Modelling the mechanical properties of concrete produced with polycarbonate waste ash by machine learning.

India's cement industry is the second largest in the world, generating 6.9% of the global cement output. Polycarbonate waste ash is a major problem in India and around the globe. Approximately 370,000 tons of scientific waste are generated annually from fitness care facilities in India. Polycarbonate waste helps reduce the environmental burden associated with disposal and decreases the need for new raw materials. The primary variable in this study is the quantity of polycarbonate waste ash (5, 10, 15, 20 and 25% of the weight of cement), partial replacement of cement, water-cement ratio and aggregates. The mechanical properties, such as compressive strength, split tensile strength and flexural test results, of the mixtures with the polycarbonate waste ash were superior at 7, 14 and 28 days compared to those of the control mix. The water absorption rate is less than that of standard concrete. Compared with those of conventional concrete, polycarbonate waste concrete mixtures undergo minimal weight loss under acid curing conditions. Polycarbonate waste is utilized in the construction industry to reduce pollution and improve the economy. This study further simulated the strength characteristics of concrete made with waste polycarbonate ash using least absolute shrinkage and selection operator regression and decision trees. Cement, polycarbonate waste, slump, water absorption, and the ratio of water to cement were the main components that were considered input variables. The suggested decision tree model was successful with unparalleled predictive accuracy across important metrics. Its outstanding predictive ability for split tensile strength (R2 = 0.879403), flexural strength (R2 = 0.91197), and compressive strength (R2 = 0.853683) confirmed that this method was the preferred choice for these strength predictions.

Modeling the influence of lime on the unconfined compressive strength of reconstituted graded soil using advanced machine learning approaches for subgrade and liner applications.

In the field of soil mechanics, especially in transportation and environmental geotechnics, the use of machine learning (ML) techniques has emerged as a powerful tool for predicting and understanding the compressive strength behavior of soils especially graded ones. This is to overcome the sophisticated equipment, laboratory space and cost needs utilized in multiple experiments on the treatment of soils for environmental geotechnics systems. This present study explores the application of machine learning (ML) techniques, namely Genetic Programming (GP), Artificial Neural Networks (ANN), Evolutionary Polynomial Regression (EPR), and the Response Surface Methodology in predicting the unconfined compressive strength (UCS) of soil-lime mixtures. This was for purposes of subgrade and landfill liner design and construction. By utilizing input variables such as Gravel, Sand, Silt, Clay, and Lime contents (G, S, M, C, L), the models forecasted the strength values after 7 and 28 days of curing. The accuracy of the developed models was compared, revealing that both ANN and EPR achieved a similar level of accuracy for UCS after 7 days, while the GP model performed slightly lower. The complexity of the formula required for predicting UCS after 28 days resulted in decreased accuracy. The ANN and EPR models achieved accuracies of 85% and 82%, with R2 of 0.947 and 0.923, and average error of 0.15 and 0.18, respectively, while the GP model exhibited a lower accuracy of 66.0%. Conversely, the RSM produced models for the UCS with predicted R2 of more than 98% and 99%, for the 7- and 28- day curing regimes, respectively. The RSM also produced adequate precision in modelling UCS of more than 14% against the standard 7%. All input factors were found to have almost equal importance, except for the lime content (L), which had an average influence. This shows the importance of soil gradation in the design and construction of subgrade and landfill liners. This research further demonstrates the potential of ML techniques for predicting the strength of lime reconstituted G-S-M-C graded soils and provides valuable insights for engineering applications in exact and sustainable subgrade and liner designs, construction and performance monitoring and rehabilitation of the constructed civil engineering infrastructure.

Modeling the influence of bacteria concentration on the mechanical properties of self-healing concrete (SHC) for sustainable bio-concrete structures.

