Search

Abstract The California bearing ratio (CBR) of a granular materials are influence by the soil particle distribution indices such as D10, D30, D50, and D60 and also the compaction properties such as the maximum dry density (MDD) and the optimum moisture content (OMC). For this reason, the particle packing and compactibility of the soil play a big role in the design and construction of subbases and landfills. In this research paper, experimental data entries have been collected reflecting the CBR behavior of granular soil used to construct landfill and subbase. The database was utilized in the ratio of 78–22% to predict the CBR behavior considering the artificial neural network (ANN), the evolutionary polynomial regression (EPR), the genetic programming (GP), Extreme Gradient Boosting (XGBoost), Random Forest (RF) and the response surface methodology (RSM) intelligent learning and symbolic abilities. The relative importance values for each input parameter were carried out, which indicated that the (CBR) value depends mainly on the average particle size (D30, 50 & 60). They showed a combined influence index of 66% of the considered parameters in the model exercise. This further shows the importance and structural influence of the particles within the D50 and D60 range in a granular material consistency in the design and construction purposes. Performance indices were also used to study the ability of the models. The ANN model showed the best performance with accuracy of 88%, then GP, EPR and RF with almost the same accuracies of 85% and lastly the XGBoost with accuracy of 81%. Also, the RSM produced an R2 of 0.9464 with a p-value of less than 0.0001. These values show that the ANN produced the decisive model with the superior performance indices in the forecast of CBR of granular material used as subbase and waste compacted earth liner material. The results further show that optimal performance of the CBR depended on D50 and D60 for the design of subgrade, subbase, and liner purposes and also during the performance monitoring phase of the constructed flexible pavement foundations and compacted earth liners.

Abstract Particle size is considered one of the significant characteristics used in geotechnical practices. Traditionally, sieve analysis is utilized for coarse-grained soil. However, this method could be time consuming and take much effort, especially for large scale infrastructure projects. This paper presents an efficient method for estimating gravel particle characterization utilizing image processing and artificial neural network technique (IPNN). The proposed algorithm is performed by utilizing particle boundary delineation and shape feature extraction to train a neural network model for estimating gravel size distribution curve. It is found that excellent agreement exists between the results obtained from conventional sieve analysis and neural analysis for gravel soil particles with maximum difference in passing percentages up to only 3.70%. The proposed technique shows satisfactory results for crushed stone samples with maximum difference in passing percentages about 10.90% mainly in large diameter particles. The presented technique (IPNN) could offer a promising alternative technique for material quality control process especially in large scale projects.

Abstract Solar energy is the most promising source for generating residential, commercial, and industrial electricity. However, solar panels should be eco-friendly to increase sustainability during manufacturing and recycling. This study investigates the potential of using natural fibre composites as eco-friendly alternatives to conventional polyethylene terephthalate (PET) back sheets in solar panels. Furthermore, it examines the performance of sisal fibres coated with zeolite-polyester resin. The chemical composition, structural integrity, and crystalline properties of the composites were evaluated through extensive microstructural analysis. The results from the experimental analysis revealed significant improvements in voltage (8%) and current (6%) for the coated sisal fibre panels compared to conventional panels. Power output increased by 12%, and overall efficiency improved from 9.75 to 10.8%. Solar panels with sisal fibre sheets exhibit adequate tensile strength and impact resistance and reduce operating temperature by 2–3 °C, ensuring stable operation and minimizing heat loss. Statistical analysis confirmed the reliability and significance of these results. The life cycle analysis demonstrated a 60% reduction in CO2 emissions and a 50% decrease in energy consumption during the production, utilization and disposal of sisal fibre sheets. These findings underscore the viability of natural fibre composites in enhancing the performance and sustainability of solar panels.

Abstract Steel construction is increasingly using thin-walled profiles to achieve lighter, more cost-effective structures. However, analyzing the behavior of these elements becomes very complex due to the combined effects of local buckling in the thin walls and overall global buckling of the entire column. These factors make traditional analytical methods difficult to apply. Hence, in this research work, the strength of bi-axially loaded track and channel cold formed composite column has been estimated by applying three AI-based symbolic regression techniques namely (GP), (EPR) and (GMDH-NN). These techniques were selected because their output models are closed form equations that could be manually used. The methodology began with collecting a 90 records database from previous researches and conducting statistical, correlation and sensitivity analysis, and then the database was used to train and validate the three models. All the models used local and global slenderness ratios (λ, λc, λt) and relative eccentricities (ex/D, ey/B) as inputs and (F/Fy) as output. The performances of the developed models were compared with the predicted capacities from two design codes (AISI and EC3). The results showed that both design codes have prediction error of 33% while the three developed models showed better performance with error percent of 6%, and the (EPR) model is the simplest one. Also, both correlation and sensitivity analysis showed that the global slenderness ratio (λ) has the main influence on the strength, then the relative eccentricities (ex/D, ey/B) and finally the local slenderness ratios (λc, λt).

