Vinay Gadi, Shivam Singh, Manish Singhariya, Ankit Garg, Sreedeep S. and Ravi K.
The purpose of this paper is to numerically investigate the combined effects of canopy (leaf area index [LAI]) and root properties (root distribution function [Rdf] and root area…
Abstract
Purpose
The purpose of this paper is to numerically investigate the combined effects of canopy (leaf area index [LAI]) and root properties (root distribution function [Rdf] and root area index [RAI]) on a suction induced in soil-root composite under three different scenarios.
Design/methodology/approach
Richards equation coupled with sink term was solved using a commercial finite element package “HYDRUS” to investigate suction induced in soil-root composite.
Findings
Scenario 1 unveiled that soil-root composite induces 1 to 20 per cent higher suction than bare soil under the absence of transpiration. From Scenario 2, value of suction at depth of maximum RAI in case of linearly decreasing Rdf was found to be higher than that of other Rdfs. However, depth of suction influence zone (SIZ) for uniform Rdf and non-linear Rdf was found to be 10 and 11 per cent higher than that of linearly decreasing Rdf. Depth of evaporation dominant zone (EDZ) for uniformly decreasing Rdf and non-linear Rdf was found to be 1.08 to 3 times higher than that of linearly decreasing Rdf. From Scenario 3, influence of LAI on depth of SIZ is minimal. Depth of EDZ was found to decrease with the increase in LAI. Based on simple calculation on infinite slope stability, influence of variation in root and shoot properties was found to be significant on its factor of safety.
Research limitations/implications
Numerical constitutive model has limitations that it does not consider aging of plant. This model is only applicable for a particular set of soil conditions. A long-term study is required in this field to further quantify parameters for improving calibration and modeling performance.
Practical implications
Following are the practical implication: consideration of vegetation properties into engineered design of green infrastructure (slopes in this case) and selection of vegetation with appropriate characteristics in design for enhancement of stability of green infrastructure.
Originality/value
Contents of this paper are original, and they have not been submitted to any other journal.
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Harsha Vardhan, Sanandam Bordoloi, Akhil Garg, Ankit Garg and Sreedeep S.
The purpose of this study is to measure the effects of density, moisture, fiber content on unconfined compressive strength (UCS) of soil by formulating the models based on…
Abstract
Purpose
The purpose of this study is to measure the effects of density, moisture, fiber content on unconfined compressive strength (UCS) of soil by formulating the models based on evolutionary approach and artificial neural networks (ANN).
Design/methodology/approach
The present work proposes evolutionary approach of multi-gene genetic programming (MGGP) to formulate the functional relationships between UCS of reinforced soil and four inputs (soil moisture, soil density, fiber content and unreinforced soil strength) of the silty sand. The hidden non-linear relationships between UCS of reinforced soil and the four inputs are determined by sensitivity and parametric analysis of the MGGP model.
Findings
The performance of MGGP is compared to those of ANN and the statistical analysis indicates that the MGGP model is the best and is able to generalize the UCS of reinforced soil satisfactorily beyond the given input range.
Research limitations/implications
The explicit MGGP model will be useful to provide optimum input values for design and analysis of various geotechnical infrastructures. In addition, utilization of Water hyacinth reinforced fiber reinforced soil will minimize negative impact of this species on environment and may generate rural employment.
Originality/value
This work is first of its kind in application and development of explicit holistic model for evaluating the compressive strength of heterogeneous soil blinded with fiber content. This includes the experimental and cross-validation for testing robustness of the model.
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R. Dhanalakshmi, Monica Benjamin, Arunkumar Sivaraman, Kiran Sood and S. S. Sreedeep
Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing…
Abstract
Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing intelligent devices used in our daily lives to examine various machine learning models that can be applied to make an appliance ‘intelligent’ and discuss the different pros and cons of the implementation.
Methodology: Most smart appliances need machine learning models to decrypt the meaning and functioning behind the sensor’s data to execute accurate predictions and come to appropriate conclusions.
Findings: The future holds endless possibilities for devices to be connected in different ways, and these devices will be in our homes, offices, industries and even vehicles that can connect each other. The massive number of connected devices could congest the network; hence there is necessary to incorporate intelligence on end devices using machine learning algorithms. The connected devices that allow automatic control appliance driven by the user’s preference would avail itself to use the Network to communicate with devices close to its proximity or use other channels to liaise with external utility systems. Data processing is facilitated through edge devices, and machine learning algorithms can be applied.
Significance: This chapter overviews smart appliances that use machine learning at the edge. It highlights the effects of using these appliances and how they raise the overall living standards when smarter cities are introduced by integrating such devices.