In this research paper, the intelligent learning abilities of the gray wolf optimization (GWO), multi-verse optimization (MVO), moth fly optimization, particle swarm optimization (PSO), and whale optimization algorithm (WOA) metaheuristic techniques and the response surface methodology (RSM) has been studied in the prediction of the mechanical properties of self-healing concrete. Bio-concrete technology stimulated by the concentration of bacteria has been utilized as a sustainable structural concrete for the future of the built environment. This is due to the recovery tendency of the concrete structures after noticeable structural failures. However, it requires a somewhat expensive exercise and technology to create the medium for the growth of the bacteria needed for this self-healing ability. The method of data gathering, analysis and intelligent prediction has been adopted to propose parametric relationships between the bacteria usage and the concrete performance in terms of strength and durability. This makes is cheaper to design self-healing concrete structures based on the optimized mathematical relationships and models proposed from this exercise. The performance of the models was tested by using the coefficient of determination (R2), root mean squared errors, mean absolute errors, mean squared errors, variance accounted for and the coefficient of error. At the end of the prediction protocol and model performance evaluation, it was found that the classified metaheuristic techniques outclassed the RSM due their ability to mimic human and animal genetics of mutation. Furthermore, it can be finally remarked that the GWO outclassed the other methods in predicting the concrete slump (Sl) with R2 of 0.998 and 0.989 for the train and test, respectively, the PSO outclassed the rest in predicting the flexural strength with R2 of 0.989 and 0.937 for train and test, respectively and the MVO outclassed the others in predicting the compressive strength with R2 of 0.998 and 0.958 for train and test, respectively.

Prediction of steel plate-based damper for improving the behavior of concentrically braced frames based on RSM and ML approaches for sustainable structures.

The stiffness (K) and slenderness factor (λ) of a steel plate-based damper has been studied on the basis of elastic-inelastic-plastic buckling (EIP) modes and flexural/shear/flexural-shear failure mechanisms (FSF-S), which has been designed for the improvement of the behavior of concentrically braced frames. Steel plate-based dampers offer significant benefits in terms of mode shapes and failure mechanisms, contributing to improved dynamic performance, enhanced structural resilience, and increased safety of civil engineering structures. Their effectiveness in mitigating dynamic loads makes them a valuable tool for engineers designing structures to withstand extreme environmental conditions and seismic events. This study was undertaken by using the learning abilities of the response surface methodology (RSM), artificial neural network (ANN) and the evolutionary polynomial regression (EPR). Steel plate dampers are special structural designs used to withstand the effect of special loading conditions especially seismic effects. Its design based on the prediction of its stiffness (K) and slenderness factor (λ) cannot be overlooked in the present-day artificial intelligence technology. In this research work, thirty-three entries based on the steel plate damper geometrical properties were recorded and deployed for the intelligent forecast of the fundamental properties (λ and K). Design ratios of the steel plate damper properties were considered and models behavior was recorded. From the outcome of the model, it can be observed that even though the EPR and ANN in that order outclassed the other techniques, the RSM produced model minimization and maximization features of the desirability levels, color factor scales and 3D surface observation, which shows the real model behaviors. Overall, the EPR with R2 of 0.999 and 1.000 for the λ and K, respectively showed to be the decisive model but the RSM has features that can be beneficial to the structural design of the studied steel plate damper for a more robust and sustainable construction. With these performances recorded in this exercise, the techniques have shown their potential to be applied in the prediction of steel damper stiffness with optimized characteristic features to withstand structural stresses.

Machine learning optimization of bio-sandcrete brick modelling using response surface methodology.

In this study, raw grinded groundnut shell (RGGNS) was used as a fine aggregate in the brick industry to reuse agricultural waste in building materials. In this study, an experimental approach was used to examine a new cement brick with raw groundnut shells integrated with compressive strength, water absorption and dry density optimization utilizing response surface methodology (RSM). The raw ground-nut shell content improved the fine aggregate performance of the 40%, 50%, and 60% samples. The 28-day high compressive strength with the raw ground-nut shell was 6.1 N/mm2 maximum, as needed by the technical standard. Samples made from 40%, 50%, and 60% raw groundnut shells yielded densities of 1.7, 2.2, and 1.9 kg/cm3 for groundnut shell (GNS) brick, respectively. A product's mechanical properties meet the IS code standard's minimum requirements. RSM was then utilized to develop a model for the addition of raw groundnut shell to concrete. R-square and Adeq precision values indicated that the results are highly significant, and equations for predicting compressive strength, water absorption, and dry density have been developed. In addition, optimization was performed on the RSM findings to determine the efficiency optimization of the model. Following the optimization results, experiments were conducted to determine the applicability of the optimized model.

