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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.

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.

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.

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.

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.

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.

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.

The study is carried out to analytically reconnoiter geotechnical index properties of subgrade soils as key variables that shape the cost profile of road infrastructure projects in a tropical geographic setting with starkly heterogenous ground conditions.

Using the Niger Delta region, as a point of reference, data on geotechnical index properties of subgrade soils at spatially dispersed locations for 61 completed highway projects are collated. Exploratory statistical tests were carried out to infer significant associations with final project costs before regression analysis. Regression analysis is principally deployed as an explanatory analytical tool, relevant to quantify the sensitivity of highway project costs to the individual and collective impact of geotechnical variables.

Several parameters of expansivity and compressibility exhibited significantly strong associations with the final costs recorded on the highway projects. The statistical analysis further established a cause-effect relationship, whereby small changes in the geotechnical properties of sub-grade soils at project locations, would result in disproportionately large changes in the cost of road construction.

The study findings provide insight into the sensitivity of road construction costs to geotechnical variables, which can serve as a useful input in financial risk analysis for development appraisal and the generation of location adjustment factors.

The study statistically demonstrates location-induced construction cost profiles, triggered in response to the spatial geotechnical variability and occurrence of problem subgrade soils in the humid tropics, which may be different from those traditionally established in studies of cold and temperate climate soils.

This paper aims to investigate the statistical validity of geotechnical risk factors in accounting for cost overruns in highway projects. The study hypothesises that “latent pathogens” because of mismanaged geotechnical risk, which lay dormant in organisational practices of highway agencies, trigger cost overruns.

To test this hypothesis, cost and geotechnical data gathered for 61 completed highway projects, executed in the Niger Delta, recording unusually high cost overruns, along with qualitative data from 16 interviews with the project commissioners, were comprehensively analysed via regression modelling, to statistically explain recorded cost variance.

The results provide empirical evidence supporting a cause–effect relationship between the extent of cost overrun and key geotechnical factors. It is suggested that positive changes made in the geotechnical practices of the highway agencies will produce an expected exponential decrease in the level of cost overruns recorded in highway projects.

The study is limited to explaining the propagation of unusually high cost overruns in the geologic setting of the Niger Delta region of Nigeria. As such there is a need to test the generalisability of the theory presented.

The emergent view of geotechnical practice calls for further research, necessary to align geotechnical best practice into highway project delivery in the Niger Delta region.

The study used a robust methodological approach to understanding the propagation of cost overruns in highway projects, based on a characterisation of geotechnical intricacies, which is unprecedented in cost overrun research.

The purpose of this paper is to present an investigation of the effect of different gravity conditions on the penetration mechanism using the two-dimensional Distinct Element Method (DEM), which ranges from high gravity used in centrifuge model tests to low gravity incurred by serial parabolic flight, with the aim of efficiently analyzing cone penetration tests on the lunar surface.

Seven penetration tests were numerically simulated on loose granular ground under different gravity conditions, i.e. one-sixth, one-half, one, five, ten, 15 and 20 terrestrial gravities. The effect of gravity on the mechanisms is examined with aspect to the tip resistance, deformation pattern, displacement paths, stress fields, stress paths, strain and rotation paths, and velocity fields during the penetration process.

First, under both low and high gravities, the penetration leads to high gradients of the value and direction of stresses in addition to high gradients in the velocity field near the penetrometer. In addition, the soil near the penetrometer undergoes large rotations of the principal stresses. Second, high gravity leads to a larger rotation of principal stresses and more downward particle motions than low gravity. Third, the tip resistance increases with penetration depth and gravity. Both the maximum (steady) normalized cone tip resistance and the maximum normalized mean (deviatoric) stress can be uniquely expressed by a linear equation in terms of the reciprocal of gravity.

This study investigates the effect of different gravity conditions on penetration mechanisms by using DEM.

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.

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.

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.

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.

Nowadays, application of biopolymers on geotechnical engineering works is booming to avoid the harsh effects on environment by using conventional methods for soil treatment. In this present study, xanthan gum (XG) is used as a biopolymer to improve dispersive properties of the soils because these soils are easily prone to erosion, which may lead to the damage of many hydraulic structures.

In the present study, attempts are made to reduce the dispersive potential and increase the Strength and erosion resistance by treating the soils with various percentages of XG (0.5%, 1%, 1.5% and 2%). To assess the dispersive potential and erosion resistance of soils, tests such as double hydrometer test, pinhole erosion test, crumb test and cylinder dispersion test were conducted. Further tests were expanded for its geotechnical characteristics such as Atterberg’s limits, standard proctor test, unconfined compressive strength test, one-dimensional consolidation for various curing days. Scanning electron microscopy analysis was also carried out to know the microscopic view towards its particle orientation and bindings. Chemical tests such as sodium absorption ratio, total dissolved solids (TDS) and percentage sodium (PS), electronic conductivity and pH tests were also conducted.