Abstract In this work, intelligent numerical models for the prediction of debris flow susceptibility using slope stability failure factor of safety (FOS) machine learning predictions have been developed. These machine learning techniques were trained using novel metaheuristic methods. The application of these training mechanisms was necessitated by the need to enhance the robustness and performance of the three main machine learning methods. It was necessary to develop intelligent models for the prediction of the FOS of debris flow down a slope with measured geometry due to the sophisticated equipment required for regular field studies on slopes prone to debris flow and the associated high project budgets and contingencies. With the development of smart models, the design and monitoring of the behavior of the slopes can be achieved at a reduced cost and time. Furthermore, multiple performance evaluation indices were utilized to ensure the model’s accuracy was maintained. The adaptive neuro-fuzzy inference system, combined with the particle swarm optimization algorithm, outperformed other techniques. It achieved an FOS of debris flow down a slope performance of over 85%, consistently surpassing other methods.

Abstract It has been imperative to study and stabilize cohesive soils for use in the construction of pavement subgrade and compacted landfill liners considering their unconfined compressive strength (UCS). As long as natural cohesive soil falls below 200 kN/m2 in strength, there is a structural necessity to improve its mechanical property to be suitable for the intended structural purposes. Subgrades and landfills are important environmental geotechnics structures needing the attention of engineering services due to their role in protecting the environment from associated hazards. In this research project, a comparative study and suitability assessment of the best analysis has been conducted on the behavior of the unconfined compressive strength (UCS) of cohesive soil reconstituted with cement and lime and mechanically stabilized at optimal compaction using multiple ensemble-based machine learning classification and symbolic regression techniques. The ensemble-based ML classification techniques are the gradient boosting (GB), CN2, naïve bayes (NB), support vector machine (SVM), stochastic gradient descent (SGD), k-nearest neighbor (K-NN), decision tree (Tree) and random forest (RF) and the artificial neural network (ANN) and response surface methodology (RSM) to estimate the (UCS, MPa) of cohesive soil stabilized with cement and lime. The considered inputs were cement (C), lime (Li), liquid limit (LL), plasticity index (PI), optimum moisture content (OMC), and maximum dry density (MDD). A total of 190 mix entries were collected from experimental exercises and partitioned into 74–26% train-test dataset. At the end of the model exercises, it was found that both GB and K-NN models showed the same excellent accuracy of 95%, while CN2, SVM, and Tree models shared the same level of accuracy of about 90%. RF and SGD models showed fair accuracy level of about 65–80% and finally (NB) badly producing an unacceptable low accuracy of 13%. The ANN and the RSM also showed closely matched accuracy to the SVM and the Tree. Both of correlation matrix and sensitivity analysis indicated that UCS is greatly affected by MDD, then the consistency limits and cement content, and lime content comes in the third place while the impact of (OMC) is almost neglected. This outcome can be applied in the field to obtain optimal compacted for a lime reconstituted soil considering the almost negligible impact of compactive moisture.