Numerical modeling of the funnel multiphysical flow of fresh self-compacting concrete considering proportionate heterogeneity of aggregates.

Filling ability is one of the prominent rheological properties of the self-compacting concrete (SCC), which has been studied in this research work deploying the functional behavior of the concrete through the studied funnel apparatus using the coupled ANSYS-SPH interface. Seven (7) model cases were studied and optimized. The aim of this numerical study is to propose a more sustainable mix of coarse and fine aggregates proportion that allows for most minimum flow time to enhance a more efficient filling of forms during concreting. The maximum size of the coarse aggregates considered is 20 mm and that of the fine aggregates is below 4 mm. The Bingham model properties for the multiphysics (SPH)-ANSYS models' simulation are; viscosity = 20 ≤ µ ≤ 100 and the yield stress = 50 [Formula: see text], standard flow time, t (s) ranges; 6 ≤ t ≤ 25 and the funnel volume is 12 L. The minimum boundary flow time, which represents the time it takes for the SCC to completely flow through a specified distance, typically measured in seconds was modeled for in the seven (7) model cases. The second case with 40% coarse mixed with 60% fine completely flowed out in 16 s, thus fulfilling the minimum flow time. This minimum flow time was considered alongside other relevant parameters and tests, such as slump flow, passing ability, segregation resistance, and rheological properties (stresses), to comprehensively assess the filling ability of SCC in this model. By considering these factors and the optimized mix (40%C + 60%F:16s), engineers and researchers can optimize the SCC mix design to achieve the desired flowability and filling performance for their specific construction applications. The multiphase optimized mix was further simulated using the coupled interface of the ANSYS-SPH platform operating with the CFX command at air temperature of 25 °C. The results show energy reduction jump at the optimized flow time. Ideally, the mix, 40%C + 60%F:16s has been proposed as the mix with the most efficient flow to achieve the filling ability for sustainable structural concrete construction.

A critical review of rheological models in self-compacting concrete for sustainable structures.

Studying the rheological behavior of concrete, especially self-compacting concrete is vital in the design and structural integrity of concrete structures for design, construction, and structural material sustainability. Both analytical and numerical techniques have been applied in the previous research works to study precisely the behavior of the yield stress and plastic viscosity of the fresh self-compacting concrete with the associated flow properties and these results have not been systematically presented in a critical review, which will allow researchers, designers and filed operators the opportunity to be technically guided in their design and model techniques selection in order to achieve a more sustainable concrete model for sustainable concrete buildings. Also, the reported analytical and numerical techniques have played down on the effect of the shear strain rate behavior and as to reveal the viscosity changes of the Bingham material with respect to the strain rate. In this review paper, a critical study has been conducted to present the available methods from various research contributions and exposed the inability of these contributions to revealing the effect of the shear strain rate on the rheological behavior of the self-compacting concrete. With this, decisions related to the rheology and flow of the self-compacting concrete would have been made with apt and more exact considerations.

Recycling and sustainable applications of waste printed circuit board in concrete application and validation using response surface methodology.

