Abdolhosein Haddad, Danial Rezazadeh Eidgahee and Hosein Naderpour
The purpose of this study is to introduce a relatively simple method of probabilistic analysis on the dimensions of gravity retaining walls which might lead to a more accurate…
Abstract
Purpose
The purpose of this study is to introduce a relatively simple method of probabilistic analysis on the dimensions of gravity retaining walls which might lead to a more accurate understanding of failure. Considering the wall geometries in the case of allowable stress design, the probability of wall failure is not clearly defined. The available factor of safety may or may not be sufficient for the designed structure because of the inherent uncertainties in the geotechnical parameters. Moreover, two cases of correlated and uncorrelated geotechnical variables are considered to show how they affect the results.
Design/methodology/approach
This study is based on the failure and stability of gravity retaining walls which can be stated in three different modes of sliding, overturning and the foundation-bearing capacity failure. Each of these modes of failure might occur separately or simultaneously with a corresponding probability. Monte Carlo simulation and Taylor series method as two conventional methods of probability analysis are implemented, and the results of an assumed example are calculated and compared together.
Findings
The probability analysis of the failure in each mode is calculated separately and a global failure mode is introduced as the occurrence of three modes of sliding, overturning and foundation-bearing capacity failure. Results revealed that the global mode of failure can be used along with the allowable stress design to show the probability of the worst failure condition. Considering the performance and serviceability level of the retaining structure, the global failure mode can be used. Furthermore, the correlation of geotechnical variables seems to be relatively more dominant on the probability of global failure comparing to each mode of failure.
Originality/value
The introduced terminology of global mode of failure can be used to provide more information and confidence about the design of retaining structures. The resulted graphs maintain a thorough insight to choose the right dimensions based on the required level of safety.
Details
Keywords
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.