The structural design standards, particularly in concrete technology, heavily rely on the mechanical attributes of concrete. Utilizing dependable predictive models for these properties can minimize the need for extensive laboratory testing, evaluations, and experiments to acquire essential design data, thereby conserving time and resources. Metakaolin (MK) is frequently incorporated as an alternative to Portland cement in the production of sustainable concrete, owing to its technical advantages and positive environmental impact, aligning with the United Nations Sustainable Development Goals (UNSDGs) aimed at achieving net-zero objectives. However, this research presents a comparative study between eight (8) ML classification techniques namely, gradient boosting (GB), CN2, naïve bayes (NB), support vector machine (SVM), stochastic gradient descent (SGD), k-nearest neighbor (KNN), Tree and random forest (RF) to estimate the impact of adding metakaolin to concrete on its flexural strength considering mixture components contents and concrete age. The collected data entries for the prediction of the flexural strength (Ft) containing the following concrete components; contentof cement (C), content of metakaolin (MK), content of water (W), content of fine aggregates (FAg), content of coarse aggregates (CAg), content of super-plasticizer (P), and the concrete curing age at testing (Age) were partitioned into 80% and 20% for training and validation sets respectively. At the end of the model protocol, it was found that the GB, SVM, and KNN models which produced an average MSE value of zero (0) showed their decisive ability to predict the flexural strength of the metakaolin (MK) mixed concrete (Ft). This outcome agrees with the previous reports in the literatures; however the work of Shah et al. happens to be the closest in terms of concrete components used in the production of the mixes and the application of machine learning techniques. It was found that the present research work’s models outperformed those presented by Shah et al. Hence the decisive models reported in this research paper show potentials to be applied in the design and production of MK concrete with optimal flexural strength.

The compressive strength behavior of high-strength geopolymer concrete (HSGPC) has been studied in this research work with varying alkali concentration using the novel machine learning techniques. The alkali concentration in the activation solution plays a significant role in the geopolymerization process and affects the resulting compressive strength. In this research work, the range between 4 M and 16 M for alkali molarity (M), 18 kg/m3 and 160 kg/m3 for NaOH and 41 kg/m3 and 229 kg/m3 for NaSi was collected from literature and used in the various design mixes of this exercise. This was necessary because higher alkali concentrations promote a more efficient dissolution and activation of the aluminosilicate compounds, leading to increased geopolymerization and the formation of more calcium silicate hydrate (C-S-H) gel. The increased C-S-H gel content contributes to improved strength development. However, there is an optimal alkali concentration range for the sustainable production of geopolymer concrete, and exceeding this range can have a negative impact on compressive strength and ecofriendly handling of concrete. A total of fifty-three records were collected from literature and deployed in modeling the compressive strength (Fc) considering various curing regimes. Three symbolic machine learning techniques such as genetic programming (GP), evolutionary polynomial regression (EPR), and the artificial neural network (ANN) are used in this research model. The relative importance values for each input parameter were also evaluated, which indicated that all factors have significant impacts on (Fc), but Age (i.e., curing regime) has the most influence compared to FA, NaOH, and CAg then the other inputs. From the model relations between the calculated and predicted values, it can be shown that the decisive model, ANN produced line of parametric equation of y = 0.995x, and produced performance indices; MAE of 2.13 MPa, RMSE of 2.86 MPa and R-squared of 0.981, which makes the ANN the most reliable model in agreement with previous applications of the technique. These are against the poor performance of the EPR and GP, which produced R-squared less than 0.8 with higher error rates. The Taylor chart and the variance distribution, which further compares the accuracy and variances of the developed models support the outcomes. Generally, alkali molarity has shown its potential in the production of HSGPC due to its role in the reactivity phases of the concrete formulation; hydration, activation, pozzolanic, and geopolymerization reactions producing the gel needed for the strength gain in HSGPC.

Industrial wastes have found great use in the built environment due to the role they play in the sustainable infrastructure development especially in green concrete production. In this research investigation, the impact of wastes from the industry on the compressive strength of concrete incorporating fly ash (FA) and silica fume (SF) as additional components alongside traditional concrete mixes has been studied through the application of machine learning (ML). A green concrete database comprising 330 concrete mix data points has been collected and modelled to estimate the unconfined compressive strength behaviour. Considering the concerning environmental ramifications associated with concrete production and its utilization in construction activities, there is a pressing need to perform predictive model exercise. Furthermore, given the prevalent reliance of concrete production professionals on laboratory experiments, it is imperative to propose smart equations aimed at diminishing this dependency. These equations should be applicable for use in the design, construction, and performance assessment of concrete infrastructure, thereby reflecting the multi-objective nature of this research endeavour. It has been proposed by previous research works that the addition of FA and SF in concrete has a reduction impact on the environmental influence indicators due to reduced cement use. The artificial neural network (ANN) and the M5P models were applied in this exercise to predict the compressive strength of FA- and SF-mixed concrete also considering the impact of water reducing agent in the concrete. A sensitivity analysis was also conducted to determine the impact of the concrete components on the strength of the concrete. At the end, closed-form equations were proposed by the ANN and M5P with performance indices which outperformed previous models conducted on the same database size. The result of the sensitivity analysis showed that FA is most impactful of all the studied components thereby emphasizing the importance of adding industrial wastes in concrete production for improved mechanical properties and reduced carbon footprint in the concrete construction activities. Also, the M5P and ANN models with R2 of 0.99 showed a potential for use as decisive models to predict the compressive strength of FA- and SF-mixed concrete.

