namely the compressive and tensile strength of cement, curing period, water–binder ratio, water–cement ratio, and stone powder content contributed effectively among the 11 input variables. The sensitivity analysis showed that water–cement ratio (46.22%), curing period (25.43%), and stone powder content (13.55%) were revealed as the most influential variables, in descending order. The sensitivity of the remaining variables was recorded as w/b (11.37%) > fce (2.35%) > fct (1.35%)

Investigating the Bond Strength of FRP Laminates with Concrete Using LIGHT GBM and SHAPASH Analysis

The corrosion of steel reinforcement necessitates regular maintenance and repair of a variety of reinforced concrete structures. Retrofitting of beams, joints, columns, and slabs frequently involves the use of fiber-reinforced polymer (FRP) laminates. In order to develop simple prediction models for calculating the interfacial bond strength (IBS) of FRP laminates on a concrete prism containing grooves, this research evaluated the nonlinear capabilities of three ensemble methods—namely, random forest (RF) regression, extreme gradient boosting (XGBoost), and Light Gradient Boosting Machine (LIGHT GBM) models—based on machine learning (ML). In the present study, the IBS was the desired variable, while the model comprised five input parameters: elastic modulus x thickness of FRP (EfTf), width of FRP plate (bf), concrete compressive strength (fc′), width of groove (bg), and depth of groove (hg). The optimal parameters for each ensemble model were selected based on trial-and-error methods. The aforementioned models were trained on 70% of the entire dataset, while the remaining data (i.e., 30%) were used for the validation of the developed models. The evaluation was conducted on the basis of reliable accuracy indices. The minimum value of correlation of determination (R2 = 0.82) was observed for the testing data of the RF regression model. In contrast, the highest (R2 = 0.942) was obtained for LIGHT GBM for the training data. Overall, the three models showed robust performance in terms of correlation and error evaluation; however, the trend of accuracy was obtained as follows: LIGHT GBM > XGBoost > RF regression. Owing to the superior performance of LIGHT GBM, it may be considered a reliable ML prediction technique for computing the bond strength of FRP laminates and concrete prisms. The performance of the models was further supplemented by comparing the slopes of regression lines between the observed and predicted values, along with error analysis (i.e., mean absolute error (MAE), and root-mean-square error (RMSE)), predicted-to-experimental ratio, and Taylor diagrams. Moreover, the SHAPASH analysis revealed that the elastic modulus x thickness of FRP and width of FRP plate are the factors most responsible for IBS in FRP