The present investigation aims to examine the mechanical and durability properties of concrete that has been reinforced with a waste printed circuit board (WPCB) towards a low-carbon built environment. It assessed the fresh and hardened characteristics of the low-carbon concrete reinforced with WPCB fibres, after a curing period of 7 and 28 days. The evaluation was done by quantifying slump, compressive strength, split tensile strength, flexural strength, sorptivity, rapid, and acid tests. It further analysed eleven discrete concrete mixes with WPCB fibres at a weight percentage ranging from 1 to 5% in the cement mixture. The results indicate that incorporating WPCB fibre into concrete improves its mechanical strength. The results revealed that incorporating 5% WPCB fibre yielded the most favourable outcomes. The properties of WPCB fibre-reinforced concrete have been theoretically validated through Response Surface Methodology (RSM), which employs various statistical and mathematical tools to analyse the experimental data. The results derived from RSM were compared with the experimental results. It was found that the RSM model demonstrated a high level of accuracy (R2 ≥ 0.98) in validating the mechanical properties of WPCB fibre concrete. The statistical model exhibited no indication of prediction bias and demonstrated a statistically significant outcome, with a p-value below 0.5.

Soft computing techniques for predicting the properties of raw rice husk concrete bricks using regression-based machine learning approaches.

In this study, the replacement of raw rice husk, fly ash, and hydrated lime for fine aggregate and cement was evaluated in making raw rice husk-concrete brick. This study optimizes compressive strength, water absorption, and dry density of concrete brick containing recycled aggregates via Response Surface Methodology. The optimized model's accuracy is validated through Artificial Neural Network and Multiple Linear Regression. The Artificial Neural Network model captured the 100 data's variability from RSM optimization as indicated by the high R threshold- (R > 0.9997), (R > 0.99993), (R > 0.99997). Multiple Linear Regression model captured the data's variability the decent R2 threshold confirming- (R2 > 0.9855), (R2 > 0.9768), (R2 > 0.9155). The raw rice husk-concrete brick 28-day compressive strength, water absorption, and density prediction were more accurate when using Response Surface Methodology and Artificial Neural Network compared to Multiple Linear Regression. Lower MAE and RMSE, coupled with higher R2 values, unequivocally indicate the model's superior performance. Additionally, employing sensitivity analysis, the influence of the six input parameters on outcomes was assessed. Machine learning aids efficient prediction of concrete's mechanical properties, conserving time, labor, and resources in civil engineering.

Extensive overview of soil constitutive relations and applications for geotechnical engineering problems.

A state-of-the-art review has been conducted in this work on soil constitutive modeling, which has emphasized on: soil type, ground-water conditions, loading conditions, structural behavior, constitutive relation discipline, and dimensions. By extension also, the soil constitutive applications were reviewed on the bases of: single discipline dealing with soil mechanical properties constitutive modeling which included slope stability problems, bearing capacity, settlement of foundations, earth pressure problems, soil dynamics, soil structure interaction, thermal and hydrological conditions; bi-discipline (coupled problems) which solve problems related to thermomechanical (freeze/thaw conditions), smoothed particle hydrodynamics (SPH) and hydromechanical (consolidation, collapse and liquefaction) conditions in soils and rocks and multi-discipline constitutive models which solve complex problems related to thermo-hydromechanical (THM) conditions in soils and rocks. This work has shown that smoothed particle hydrodynamics (SPH) and hydromechanical (HM) models, which belong to bi-discipline or coupled conditions are better suited for geotechnical applications, generally, while thermo-hydromechanical (THM) models, which belong to multi-discipline are better suited to solving freeze/thaw and thermal piles problems and these are proven with high performance and flexibility.

Behavior of strip footing rested on undrained clay using consistency limits-based constitutive law.

The behavior of undrained clay was extensively studied by many earlier researchers. A lot of constitutive models were developed to describe the behavior of undrained clay based on its mechanical properties. The aim of this research is to present an innovative constitutive model for undrained clay based on its consistency limits and water content. The main concept of this model is to estimate the mechanical properties of clay using earlier correlations with consistency limits, then implement the estimated mechanical properties in a hyperbolic model and calibrate the hyperbolic parameters to match the failure criteria of the undrained clay. To verify the validity of the developed constitutive model, it was applied on a standard problem which is a strip footing rested on undrained clay layer, the results confirmed the ability of the model to simulate the nonlinear behavior of undrained clay up to ultimate condition. The main advantage of this constitutive model is the ability to capture the reduction of mechanical properties of clay with the increase in its water content, which makes it ideal to study the impact of seepage on shallow foundation.