Prediction of geotechnical properties of treated fibrous peat by artificial neural networks

Dehghanbanadaki, Ali; Sotoudeh, Mohammad Ali; Golpazir, Iman; Keshtkarbanaeemoghadam, Azin; Ilbeigi, Marjan

doi:10.1007/s10064-017-1213-2

Cited by 27 publications

(10 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Dehghanbanadaki has approached the artificial intelligence to predict UCSs in soils reinforced by cement or even fibrous materials. Also, the implementation of training methods into the artificial ANN’s process was compiled by algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA), which enable the improvement of the ANN model (Dehghanbanadaki et al , 2019a; Sun and Xu, 2016; Dehghanbanadaki et al , 2019b).…”

Section: Resultsmentioning

confidence: 99%

Artificial neural networks applied for solidified soils data prediction: a bibliometric and systematic review

Pacheco

Bragagnolo

Thomé

2021

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Artificial neural networks applied for solidified soils data prediction: a bibliometric and systematic review

Pacheco

Bragagnolo

Thomé

2021

View full text Add to dashboard Cite

show abstract

“…For this study, the former will utilise 75% of the data, with the remaining 25% left for testing. This ratio is typical for neural network studies as training percentages can range from 70% to around 85% depending on the quantity of data (Dehghanbanadaki et al, 2019;Zaleski and Prozument, 2018).…”

Section: Generation Of Datasetmentioning

confidence: 99%

Prediction of blast loading in an internal environment using artificial neural networks

Dennis

Pannell

Smyl

et al. 2020

International Journal of Protective Structures

View full text Add to dashboard Cite

Explosive loading in a confined internal environment is highly complex and is driven by nonlinear physical processes associated with reflection and coalescence of multiple shock fronts. Prediction of this loading is not currently feasible using simple tools, and instead specialist computational software or practical testing is required, which are impractical for situations with a wide range of input variables. There is a need to develop a tool which balances the accuracy of experiments or physics-based numerical schemes with the simplicity and low computational cost of an engineering-level predictive approach. Artificial neural networks (ANNs) are formed of a collection of neurons that process information via a series of connections. When fully trained, ANNs are capable of replicating and generalising multi-parameter, high-complexity problems and are able to generate new predictions for unseen problems (within the bounds of the training variables). This article presents the development and rigorous testing of an ANN to predict blast loading in a confined internal environment. The ANN was trained using validated numerical modelling data, and key parameters relating to formulation of the training data and network structure were critically analysed in order to maximise the predictive capability of the network. The developed network was generally able to predict specific impulses to within 10% of the numerical data: 90% of specific impulses in the unseen testing data, and between 81% and 87% of specific impulses for data from four additional unseen test models, were predicted to this accuracy. The network was highly capable of generalising in areas adjacent to reflecting surfaces and as those close to ambient outflow boundaries. It is shown that ANNs are highly suited to modelling blast loading in a confined internal environment, with significant improvements in accuracy achievable if a robust, well distributed training dataset is used with a network structure that is tailored to the problem being solved.

show abstract

“…De otra parte, se halló un estudio que compara el Perceptrón Multicapa y red neuronal con enjambre de partículas [22], para medir las propiedades geotécnicas de la turba mezclada con diferentes proporciones de cemento. Los resultados indican que la resistencia a la compresión no confinada y el CBR de las muestras tratadas con cemento de 300 kg/m 3 aumentaron solamente en un factor de 8,54 y 13,66, en comparación con la turba sin tratar.…”

Section: Parámetros De Compactación Y Cbr Del Suelounclassified

Importance of artificial neural networks in civil engineering: a systematic review of the literature

Purizaca

Barturen

Pérez

et al. 2020

ITECKNE

View full text Add to dashboard Cite

Artificial neural networks (ANN) have a relevant role nowadays; several areas apply this technique due to the advantages they have to solve complex problems with many constraints compared to traditional methods, which are becoming outdated. Very little is known about this technique and its application in different branches of Civil Engineering. For this reason, the present research aims to conduct a systematic review of the literature to identify the use of this technique and to determine the results of the application of ANN models in civil engineering. A total of 41 scientific articles were included, distributed as follows: 6 in Scopus, 1 in ScienceDirect, 23 in ProQuest, 7 in Google Scholar, 2 in DialNet, 2 in SciELO. It was found that ANNs are used to predict or forecast variables associated with the fields of study in civil engineering; 8 applications of ANN were found for concrete properties, 11 for soil properties, 5 for seismic analysis, 9 for hydraulics, 7 for real estate valuation and 1 for bridge design. Likewise, it was found that the multilayer Perceptron is the most used ANN model, achieving an average R2 of 0.99, which shows advantages to solve problems with precision, in shorter times, with missing data in the data sets, as well as the reduction of the error factor.

show abstract

Prediction of geotechnical properties of treated fibrous peat by artificial neural networks

Cited by 27 publications

References 19 publications

Artificial neural networks applied for solidified soils data prediction: a bibliometric and systematic review

Artificial neural networks applied for solidified soils data prediction: a bibliometric and systematic review

Prediction of blast loading in an internal environment using artificial neural networks

Importance of artificial neural networks in civil engineering: a systematic review of the literature

Contact Info

Product

Resources

About