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Liquefaction-induced lateral displacement is responsible for considerable damage to engineered structures during major earthquakes. Therefore, an accurate estimation of lateral displacement in liquefaction-prone regions is an essential task for geotechnical experts for sustainable development. This paper presents a novel probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesian belief network (BBN) approach based on an interpretive structural modeling technique. The BBN models are trained and tested using a wide-range case-history records database. The two BBN models are proposed to predict lateral displacements for free-face and sloping ground conditions. The predictive performance results of the proposed BBN models are compared with those of frequently used multiple linear regression and genetic programming models. The results reveal that the BBN models are able to learn complex relationships between lateral displacement and its influencing factors as cause-effect relationships, with reasonable precision. This study also presents a sensitivity analysis to evaluate the impacts of input factors on the lateral displacement.

The unprecedented liquefaction-related land damage during earthquakes has highlighted the need to develop a model that better interprets the liquefaction land damage vulnerability (LLDV) when determining whether liquefaction is likely to cause damage at the ground’s surface. This paper presents the development of a novel comprehensive framework based on select case history records of cone penetration tests using a Bayesian belief network (BBN) methodology to assess seismic soil liquefaction and liquefaction land damage potentials in one model. The BBN-based LLDV model is developed by integrating multi-related factors of seismic soil liquefaction and its induced hazards using a machine learning (ML) algorithm-K2 and domain knowledge (DK) data fusion methodology. Compared with the C4.5 decision tree-J48 model, naive Bayesian (NB) classifier, and BBN-K2 ML prediction methods in terms of overall accuracy and the Cohen’s kappa coefficient, the proposed BBN K2 and DK model has a better performance and provides a substitutive novel LLDV framework for characterizing the vulnerability of land to liquefaction-induced damage. The proposed model not only predicts quantitatively the seismic soil liquefaction potential and its ground damage potential probability but can also identify the main reasons and fault-finding state combinations, and the results are likely to assist in decisions on seismic risk mitigation measures for sustainable development. The proposed model is simple to perform in practice and provides a step toward a more sophisticated liquefaction risk assessment modeling. This study also interprets the BBN model sensitivity analysis and most probable explanation of seismic soil liquefied sites based on an engineering point of view.

This paper proposes a probabilistic graphical model that integrates interpretive structural modeling (ISM) and Bayesian belief network (BBN) approaches to predict cone penetration test (CPT)-based soil liquefaction potential. In this study, an ISM approach was employed to identify relationships between influence factors, whereas BBN approach was used to describe the quantitative strength of their relationships using conditional and marginal probabilities. The proposed model combines major causes, such as soil, seismic and site conditions, of seismic soil liquefaction at once. To demonstrate the application of the propose framework, the paper elaborates on each phase of the BBN framework, which is then validated with historical empirical data. In context of the rate of successful prediction of liquefaction and non-liquefaction events, the proposed probabilistic graphical model is proven to be more effective, compared to logistic regression, support vector machine, random forest and naive Bayes methods. This research also interprets sensitivity analysis and the most probable explanation of seismic soil liquefaction appertaining to engineering perspective.

This study examines the potential of the soft computing technique—namely, Gaussian process regression (GPR), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. The inputs of the model are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ), and internal friction angle (ϕ). The results of the present model were compared with those obtained by two theoretical approaches reported in the literature. The statistical evaluation of results shows that the presently applied paradigm is better than the theoretical approaches and is competing well for the prediction of UBC (qu). This study shows that the developed GPR is a robust model for the qu prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter.

A liquefaction-induced settlement assessment is considered one of the major challenges in geotechnical earthquake engineering. This paper presents random forest (RF) and reduced error pruning tree (REP Tree) models for predicting settlement caused by liquefaction. Standard penetration test (SPT) data were obtained for five separate borehole sites near the Pohang Earthquake epicenter. The data used in this study comprise of four features, namely depth, unit weight, corrected SPT blow count and cyclic stress ratio. The available data is divided into two parts: training set (80%) and test set (20%). The output of the RF and REP Tree models is evaluated using statistical parameters including coefficient of correlation (r), mean absolute error (MAE), and root mean squared error (RMSE). The applications for the aforementioned approach for predicting the liquefaction-induced settlement are compared and discussed. The analysis of statistical metrics for the evaluating liquefaction-induced settlement dataset demonstrates that the RF achieved comparatively better and reliable results.

