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.

中文翻译：

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应用于固化土壤数据预测的人工神经网络：文献计量学和系统评价

目的

本文的目的是系统地分析最新技术，确定主要研究小组及其相关主题。所发现的研究类型对于理解人工神经网络 (ANN) 在胶结土壤中的应用和使用该技术的潜力以及外推到新的岩土工程或土木和环境工程领域的可行性至关重要。

设计/方法/方法

这项工作的特点是对最新技术的探索性观点进行文献计量和系统研究。它还通过人工神经网络的预测/建模说服了胶结土壤改良、生物胶结或微生物诱导的方解石沉淀 (MICP) 土壤改良的定性和定量数据分析。本研究旨在汇编和研究该主题的最新技术，从而有可能对该主题提出批判性观点。为此，我们分析了两个主要数据库：Scopus 和 Web of Science。实施了系统审查技术以及文献计量指标。

发现

本文连接了研究成果之间的网络，并说明了人工神经网络在土壤改良预测中的主要应用，特别是在水泥基土壤和生物水泥土壤（例如 MICP 技术）中。此外，作为文献计量学和系统评价，这项工作可以在没有涉及土壤 ANN 的研究的情况下实现关键点，并且它提供了对在不久的将来缺乏探索性研究的理解。

研究限制/影响

由于研究主题，文章提出了人工神经网络在岩土工程中的其他应用，例如其他与岩土力学阻力无关的测试，如无侧限压缩试验和三轴试验。

实际影响

本文系统地、批判性地提出了未来研究方向的一些有趣观点，例如在生物水泥过程中使用 ANN 的非方法，如 MICP。

社会影响

关于社会环境，本文提出了一些方法，以某种方式减轻计算使用或土壤岩土改良所需的元素，从而优化相同的结果。

原创性/价值

神经网络在工程中已经研究了很长时间，但当前的计算能力增加了几个工程应用的实现。除此之外，土壤胶结是一种广泛使用的技术，其预测模式通常需要较高的计算强度，这些参数可以通过使用 ANN 来减轻，因为人工智能寻求从数据集的实施中学习，从而降低计算成本并提高准确性。