Optimization of Green Concrete Containing Fly Ash and Rice Husk Ash Based on Hydro-Mechanical Properties and Life Cycle Assessment Considerations

The development of sustainable concrete in achieving the developmental goals of the United Nations in terms of sustainable infrastructure and innovative technology forms part of the focus of this research paper. In order to move towards sustainability, the utilization of the by-products of agro-industrial operations, which are fly ash (FA) and rice husk ash (RHA), in the production of concrete has been studied. Considering the environmental impact of concrete constituents, multiple mechanical and hydraulic properties of fly ash (FA) and rice husk ash (RHA) concrete have been proposed using intelligent techniques; artificial neural network (ANN) and evolutionary polynomial regressions (EPR). Also, an intelligent mix design tool/chart for this case under study is proposed. Multiple data points of concrete materials, which were further reduced to ratios as follows; cement to binder ratio (C/B), aggregate to binder ratio (Ag/B), and plasticizer to binder ratio (PL/B) were used in this exercise. At the end of the protocol, it is observed that the constituents’ ratios are dependent on the behavior of the whole, which can be solved by using the proposed model equations and mix design charts. The models performed optimally, as none showed any performance below 80%. However, ANN, which predicted Fc03, Fc07, Fc28, Fc60, Fc90, Ft28, Ff28 & Fb28, S, Ec28 & K28, and P with an accuracy of greater than 95% each with average error of less than 9.4% each, is considered the decisive technique in predicting all the studied concrete properties, including the life cycle assessment potential of the concrete materials. Doi: 10.28991/CEJ-2022-08-12-018 Full Text: PD

Performance Based Review and Fine-Tuning of TRM-Concrete Bond Strength Existing Models

Textile reinforced mortars (TRMs) are new composite materials which were considered as a proper alternative for fiber reinforced polymers (FRPs) to strengthen various structural elements. In comparison to FRPs, the TRMs have more fire resistance, more environmental consistency and are safer the structural elements because of their better bond to substrate and various failure modes. There are a lot of existing models to calculate the bond strength between TRMs and concrete substrate. But, most of them originated from the FRP-concrete bond models and are not accurate enough to estimate the TRM-concrete bond strength. In this paper, new TRM-concrete bond models were calibrated to predict the bond strength between various TRM composites and the concrete substrate. To achieve this goal, a database including 221 experimental direct shear tests were compiled and a simple existing model was selected to be calibrated via soft computing techniques. It was found that the presented novel models could be accurately utilized to anticipate the TRM-concrete bond strength with various types of fibers and different geometrical features with R value of 0.6909 and NMAE error value of 12.62%

Behaviour Investigation of Sma-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques

Most isolators have numerous displacements due to their low stiffness and damping properties. Accordingly, the supplementary damping systems have vital roles in damping enhancement and lower the isolation system displacement. Nevertheless, in many cases, even by utilising additional dampers in isolation systems, the occurrence of residual displacement is inevitable. To address this issue, in this study, a new smart type of bar hysteretic dampers equipped with shape memory alloy (SMA) bars with recentring features, as the supplementary damper, is introduced and investigated. In this regard, 630 numerical models of SMA-equipped bar hysteretic dampers (SMA-BHDs) were constructed based on experimental samples with different lengths, numbers, and cross sections of SMA bars. Furthermore, by utilising hysteresis curves and the corresponding ideal bilinear curves, the role of geometrical and mechanical parameters in the cyclic behaviour of SMA-BHDs was examined. Due to the deficiency of existing analytical models, proposed previously for steel bar hysteretic dampers (SBHDs), to estimate the first yield point displacement and post-yield stiffness ratio in SMA-BHDs accurately, new models were developed by the artificial neural network (ANN) and group method of data handling (GMDH) approaches. The results showed that, although the ANN models outperform GMDH ones, both ANN-and GMDH-based models can accurately estimate the linear and nonlinear behaviour of SMA-BHDs in pre-and post-yield parts with low errors and high accuracy and consistency

Air Quality Prediction - A Study Using Neural Network Based Approach

India is the 7th largest country by area and 2nd most populated country in the world. The reports prepared by IQAir revels that India is 3rd most polluted country after Bangladesh and Pakistan, on the basis of fine particulates (PM2.5) concentration for the year 2020. In this article, the quality of air in six Indian cities is predicted using data-driven Artificial Neural Network. The data was taken from the 'Kaggle' online source. For six Indian cities, 6139 data sets for ten contaminants (PM2.5, PM10, NO, NO2, NH3, CO, SO2, O3, C6H6 and C7H8) were chosen. The datasets were collected throughout the last five years, from 2016 to 2020, and were used to develop the predictive model. Two machine learning model are proposing in this study namely Artificial Intelligence (AI) and Gaussian Process Regression (GPR) The R-value of ANN and GPR models are 0.9611 and 0.9843 sequentially. The other performance indices such as RMSE, MAPE, MAE of the GPR model are 21.4079, 7.8945% and 13.5884, respectively. The developed model is quite useful to update citizens about the predicted air quality of the urban spaces and protect them from getting affected by the poor ambient air quality. It can also be used to find the proper abatement strategies as well as operational measures

Optimal Compressive Strength of RHA Ultra-High-Performance Lightweight Concrete (UHPLC) and Its Environmental Performance Using Life Cycle Assessment