The production of geopolymer concrete (GPC) with the addition of industrial wastes as the formulation base is of interest to sustainable built environment. However, repeated experimental trials costs a huge budget, hence the prediction and validation of the strength behavior of the GPC mixed with some selected industrial wastes. Data gathering and analysis of a total 249 globally representative datasets of a high-strength geopolymer concrete (HSGPC) collected from experimental mix entries has been used in this research work. These mixes comprised of industrial wastes; fly ash (FA) and metallurgical slag (MS) and mix entry parameters like rest period (RP), curing temperature (CT), alkali ratio (AR), which stands for NaOH/Na2SiO3 ratio, superplasticizer (SP), extra water added (EWA), which was needed to complete hydration reaction, alkali molarity (M), alkali activator/binder ratio (A/B), coarse aggregate (CAgg), and fine aggregate (FAgg). These parameters were deployed as the inputs to the modeling of the compressive strength (CS). The range of CS considered in this global database was between 18 MPa and 89.6 MPa. The FA was applied between 254.54 kg/m3 and 515 kg/m3 while the MS was applied between 0% and 100% by weight of the FA to produce the tested HSGPC mixes. The Gaussian support vector regression hybridized with the extreme gradient boosting algorithms (GSVR-XGB) has been deployed to execute a prediction model for the studied concrete CS. The basic linear fittings to determine agreement between the parameters and the Pearson correlation between the studied parameters of the geopolymer concrete were presented. It can be observed that the CS showed very poor correlations with the values of the input parameters and required an improvement of the internal consistency of the dataset to achieve a good model performance. This necessitated the deployment of the super-hybrid interface between the Gaussian support vector regression (GSVR) and the extreme gradient boosting (XGB) algorithms. The frequency histogram and the Gaussian support vector machine architecture for the output (CS) are presented and these show serious outliers in the support vector machine which were tuned by using the boosting algorithms combined in the computation interface to enhance the GSVR hyperplane. This eventually produced a super-performance and execution speed remarkable for its use in the forecasting of the CS of the high-strength geopolymer concrete (HSGPC) for sustainable concrete design, production and placement during construction activities. Furthermore, the measure of the performance evaluation in comparison between measured and predicted values are presented on the basis of the MAE, MSE, RMSE, MAPE and R2 for the MLR and the SVR. It can be observed that the MAE produced 16.731 MPa, MSE produced 173.398 MPa, RMSE produced 0.452 MPa, MAPE produced 0.486 MPa and with R2 of 0.720 for the MLR and the MAE produced 6.855 MPa, MSE produced 109.582 MPa, RMSE produced 10.468 MPa, MAPE produced 0.190 MPa and with R2 of 0.994. These results show the super-performance display of the hybrid algorithms of the Gaussian support vector regression (GSVR) and the extreme gradient boosting (XGBoosting), which produced a superior and decisive model with excellent output compared to the MLR. Also, the execution time reduced from a 24-hour runtime to 1-hour runtime, which reduced the time and energy utilized in the model execution. Also, the GSVR-XGB produced minimal errors. The significant parameters that have a substantial effect on the outcome can be identified as AR and SP for the MLR and the GSVR-XGB, respectively and this presents insights into the behavior of geopolymer concrete.

Abstract 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.