The paper develops a framework to analyze the interactions among seismic soil liquefaction significant factors using the interpretive structural model (ISM) approach based on cone penetration test. To identify the contextual relationships among the significant factors, systematic literature review approach was used bearing in mind the selection principle. Since multiple factors influence seismic soil liquefaction, determining all factors in soil liquefaction would be extremely difficult, as even a few seismic soil liquefaction factors are not easy to deal with. This study highlighted two main characteristics of seismic soil liquefaction factors. First, the seismic soil liquefaction factors–peak ground acceleration F2 (amax), equivalent clean sand penetration resistance F5 (qc1Ncs), and thickness of soil layer F11 (Ts) influenced soil liquefaction directly and were located at level 2 (top level) in the ISM model, meaning they require additional seismic soil liquefaction factors except thickness of soil layer F11 (Ts) to collaboratively impact on soil liquefaction potential. The multilevel hierarchy reveals that depth of soil deposit F10 (Ds) is formed the base of ISM hierarchy. Secondly, Matrice d’impacts croisés multiplication appliqués à un classement (MICMAC) analysis has been employed for evaluating these identified factors in accordance with driving power and dependence power. Factors with a higher driving power should be given special consideration. Autonomous soil liquefaction factors have no reliance on other soil liquefaction factors and interfere less. In order to identify the significant factors that affect seismic soil liquefaction susceptibility, the model built in this study clearly illustrates the complex relationships between factors and demonstrates the direct and indirect relationships.

Supervised learning algorithms are a recent trend for the prediction of mechanical properties of concrete. This paper presents AdaBoost, random forest (RF), and decision tree (DT) models for predicting the compressive strength of concrete at high temperature, based on the experimental data of 207 tests. The cement content, water, fine and coarse aggregates, silica fume, nano silica, fly ash, super plasticizer, and temperature were used as inputs for the models’ development. The performance of the AdaBoost, RF, and DT models are assessed using statistical indices, including the coefficient of determination (R2), root mean squared error-observations standard deviation ratio (RSR), mean absolute percentage error, and relative root mean square error. The applications of the above-mentioned approach for predicting the compressive strength of concrete at high temperature are compared with each other, and also to the artificial neural network and adaptive neuro-fuzzy inference system models described in the literature, to demonstrate the suitability of using the supervised learning methods for modeling to predict the compressive strength at high temperature. The results indicated a strong correlation between experimental and predicted values, with R2 above 0.9 and RSR lower than 0.5 during the learning and testing phases for the AdaBoost model. Moreover, the cement content in the mix was revealed as the most sensitive parameter by sensitivity analysis.

Rockburst is a complex phenomenon of dynamic instability in the underground excavation of rock. Owing to the complex and unclear rockburst mechanism, it is difficult to accurately predict and reasonably assess the rockburst potential. With the increasing availability of case histories from rock engineering and the advancement of data science, the data mining algorithms provide a good way to predict complex phenomena, like rockburst potential. This paper investigates the potential of J48 and random tree algorithms to predict the rockburst classification ranks using 165 cases, with four parameters, namely maximum tangential stress of surrounding rock, uniaxial compressive strength, uniaxial tensile strength, and strain energy storage index. A comparison of developed models’ performances reveals that the random tree gives more reliable predictions than J48 and other empirical models (Russenes criterion, rock brittleness coefficient criterion, and artificial neural networks). Similar comparisons with convolutional neural network resulted at par performance in modeling the rockburst hazard data.

Predicting pillar stability in underground mines is a critical problem because the instability of the pillar can cause large-scale collapse hazards. To predict the pillar stability for underground coal and stone mines, two new models (random tree and C4.5 decision tree algorithms) are proposed in this paper. Pillar stability depends on the parameters: width of the pillar (W), height of the pillar (H), W/H ratio, uniaxial compressive strength of the rock (&sigma, ucs), and pillar stress (&sigma, p). These parameters are taken as input variables, while underground mines pillar stability as output. Various performance indices, i.e., accuracy, precision, recall, F-measure, Matthews correlation coefficient (MCC) were used to evaluate the performance of the models. The performance evaluation of the established models showed that both models were able to predict pillar stability with reasonable accuracy. Results of the random tree and C4.5 decision tree were also compared with available models of support vector machine (SVM) and fishery discriminant analysis (FDA). The results show that the proposed random tree provides a reliable and feasible method of evaluating the pillar stability for underground mines.