Frequent laboratory needs during the production of concrete for infrastructure development purposes are a factor of serious concern for sustainable development. In order to overcome this trend, an intelligent forecast of the concrete properties based on multiple data points collected from various concrete mixes produced and cured under different conditions is adopted. It is equally important to consider the impact of the concrete components in this attempt to take care of the environmental risks involved in this production. In this work, 192 mixes of an ultra-high-performance lightweight concrete (UHPLC) were collected from literature representing different mixes cured under different periods and laboratory conditions. These mix proportions constitute measured variables, which are curing age (A), cement content (C), fine aggregate (FAg), plasticizer (PL), and rice husk ash (RHA). The studied concrete property was the unconfined compressive strength (Fc). This exercise was necessary to reduce multiple dependence on laboratory examinations by proposing concrete strength equations. First, the life cycle assessment evaluation was conducted on the rice husk ash-based UHPLC, and the results from the 192 mixes show that the C-783 mix (87 kg/m3 RHA) has the highest score on the environmental performance evaluation, while C-300 (75 kg/m3 RHA) with life cycle indices of 289.85 kg CO2eq. Global warming potential (GWP), 0.66 kg SO2eq. Terrestrial acidification and 5.77 m3 water consumption was selected to be the optimal choice due to its good profile in the LCA and the Fc associated with the mix. Second, intelligent predictions were conducted by using six algorithms (ANN-BP), (ANN-GRG), (ANN-GA), (GP), (EPR), and (GMDH-Combi). The results show that (ANN-BP) with performance indices of R; 0.989, R2; 0.979, mean square error (MSE); 2252.55, root mean squared error (RMSE); 42.46 MPa and mean absolute percentage error (MAPE); 4.95% outclassed the other five techniques and is selected as the decisive model. However, it also compared well and outclassed previous models, which had used gene expression programming (GEP) and random forest regression (RFR) and achieved R2of 0.96 and 0.91, respectively. Doi: 10.28991/CEJ-2022-08-11-03 Full Text: PD

Leveraging Deep Learning and SNA approaches for Smart City Policing in the Developing World

Is it possible to identify crime suspects by their mobile phone call records? Can the spatial-temporal movements of individuals linked to convicted criminals help to identify those who facilitate crime? Might we leverage the usage of mobile phones, such as incoming and outgoing call numbers, coordinates, call duration and frequency of calls, in a specific time window on either side of a crime to provide a focus for the location and period under investigation? Might the call data records of convicted criminals' social networks serve to distinguish criminals from non-criminals? To address these questions, we used heterogeneous call data records dataset by tapping into the power of social network analysis and the advancements in graph convolutional networks. In collaboration with the Punjab Police and Punjab Information Technology Board, these techniques were useful in identifying convicted individuals. The approaches employed are useful in identifying crime suspects and facilitators to support smart policing in the fight against the country's increasing crime rates. Last but not least, the applied methods are highly desirable to complement high-cost video-based smart city surveillance platforms in developing countries

Effect of shelterwood logging on diversity of tree species in the Loveh Forest, Gorgan

In order to identify the effect of shelterwood logging on the species diversity in each stages performance, a study was conducted at the Loveh forest, east of Golestan province. Totally, 117 plots with 60×60m were set down with systematic cluster sampling method at the study area. Diameter at breast height (DBH) of trees and species were assessed in each plot. In this study three managed stands including 20 years practiced stands (shelterwood system), 40 years practiced stands (shelterwood system), and improvement stand as well as one unmanaged stand were compared based on Shannon-Wiener, Smith-Wilson and richness indices. The results of analysis of variances showed that difference among four stands were significant (

Damage identification in reinforced concrete beams using wavelet transform of modal excitation responses

This study focuses on identifying damage in reinforced concrete (RC) beams using timedomain modal testing and wavelet analysis. A numerical model of an RC beam was used to generate various damage scenarios with different severities and locations. Acceleration time histories were recorded for both damaged and undamaged structures. Two damage indices, DI_MW and DI_SW, derived from the wavelet analysis, were employed to determine the location and severity of the damage. The results showed that different wavelet families and specific mother wavelets had varying effectiveness in detecting damage. The Daubechies wavelet family (db2, db6, and db9) detected damage at the center and sides of the RC beams due to good time and frequency localization. The Biorthogonal wavelet family (bior2.8 and bior3.1) provided improved time–frequency resolution. The Symlets wavelet family (sym2 and sym7) offered a balanced trade-off between time and frequency localization. The Shannon wavelet family (shan1-0.5 and shan1-0.1) exhibited good time localization, while the Frequency B-Spline wavelet family (fbsp2-1-0.1) excelled in frequency localization. Certain combinations of mother wavelets, such as shan1-0.5 with the DI_SW index, were highly effective in detecting damage. The DI_SW index outperformed DI_MW across different numerical models. Selecting appropriate wavelet analysis techniques, particularly utilizing shan1-0.5 in the DI_SW, proved effective for detecting damage in RC beams

Estimating the Buckling Load of Steel Plates with Center Cut-Outs by ANN, GEP and EPR Techniques

Steel plates are used in the construction of various structures in civil engineering, aerospace, and shipbuilding. One of the main failure modes of plate members is buckling. Openings are provided in plates to accommodate various additional facilities and make the structure more serviceable. The present study examined the critical buckling load of rectangular steel plates with centrally placed circular openings and different support conditions. Various datasets were compiled from the literature and integrated into artificial intelligence techniques like Gene Expression Programming (GEP), Artificial Neural Network (ANN) and Evolutionary Polynomial Regression (EPR) to predict the critical buckling loads of the steel plates. The comparison of the developed models was conducted by determining various statistical parameters. The assessment revealed that the ANN model, with an R2 of 98.6% with an average error of 10.4%, outperformed the other two models showing its superiority in terms of better precision and less error. Thus, artificial intelligence techniques can be adopted as a successful technique for the prediction of the buckling load, and it is a sustainable method that can be used to solve practical problems encountered in the field of civil engineering, especially in steel structures