The results revealed that there is a reduction in the dispersive potential of XG treated soils for all the combinations. Addition of XG decreased the PS in the soil as a result dispersivity of soil decreased. Strength and erosion resistance of soil increased with the addition of XG and 1% XG was observed to be the optimum percentage for stabilizing these types of soils.

These results will be very much helpful for engineers when they come across with dispersive soils for better handling and management.

The originality of this study was an attempt towards sustainable development by treating dispersive soils with XG and effects on various geotechnical and dispersive characterizes.

This study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.

A thorough literature review (based on 165 articles) was conducted using Elsevier's Scopus due to its simplicity and as it encapsulates an extensive variety of databases to identify the literature related to the scope of the present study.

The following items were extracted: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the journals they are published by. Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas, such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering. ANN has been a widely used tool, while the researchers have also used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario.

The findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field.

There are no studies to date which serves as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study sheds light on various studies, which have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from.

The engineering management of housing subsidence cases is an important field of work for many UK engineers, and remains of enduring interest to householders, insurers and other parties involved in the construction and maintenance of residential buildings. There are often difficulties in the diagnosis and repair of buildings subject to subsidence damage due to several factors, including the complex interaction between the various causative agents, the lack of a systematic investigation procedure, and the large number of available courses of remedial action. In many cases, inaccurate diagnosis of the subsidence problem has resulted in expensive remedial measures which are either unnecessary or inappropriate (and fail to arrest the movement). This paper reviews the management of subsidence cases and describes the development of a knowledge‐based system intended to improve existing procedures by ensuring greater accuracy, consistency and effectiveness of the management regime adopted by engineers. The system addresses three key aspects of the management procedure: initial diagnosis, choice of an appropriate course of investigations, and the specification of effective remedial measures. The benefits of the knowledge‐based system are contained in the concluding section of the paper.

The purpose of this paper is to attempt to use two industrial wastes; waste foundry sands (WFS) and molasses (M) along with lime (L) to improve the strength characteristics of clayey soil.

In the first part of the study, the optimum percentages of materials (WFS, molasses, lime) have been found out by conducting differential free swell (DFS) and consistency limit tests on clayey soil by adding various admixtures. The second and third part of the study investigates the compaction behaviour and unconfined compressive strength (UCS) of clayey soil on addition of optimum amount of various materials alone and in combination with each other. Finally, the micro-structural behaviour of addition of optimum percentages of lime, WFS and molasses using Scanning electron microscopic technique has been discussed.

The laboratory results revealed that the addition of optimum content of lime along with WFS and molasses reduced DFS and plasticity index and increased maximum dry density and UCS values. The microstructural behaviour showed that the presence of lime and molasses filled the voids present in the soil and the addition of WFS helped in providing compact structure, thus improving the strength characteristics.

The study will be helpful in designing low-cost pavement designs for rural roads.

The adverse effect of waste materials on environment may be solved by using them in improving the strength characteristics of clayey soils, thereby providing healthy environment to living beings.

The study will help to provide low-cost methods to improve strength characteristics of clayey soil along with the use of waste materials; the disposal of whose is a challenging task.

Large deformation problems are frequently encountered in various fields of geotechnical engineering. The particle finite element method (PFEM) has been proven to be a promising method to solve large deformation problems. This study aims to develop a computational framework for modelling the hydro-mechanical coupled porous media at large deformation based on the PFEM.

The PFEM is extended by adopting the linear and quadratic triangular elements for pore water pressure and displacements. A six-node triangular element is used for modelling two-dimensional problems instead of the low-order three-node triangular element. Thus, the numerical instability induced by volumetric locking is avoided. The Modified Cam Clay (MCC) model is used to describe the elasto-plastic soil behaviour.

The proposed approach is used for analysing several consolidation problems. The numerical results have demonstrated that large deformation consolidation problems with the proposed approach can be accomplished without numerical difficulties and loss of accuracy. The coupled PFEM provides a stable and robust numerical tool in solving large deformation consolidation problems. It is demonstrated that the proposed approach is intrinsically stable.

The PFEM is extended to consider large deformation-coupled hydro-mechanical problem. PFEM is enhanced by using a six-node quadratic triangular element for displacement and this is coupled with a four-node quadrilateral element for modelling excess pore pressure.