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Ankit Garg, Akhil Garg, Wan-Huan Zhou, Kang Tai and M C Deo
For measuring the effect of crop root content on soil water retention curves (SWRC), a simulation approach (multi-gene genetic programming (MGGP)), which develops the model…
Abstract
Purpose
For measuring the effect of crop root content on soil water retention curves (SWRC), a simulation approach (multi-gene genetic programming (MGGP)), which develops the model structure and its coefficients automatically can be applied. However, it does not perform well due to two vital issues related to its generalization: inappropriate formulation procedure of the multi-gene model and the difficulty in model selection. The purpose of this paper is to propose a heuristic-based-MGGP (N-MGGP) to formulate the functional relationship between the water content and two input parameters (soil suction and volumetric crop root content).
Design/methodology/approach
A new simulation approach (heuristic-based-MGGP (N-MGGP)), was proposed to formulate the functional relationship between the water content and two input parameters (soil suction and volumetric crop root content). The proposed approach makes use of a statistical approach of stepwise regression and classification methods (Bayes naïve and artificial neural network (ANN)) to tackle the two issues. Simulated data obtained from the models was evaluated against the experimental data.
Findings
The performance of proposed approach was found to better than that of standardized MGGP. Sensitivity and parametric analysis conducted validates the robustness of model by unveiling dominant input parameters and hidden non-linear relationships.
Originality/value
To the best of authors’ knowledge, an empirical model is developed that measures the effect of crop root content on the SWRCs. The authors also proposed a new genetic programming approach in simulating the crop root content dependent SWRCs.
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Akhil Garg, Venkatesh Vijayaraghavan, Kang Tai, Pravin M Singru, Liang Gao and K S Sangwan
The functioning of multi-gene genetic programming (MGGP) algorithm suffers from the problem of difficulty in model selection. During the preliminary analysis, it is observed that…
Abstract
Purpose
The functioning of multi-gene genetic programming (MGGP) algorithm suffers from the problem of difficulty in model selection. During the preliminary analysis, it is observed that there are many models in the population whose performance is better than that of the model selected with a little compromise on training error. Therefore, an ensemble evolutionary (Ensemble-MGGP) approach is proposed and applied to the data obtained from the vibratory finishing process. The paper aims to discuss these issues.
Design/methodology/approach
Unlike the standard GP, each model participating in Ensemble-MGGP approach is made by combining the set of genes. Predicted residual sum of squares criterion (PRESS) criterion is integrated to improve its evolutionary search. The parametric analysis and sensitivity analysis (SA) conducted on the proposed model validates its robustness by unveiling dominant input parameters and hidden non-linear relationships.
Findings
The results indicate that the proposed Ensemble-MGGP model outperforms the standardized MGGP model. SA and parametric analysis reveals relationships and insights into vibratory finishing process.
Originality/value
Literature emphasises on characterization of vibratory finishing process using the experimental-based-studies. In addition, the issue of difficulty in model selection in genetic programming is addressed. This work proposes a new ensemble evolutionary approach to counter these issues.
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Ying-Ji Chuang and Hsing-Chih Tsai
This paper aims to use a derivative of genetic programming to predict the bond strength of glass fiber-reinforced polymer (GFRP) bars in concrete under the effects of design…
Abstract
Purpose
This paper aims to use a derivative of genetic programming to predict the bond strength of glass fiber-reinforced polymer (GFRP) bars in concrete under the effects of design guidelines. In developing bond strength prediction models, this paper prioritized simplicity and meaningfulness over extreme accuracy.
Design/methodology/approach
Assessing the bond strength of GFRP bars in concrete is a critical issue in designing and building reinforced concrete structures.
Findings
Ultimately, the equation of a linear form of a particular design guideline was suggested as the optimal prediction model. Improvements to the current design guidelines suggested by this model include setting a 1.31 magnification and considering the effects of the three significant parameters of bar diameter (db), minimum cover-to-bar diameter (C/db) and development length to bar diameter (l/db) under an acceptable root mean square error accuracy of around 2 MPa. Furthermore, the model suggests that the original influence parameter of concrete compressive strength (fc) may be removed from bond strength calculations.
Originality/value
The model suggests that the original influence parameter of concrete compressive strength (fc) may be removed from bond strength calculations.
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Abhishek Sharma and Nallasivam .K
The fundamental period of the structure plays an important role in the seismic analysis. This study aims to analyze the modal response of dam, the two-dimensional (2D) FEM model…
Abstract
Purpose
The fundamental period of the structure plays an important role in the seismic analysis. This study aims to analyze the modal response of dam, the two-dimensional (2D) FEM model is developed by using ANSYS 2022 R1 software.