Waterborne diseases have become one of the major public health concerns worldwide. This study is aimed to investigate and develop spatial distribution mapping of the potable water quality parameters in the city of Peshawar, Pakistan. A total of 108 water samples collected across the entire study area were subjected to physio-chemical and biological analyses. Tested parameters included pH, turbidity, temperature, fluoride concentration levels, and bacterial counts (faecal coliforms). Inverse distance weighting (IDW) interpolation in geographic information systems (GIS) was used for spatial analysis. Test results revealed that 48% of water samples had faecal coliforms count (per 100 mL) greater than World Health Organization (WHO) minimum limits, while 31% of samples had fluoride concentrations in excess of the WHO maximum guide values. Spatial distribution mapping was developed for faecal coliforms count and fluoride ion concentration using ArcGIS to highlight the high-risk settlements in the study area. Results showed that around 20% area under faecal coliforms and approximately 33% area based on fluoride concentrations fall under the need for treatment category. The pH and turbidity were found in compliance with WHO desirable limits. The sanitary inspection score significantly depicted that ineffective multi-barrier approaches consequently deteriorated the water quality at the consumer&rsquo, s end. Findings from the present study shall be useful to policymakers for adopting necessary remedial measures before it severely affects public health.

The unprecedented liquefaction related land damage during earthquake has highlighted the need to develop a model that better interpret the liquefaction land damage vulnerability (LLDV) when determining whether liquefaction is probably to cause damage at the ground surface. This paper presents the development of a new comprehensive framework explored from select case history records using Bayesian belief network (BBN) methodology, to assess liquefaction land damage vulnerability. The BBN based liquefaction land damage vulnerability model has been developed by integrating multi-related factors of soil liquefaction and its induced hazards into one model on the basis of cone penetration test (CPT) case history records using K2 machine learning (ML) algorithm and domain knowledge (DK). The paper highlights that the proposed BBN is a substitutive novel LLDV framework and showed relatively better performance when compared with C4.5 decision tree (DT) - J48 model, naive Bayesian (NB) classifier, and BBN – K2 ML prediction methods in terms of overall accuracy (OA) and the Cohen’s kappa coefficient. The proposed BBN–K2 and DK model is simple to perform in practice and the results are likely to assist decisions on seismic risk mitigation measures for sustainable development and provide a step towards a more sophisticated liquefaction risk assessment modeling.

Seismic soil liquefaction is considered as one of the most complex geotechnical earthquake engineering problems owing to the uncertainty and complexity involved in soil parameters, seismic parameters, and site condition factors. Each one of these parameters contains a variety of factors that trigger liquefaction and have varying degrees of importance. However, estimating accurate and reliable liquefaction-induced hazards requires identification and benchmarking of the most influential factors that control soil liquefaction. Seismic soil liquefaction factors were identified by Systematic Literature Review (SLR) approach and analyzed through Interpretive Structural Modeling (ISM) and the Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) methodologies. The ISM model presented the relationships between fifteen seismic soil liquefaction factors and their benchmarking position from higher to lower-level significant factors in hierarchy. MICMAC is used to examine the strength of the relationship between seismic soil liquefaction significant factors based on their driving and dependence power. This research characterizes the identification and benchmarking of the seismic soil liquefaction factors and their relationships. The results show that the factors&mdash, duration of earthquake, peak ground acceleration, drainage condition, and standard penetration test (SPT) blow counts&mdash, influence seismic soil liquefaction directly and soil type is the governing factor that forms the base of the ISM hierarchy and consequently triggers seismic soil liquefaction. The results provide a more accurate way of selecting significant factors for establishment of seismic soil liquefaction potential and liquefaction-induced hazards risk assessment models.

In seismically active regions, soil liquefaction is a serious geotechnical engineering problem that mainly occurs in saturated granular soils with a shallow groundwater table. Significant seismic hazards are present in Kamra, Pakistan. With the rapid increase in construction in recent years, the evaluation of liquefaction is now considered to be more important for land use planning and development. The intent of this study is to highlight soil liquefaction susceptibility that will eventually support the national authorities in developing guidelines for sustainable development and the mitigation of liquefaction. The typical subsoil profile of Kamra consists of silty gravel (GM) overlain by silty sand (SM), poorly graded sand (SP), and fill layers. Kamra is close to the active Ranja–Khairabad fault with a peak ground acceleration of 0.24g. The river Sehat and the Ghazi Brotha canal pass through the study area. In this study, the soil liquefaction potential in Kamra was assessed at 10 different sites (50 boreholes) by using a stress-based procedure for calculating the factor of safety against soil liquefaction. The results revealed that the middle layers, i.e., poorly graded sand and silty sand in the subsoil profile, are extremely susceptible to liquefaction during earthquakes with magnitudes between 7.5 and 8.0 in Kamra. The correlation between the factor of safety and the equivalent clean-sand-corrected standard penetration test (SPT) blow counts according to the earthquake magnitudes was developed and can also be utilized for areas adjoining Kamra that have the same subsoil profile.