Reliability Analysis of Piled Raft Foundation Using a Novel Hybrid Approach of ANN and Equilibrium Optimizer

Bardhan, Abidhan; Manna, Priyadip; Kumar, Vinay; Burman, Avijit; Žlender, Bojan; Samui, Pijush

doi:10.32604/cmes.2021.015885

Cited by 12 publications

(5 citation statements)

References 75 publications

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“…It is important to note that, right after model development, various performance metrics including Adj.R 2 , NS, PI, R 2 , RMSE, RSR, VAF, and WI, were used to evaluate hybrid LSSVMs. Note that these indices are frequently used [ 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 ] to evaluate the generalization capabilities of any prediction model from a variety of perspectives, including correlation accuracy, related error, variance, and so on. The expressions of these indices can be given as follows:

where p and

represent the total number of input parameters and observations, respectively; …”

Section: Resultsmentioning

confidence: 99%

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

et al. 2022

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Nowadays, concretes blended with pozzolanic additives such as fly ash (FA), silica fume (SF), slag, etc., are often used in construction practices. The utilization of pozzolanic additives and industrial by-products in concrete and grouting materials has an important role in reducing the Portland cement usage, the CO2 emissions, and disposal issues. Thus, the goal of the present work is to estimate the compressive strength (CS) of polyethylene terephthalate (PET) and two supplementary cementitious materials (SCMs), namely FA and SF, blended cementitious grouts to produce green mix. For this purpose, five hybrid least-square support vector machine (LSSVM) models were constructed using swarm intelligence algorithms, including particle swarm optimization, grey wolf optimizer, salp swarm algorithm, Harris hawks optimization, and slime mold algorithm. To construct and validate the developed hybrid models, a sum of 156 samples were generated in the lab with varying percentages of PET and SCM. To estimate the CS, five influencing parameters, namely PET, SCM, FLOW, 1-day CS (CS1D), and 7-day CS (CS7D), were considered. The performance of the developed models was assessed in terms of multiple performance indices. Based on the results, the proposed LSSVM-PSO (a hybrid model of LSSVM and particle swarm optimization) was determined to be the best performing model with R2 = 0.9708, RMSE = 0.0424, and total score = 40 in the validation phase. The results of sensitivity analysis demonstrate that all the input parameters substantially impact the 28-day CS (CS28D) of cementitious grouts. Among them, the CS7D has the most significant effect. From the experimental results, it can be deduced that PET/SCM has no detrimental impact on CS28D of cementitious grouts, making PET a viable alternative for generating sustainable and green concrete. In addition, the proposed LSSVM-PSO model can be utilized as a novel alternative for estimating the CS of cementitious grouts, which will aid engineers during the design phase of civil engineering projects.

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where p and

represent the total number of input parameters and observations, respectively; …”

Section: Resultsmentioning

confidence: 99%

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

et al. 2022

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“…The performance parameters may be divided into error measuring parameters (RMSE, MAE, RSR, and WMAPE) and parameters for trend measurement (R 2 , adj. R 2 , VAF, PI, WI, and NS) 67–69 Coefficient of determination (R 2 )

R^{2} goodbreak= \frac{\sum_{i = 1}^{n} {(d_{i} - d_{italicmean})}^{2} - \sum_{i = 1}^{n} {(d_{i} - y_{i})}^{2}}{\sum_{i = 1}^{n} {(d_{i} - d_{italicmean})}^{2}}

…”

Section: Performance Evaluationmentioning

confidence: 99%

A comparative study of prediction of compressive strength of ultra‐high performance concrete using soft computing technique

Kumar

Rai

Samui

2023

Structural Concrete

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Concrete which is the most commercialized construction material and thus it plays a key role in this era of development and hence its evolution is of utmost importance and therefore the evolution of the quality of concrete to that of its highly evolved type namely, ultra‐high performance concrete (UHPC) is undeniably the boon to this sector. Though, the correlations between the technical characteristics of UHPC and the composition of its mixture are complicated, nonlinear, and complex to characterize using standard statistical techniques. This paper is intended to use both deep neural network and ensemble machine learning algorithms namely gradient boosting, extreme gradient boosting, random forest regressor, extra tree regressor, and voting regressor trained with an 810 UHPC mixture collections with 15 input variables to predict its compressive strength. After adjusting a regression model, the prediction efficiency and generalization ability of the developed models are validated using a number of performance parameters. It was established that all employed models performed better at forecasting result, the extra tree regressor was the most accurate followed by extreme gradient boosting.

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“…Meta-heuristic algorithm (Meta-heuristic) [83][84][85] is a method for solving complex optimization problems based on the mechanism of computational intelligence to find optimal or satisfactory solutions, sometimes called intelligent optimization algorithm (Intelligent optimization algorithm) [86], intelligent optimization through the biological, physical, chemical, social, artistic and other systems or fields in the relevant behavior By understanding the relevant behaviors, functions, experiences, rules, and mechanisms of action in biological, physical, chemical, social, and artistic systems or fields, intelligent optimization reveals the design principles of optimization algorithms, refines the corresponding feature models under the guidance of specific problem characteristics, and designs intelligent iterative search-based optimization algorithms.…”

Section: Meta-heuristic Algorithmmentioning

confidence: 99%

Recent Advances of Deep Learning in Geological Hazard Forecasting

Wang¹,

Sun²,

Chen³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Geological hazard is an adverse geological condition that can cause loss of life and property. Accurate prediction and analysis of geological hazards is an important and challenging task. In the past decade, there has been a great expansion of geohazard detection data and advancement in data-driven simulation techniques. In particular, great efforts have been made in applying deep learning to predict geohazards. To understand the recent progress in this field, this paper provides an overview of the commonly used data sources and deep neural networks in the prediction of a variety of geological hazards. KEYWORDSGeological hazard; deep learning; neural networks; geohazard data sources; earthquake; volcanic 1 Introduction Geological hazards are caused by endogenous and exogenous geological processes or anthropogenic factors on earth [1,2], which encompass a broad range of abnormal stratigraphic activity or extreme changes in geological environments, such as landslides, avalanches, debris flows, earthquakes, etc. Geological hazards pose a serious threat to human life and property [3]. Only by accurately forecasting the occurrence of geohazards can emergency measures be devised to mitigate the damage caused by the impending disaster. Geological hazard forecasting refers to the use of logical reasoning, numerical simulation, and comprehensive analysis based on historical geological hazard activity patterns, formation conditions, occurrence mechanisms, and other factors to speculate and assess the development and changes of geological hazards and the possible degree of danger and damage in a certain period in the future. Geohazard forecasting has attracted tremendous attention nowadays, particularly considering natural resource scarcity, environmental degradation, population expansion, sustainable development, and world economic integration. Many efforts have been made in forecasting geological hazards in the past decades. The "3S technology", including Global Position System (GPS), Geographic Information System (GIS) [4], and Remote Sensing (RS), has been widely applied in the field of geological hazard forecasting [5]. The

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Reliability Analysis of Piled Raft Foundation Using a Novel Hybrid Approach of ANN and Equilibrium Optimizer

Cited by 12 publications

References 75 publications

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

A comparative study of prediction of compressive strength of ultra‐high performance concrete using soft computing technique

Recent Advances of Deep Learning in Geological Hazard Forecasting

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