To address the growing concerns about the environmental impact and construction costs, there has been an increasing interest in the use of recycled aggregates in concrete applications. Among the mechanical properties of concrete, compressive strength (fc) is particularly significant. This study explored the estimation of the compressive strength of recycled aggregate concrete using various machine-learning techniques. In this study, ‘Genetic Programming’ (GP), ‘artificial neural networks’ (ANN), and ‘Evolutionary Polynomial Regression’ (EPR) were employed to predict the 28-day compressive strength of recycled aggregate concrete. The considered predictive inputs encompass a range of factors, including cement, fine aggregate, recycled fine aggregate, coarse aggregate, recycled course aggregate, water, water-cement ratio, and superplasticizers, which produced 476 data entries. Among the models developed, the hybrid ANN-based model demonstrated superior performance compared with the other models. A rigorous assessment of the model performance was conducted through diverse statistical calculations, such as spearman correlation and internal consistency, relative importance of input parameters, sum of squared error (SSE) and the coefficient of determination designated as R-squared (R2). To reinforce the evaluation, a Taylor diagram and marginal histogram were employed as assessment parameters. Considering the statistical error analysis, Taylor diagram, and marginal histogram, the ANN-hybrid model was capable of accurately estimating the compressive strength (fc) of recycled aggregate concrete. The adopted machine learning models have the potential to conserve material resources and reduce the technical labor involved in determining the compressive strength of recycled aggregates in concrete applications.

AbstractFilling 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 Orimet apparatus using the coupled ANSYS-SPH interface. Seven (7) model cases: Case 1: 0% coarse particles and 100% fine particles, Case 2: 60% coarse particles and 40% fine particles, Case 3: 55% coarse particles and 45% fine particles, Case 4: 50% coarse particles and 50% fine particles, Case 5: 45% coarse particles and 55% fine particles, Case 6: 40% coarse particles and 60% fine particles and Case 7: 100% coarse particles and 0% fine particles were studied and optimized. 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; 0 ≤ t ≤ 6, and the Orimet volume is 10 l. The minimum boundary flow time, which represents the time (0 ≤ t ≤ 6) 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 first case; 0%C mixed with 100%F flowed out completely in 6 s, second case; 40%C mixed with 60%F completely flowed out in 5 s, third case; 45%C mixed with 55%F completely flowed out in 9 s, fourth case; 50%C mixed with 50%F completely flowed out in 12 s, fifth case; 55%C mixed with 45%F completely flowed out in 11 s, sixth case; 60%C mixed with 40%F completely flowed out in 12 s, and lastly, the 7th case; 100%C mixed with 0%F completely flowed out in 20 s. The 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:5 s), 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 (40%C + 60%F:5 s) was further simulated using the coupled interface of the ANSYS-SPH platform operating with the CFX command at air temperature of 25 °C, which incorporated the studied density of 2400 kg/m3, plastic viscosity boundary, yield stress, and aggregate sampling. The model simulation operated on total number of nodes = 143,083, total number of elements = 753,292, total number of tetrahedrons = 753,292, and total number of faces = 68,488, and produced Dynamic Viscosity = 1.831E-05 kg m−1 s−1, Thermal Conductivity = 2.61E-02 W m−1 K−1, Absorption Coefficient = 0.01 m−1, Thermal Conductivity = 2.61E-02 W m−1 K−1, Refractive Index = 1.0 m m−1, Molar Mass = 1 kg kmol−1, Specific Heat Capacity = 8.80E + 02 J kg−1 K−1, Normal Speed = 165 mm s−1, Pressure Profile Blend = 0.05, and Maximum Partition Smoothing Sweeps = 100. Also, the Global Length = 1.9144E-01, Minimum Extent = 1.1800E-01, Maximum Extent = 6.5976E-01, Density = 1.1850E + 00, Velocity = 1.6500E-01, Advection Time = 1.1602E + 00, and Reynolds Number = 2.0443E + 03. The simulation also produced wall forces and moments on the wall of the Orimet for the optimized mix containing 40%C + 60%F:5 s flow mix as follows; pressure force on wall; −3.0996E-08, −2.0863E-07, and −3.5048E-04 for x-component, y-component, and z-component, respectively, viscous force on wall; −5.5332E-10, −9.2298E-10, and −2.7250E-05 for the x-, y-, and z-components, respectively, pressure moment on wall; −5.0051E-05, 3.1362E-06, and 2.2774E-09 for the x-, y-, and z-components, respectively and viscous moment on wall; −3.8925E-06, 2.4396E-07, and −7.2693E-11 for the x-, y-, and z-components, respectively. Also, the maximum residuals were located at node 110413 for the pressure, node 76766 for the K-TurbKE, and node 110724 for the E-Diss.K. Ideally, the mix, 40%C + 60%F:5 s has been proposed as the mix with the most efficient flow to achieve the filling ability for sustainable structural concrete construction.

