Evaluation of Strength of Clayey Soil by UCS Test with Addition of Rice Husk Ash and Lime

The present study investigates the benefits of using rice husk ash (RHA) with clayey soil as the subgrade material in flexible pavements with addition of small amount of lime. Four ratios of RHA of 5%, 10%, 15% and 20% mixed with the clayey soil by weight of soil sample. Further for getting the better performance, lime has been added in this study in the varying proportions from 1% to 3% by weight of soil. The compaction characteristics and unconfined compressive strength tests were conducted on these different mixed soils. The test results shows that the rice husk ash can be used advantageously with addition of clayey soil and lime as cost effective mix for construction of subgrade of the roadway pavement

Effect of saw dust ash (SDA) and recycled asphalt pavement (RAP) in the bituminous concrete

The present study deals with the effect of saw dust ash (SDA) as an alternative filler and recycled asphalt pavement (RAP) material in bituminous concrete (BC). Several physical and chemical experiments were performed to characterize the SDA and RAP material according to the standard specifications. To execute such study, virgin aggregate and RAP were blended as 100:0, 90:10, 80:20, 70:30, 60:40, 50:50, 40:60, 25 :75, 0 :100% and 8% SDA was used as filler to prepare the BC mix. In this study, 2% ordinary Portland cement (OPC-43) was utilized to prepare the control mix. However, to quantify the performance of RAP and 8% SDA in the BC mix, several Marshall properties, and the performances criterion against various distress (rutting, cracking, and moisture susceptibility) were conducted. The experimental results reveal that 60% RAP-8%SDA enriched BC mix shows superior rutting, cracking, and moisture resistance potency with the highest structural integrity of 82.10%. Since the dominant percent of calcium oxide mineral present in that filler reacts with the dipolar carboxylic acid which already exists in an aged binder and thus the adhesive property of the binder in the BC is improved significantly

Generalized Daily Reference Evapotranspiration Models Based on a Hybrid Optimization Algorithm Tuned Fuzzy Tree Approach

Reference evapotranspiration (ET0) is an important driver in managing scarce water resources and making decisions on real-time and future irrigation scheduling. Therefore, accurate prediction of ET0 is crucial in the water resources management discipline. In this study, the prediction of ET0 was performed by employing several optimization algorithms tuned Fuzzy Inference System (FIS) and Fuzzy Tree (FT) models, for the first time, whose generalization capability was tested using data from other stations. The FISs and FTs were developed through parameters tuning using Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Pattern Search (PS), and their combinations. The FT was developed by combining several FIS objects that received ranked meteorological variables. A total of 50 FIS and FT models were developed and the model ranking was performed utilizing Shannon's Entropy (SE). Evaluation outcomes revealed the superiority of the hybrid PSO-GA tuned Sugeno type 1 FT model (with R = 0.929, NRMSE = 0.169, accuracy = 0.999, NS = 0.856, and IOA = 0.985) over others. For evaluating the generalization capability of the best model, three different parts of datasets (all-inclusive, 1(st) half, and 2(nd) half) of the five test stations were evaluated. The proposed hybrid PSO-GA tuned Sugeno type 1 FT model performed similarly well, according to the findings, on the datasets of the test stations. The study concluded that the hybrid PSO-GA tuned Sugeno type 1 FT approach, which was composed of several standalone FIS objects, was suitable for predicting daily ET0 values

Replacing Lime with Rice Husk Ash to Reduce Carbon Footprint of Bituminous Mixtures

There have been several emphasized pathways toward a reduction in carbon footprint in the built environment such as recycling, technologies with lower energy consumption, and alternative materials. Among alternative materials, bio-based materials and nature inspired solutions have been well-received. This study examines the merits of using rice husk ash as a replacement for lime; lime has a high carbon footprint mainly associated with the decomposition of calcium carbonate to calcium oxide to form lime. Lime is commonly used in bituminous composites for roadway construction to mitigate their susceptibility to moisture damage. Replacing lime with a low-carbon alternative could allow a reduction in CO2 equivalent of bituminous composites. This paper studies the merits of using rice husk ash (RHA) as a substitute for conventional hydrated lime (HL) in bituminous composites. It should be noted that rice industries burn rice husks in a boiler as fuel, generating a substantial volume of RHA. The disposal of this ash has major environmental impacts associated with the contamination of air and water. Here, we study physical and chemical characteristics of both HL and RHA for use in bitumen mixtures. This was followed by examining the extent of dispersion of each filler in bitumen via optical microscopy to ensure their uniform dispersion. The properties of the mixtures were further studied using the Marshall mix design method. It was found that a 25.67% increase in Marshall stability and a 5.95% decrease in optimum binder content were achieved when HL was replaced by RHA at 4% filler concentration. In addition, mixtures containing RHA exhibited higher resistance to cracking and permanent deformation compared to mixtures containing HL. Additionally, 4% RHA in the mix showed stripping resistance similar to the conventional mix with HL. The mixture with 4% RHA had a lower carbon footprint with enhanced economic and environmental impacts compared to the conventional mix with HL. The study results provide insights pertaining to the merits of bio-based materials to reduce the carbon footprint of pavements