Design/methodology/approach
To examine the optimized mesh size to achieve grid independence, the variable element size has been considered, and its optimal value is calculated using the technique of response surface optimization. Further, the effect of damping ratios of 5%, 8% and 10% is also considered for the free vibration analysis of the dam structure.
Findings
The results show that the natural frequencies of the dam decrease with a reduction in stiffness of the whole structure. Further, the effect of pre-stress conditions is analyzed and the study has proved that the natural frequency increases after considering the pre-stress as initial condition during modal analysis. Further, it is found that the damping has a substantial effect on frequency for higher modes of vibrations.
Research limitations/implications
The study only focused on modal analysis of the gravity dam, and this study’s results can be used further to evaluate the dynamic behavior of the dam including hydrodynamic conditions.
Originality/value
The finite element tool is used to evaluate the modal response of gravity dam incorporating pre-stress and damping ratio along with soil–structure interaction. Moreover, to the best of the authors’ knowledge, no earlier study has been conducted to evaluate the effect of damping and pre-stress conditions on the stability and natural frequency of the system.
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Mohammad Reza Karami, Mohsen Keramati, Reza Maadi and Hossein Moradi Moghaddam
This study aims to examine the reuse of plastic and fly ash (FA) to improve the soil and achieve sustainable development goals.
Abstract
Purpose
This study aims to examine the reuse of plastic and fly ash (FA) to improve the soil and achieve sustainable development goals.
Design/methodology/approach
Sand from the Anzali port was reinforced with Geopet (GP) and stabilized with FA plus 3% sodium hydroxide. The GP was placed in FA-stabilized soil and the California bearing ratio (CBR), and unconfined compressive strength (UCS) tests were performed on samples at the optimum moisture content.
Findings
The results showed that the improvement in the optimum CBR was 174.9%. The UCS increased 15.25% and 48.65% in soil reinforced with three layers of GP plus 15% FA over those containing 10% and 5% FA, respectively. Additionally, the current analysis used response surface methodology (RSM) to investigate the impact of FA percentage, GP layers and their interaction on CBR. The results highlight the efficacy of the used RSM model, as evidenced by the significantly low p-value (<0.0001).
Originality/value
This demonstrates the suitability and effectiveness of RSM for evaluating CBR in this scientific study.
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Vinicius Luiz Pacheco, Lucimara Bragagnolo and Antonio Thomé
The purpose of this article is to analyze the state-of-the art in a systematic way, identifying the main research groups and their related topics. The types of studies found are…
Abstract
Purpose
The purpose of this article is to analyze the state-of-the art in a systematic way, identifying the main research groups and their related topics. The types of studies found are fundamental for understanding the application of artificial neural networks (ANNs) in cemented soils and the potential for using the technique, as well as the feasibility of extrapolation to new geotechnical or civil and environmental engineering segments.
Design/methodology/approach
This work is characterized as being bibliometric and systematic research of an exploratory perspective of state-of-the-art. It also persuades the qualitative and quantitative data analysis of cemented soil improvement, biocemented or microbially induced calcite precipitation (MICP) soil improvement by prediction/modeling by ANN. This study sought to compile and study the state of the art of the topic which possibilities to have a critical view about the theme. To do so, two main databases were analyzed: Scopus and Web of Science. Systematic review techniques, as well as bibliometric indicators, were implemented.
Findings
This paper connected the network between the achievements of the researches and illustrated the main application of ANNs in soil improvement prediction, specifically on cemented-based soils and biocemented soils (e.g. MICP technique). Also, as a bibliometric and systematic review, this work could achieve the key points in the absence of researches involving soil-ANN, and it provided the understanding of the lack of exploratory studies to be approached in the near future.
Research limitations/implications
Because of the research topic the article suggested other applications of ANNs in geotechnical engineering, such as other tests not related to geomechanical resistance such as unconfined compression test test and triaxial test.
Practical implications
This article systematically and critically presents some interesting points in the direction of future research, such as the non-approach to the use of ANNs in biocementation processes, such as MICP.
Social implications
Regarding the social environment, the paper brings approaches on methods that somehow mitigate the computational use, or elements necessary for geotechnical improvement of the soil, thereby optimizing the same consequently.
Originality/value
Neural networks have been studied for a long time in engineering, but the current computational power has increased the implementation for several engineering applications. Besides that, soil cementation is a widespread technique and its prediction modes often require high computational strength, such parameters can be mitigated with the use of ANNs, because artificial intelligence seeks learning from the implementation of the data set, reducing computational cost and increasing accuracy.