The development of concrete with excellent water and frost resistance providing high level of sound and thermal insulation has triggered the formulation of foamed concrete. However, multiple laboratory studies are required to produce reasonable data to design the relevant codes and mathematics with which design of mixes is made easier at low cost. In this research paper, the artificial intelligence (AI)-based symbolic regression technique estimation of the compressive strength of foamed concrete has been reported. Foamed concrete has been a subject of serious research in sustainable built-environment due to its lightweight and structural functionality. In this research work, data gathering method was applied to gather a globally representative data base comprising concrete density to water density (concrete density g/cm3) (γ/γw), water-cement ratio (W/C), and sand-cement ratio (S/C) as input variable and the compressive strength (Fc) as the study output. The dimensionless factors have been derived to eliminate data handling complexities and improve model performances. The 230 data entries from foamed concrete mixes were partitioned into 75% and 25% for training and validation data, respectively. At the end of the model execution, it was found that the response surface methodology (RSM) produced a symbolic closed-form equation like the genetic programming (GP), evolutionary polynomial regression (EPR), and the group method of data-handling-neural network (GMDH-NN). Even though the RSM closed with a minimum error, the GP, EPR and GMDH-NN were faster in runtime. The overall outcomes show that the GP outclassed the EPR, RSM and the GMDH-NN, though with minor margin. Meanwhile the EPR produced the highest outliers from the ±25% test of accuracy envelope. Overall, the present models outperformed those reported in the literature due the parameter reduction through dimensionless factors derivation and provided a decisive model to predict the Fc of foamed concrete.

Abstract 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.

Abstract 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.

Abstract 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 $$\le {\tau }_{0}\le 200$$ ≤ τ 0 ≤ 200 , 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.

Abstract 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.

The mechanical characteristics of concrete are crucial factors in structural design standards especially in concrete technology. Employing reliable prediction models for concrete’s mechanical properties can reduce the number of necessary laboratory trials, checks and experiments to obtain valuable representative design data, thus saving both time and resources. Metakaolin (MK) is commonly utilized as a supplementary replacement for Portland cement in sustainable concrete production due to its technical and environmental benefits towards net-zero goals of the United Nations Sustainable Development Goals (UNSDGs). In this research work, 204 data entries from concrete mixes produced with the addition of metakaolin (MK) were collected and analyzed using eight (8) ensemble machine learning tools and one (1) symbolic regression technique. The application of multiple machine learning protocols such as the ensemble group and the symbolic regression techniques have not been presented in any previous research work on the modeling of splitting tensile strength of MK mixed concrete. The data was partitioned and applied according to standard conditions. Lastly, some selected performance evaluation indices were used to test the models’ accuracy in predicting the splitting strength (Fsp) of the studied MK-mixed concrete. At the end, results show that the k-nearest neighbor (KNN) outperformed the other techniques in the ensemble group with the following indices; SSE of 4% and 1%, MAE of 0.1 and 0.2 MPa, MSE of 0, RMSE of 0.1 and 0.2 MPa, Error of 0.04% and 0.04%, Accuracy of 0.96 and 0.96 and R2 of 0.98 and 0.98 for the training and validation models, respectively. This is followed closely by the support vector machine (SVM) with the following indices; SSE of 7% and 3%, MAE of 0.2 and 0.2 MPa, MSE of 0.0 and 0.1 MPa, RMSE of 0.2 and 0.3 MPa, Error of 0.05% and 0.06%, Accuracy of 0.95 and 0.94, and R2 of 0.96 and 0.95, for the training and validation models, respectively. The third model in the superiority rank is the CN2 with the following performance indices; SSE of 15% and 4%, MAE of 0.2 and 0.2 MPa, MSE of 0.1 and 0.1 MPa, RMSE of 0.3 and 0.3 MPa, Error of 0.08% and 0.07%, Accuracy of 0.92 and 0.93 and R2 of 0.92 and 0.93, for the training and validation models, respectively. These models outperformed the models utilized on the MK-mixed concrete found in the literature, therefore are the better decisive modes for the prediction of the splitting strength (Fsp) of the studied MK-mixed concrete with 204 mix data entries. Conversely, the NB and SGD produced unacceptable model performances, however, this is true for the modeled database collected for the MK-mixed Fsp. The RSM model also produced superior performance with an accuracy of over 95% and adequate precision of more than 27. Overall, the KNN, SVM, CN2 and RSM have shown to possess the potential to predict the MK-mixed Fsp for structural concrete designs and production.