Daily Prediction and Multi-Step Forward Forecasting of Reference Evapotranspiration Using LSTM and Bi-LSTM Models

Precise forecasting of reference evapotranspiration (ET0) is one of the critical initial steps in determining crop water requirements, which contributes to the reliable management and long-term planning of the world’s scarce water sources. This study provides daily prediction and multi-step forward forecasting of ET0 utilizing a long short-term memory network (LSTM) and a bi-directional LSTM (Bi-LSTM) model. For daily predictions, the LSTM model’s accuracy was compared to that of other artificial intelligence-based models commonly used in ET0 forecasting, including support vector regression (SVR), M5 model tree (M5Tree), multivariate adaptive regression spline (MARS), probabilistic linear regression (PLR), adaptive neuro-fuzzy inference system (ANFIS), and Gaussian process regression (GPR). The LSTM model outperformed the other models in a comparison based on Shannon’s entropy-based decision theory, while the M5 tree and PLR models proved to be the lowest performers. Prior to performing a multi-step-ahead forecasting, ANFIS, sequence-to-sequence regression LSTM network (SSR-LSTM), LSTM, and Bi-LSTM approaches were used for one-step-ahead forecasting utilizing the past values of the ET0 time series. The results showed that the Bi-LSTM model outperformed other models and that the sequence of models in ascending order in terms of accuracies was Bi-LSTM > SSR-LSTM > ANFIS > LSTM. The Bi-LSTM model provided multi-step (5 day)-ahead ET0 forecasting in the next step. According to the results, the Bi-LSTM provided reasonably accurate and acceptable forecasting of multi-step-forward ET0 with relatively lower levels of forecasting errors. In the final step, the generalization capability of the proposed best models (LSTM for daily predictions and Bi-LSTM for multi-step-ahead forecasting) was evaluated on new unseen data obtained from a test station, Ishurdi. The model’s performance was assessed on three distinct datasets (the entire dataset and the first and the second halves of the entire dataset) derived from the test dataset between 1 January 2015 and 31 December 2020. The results indicated that the deep learning techniques (LSTM and Bi-LSTM) achieved equally good performances as the training station dataset, for which the models were developed. The research outcomes demonstrated the ability of the developed deep learning models to generalize the prediction capabilities outside the training station

Daily Prediction and Multi-Step Forward Forecasting of Reference Evapotranspiration Using LSTM and Bi-LSTM Models

Precise forecasting of reference evapotranspiration (ET0) is one of the critical initial steps in determining crop water requirements, which contributes to the reliable management and long-term planning of the world’s scarce water sources. This study provides daily prediction and multi-step forward forecasting of ET0 utilizing a long short-term memory network (LSTM) and a bi-directional LSTM (Bi-LSTM) model. For daily predictions, the LSTM model’s accuracy was compared to that of other artificial intelligence-based models commonly used in ET0 forecasting, including support vector regression (SVR), M5 model tree (M5Tree), multivariate adaptive regression spline (MARS), probabilistic linear regression (PLR), adaptive neuro-fuzzy inference system (ANFIS), and Gaussian process regression (GPR). The LSTM model outperformed the other models in a comparison based on Shannon’s entropy-based decision theory, while the M5 tree and PLR models proved to be the lowest performers. Prior to performing a multi-step-ahead forecasting, ANFIS, sequence-to-sequence regression LSTM network (SSR-LSTM), LSTM, and Bi-LSTM approaches were used for one-step-ahead forecasting utilizing the past values of the ET0 time series. The results showed that the Bi-LSTM model outperformed other models and that the sequence of models in ascending order in terms of accuracies was Bi-LSTM > SSR-LSTM > ANFIS > LSTM. The Bi-LSTM model provided multi-step (5 day)-ahead ET0 forecasting in the next step. According to the results, the Bi-LSTM provided reasonably accurate and acceptable forecasting of multi-step-forward ET0 with relatively lower levels of forecasting errors. In the final step, the generalization capability of the proposed best models (LSTM for daily predictions and Bi-LSTM for multi-step-ahead forecasting) was evaluated on new unseen data obtained from a test station, Ishurdi. The model’s performance was assessed on three distinct datasets (the entire dataset and the first and the second halves of the entire dataset) derived from the test dataset between 1 January 2015 and 31 December 2020. The results indicated that the deep learning techniques (LSTM and Bi-LSTM) achieved equally good performances as the training station dataset, for which the models were developed. The research outcomes demonstrated the ability of the developed deep learning models to generalize the prediction capabilities outside the training station