Showing papers by "Pijush Samui published in 2022"

Genetic prediction of ICU hospitalization and mortality in COVID‐19 patients using artificial neural networks

Abstract: There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease‐19 (COVID‐19). We aimed to a) identify complement‐related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement‐related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID‐19. Through targeted next‐generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH‐related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID‐19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID‐19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID‐19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype.

Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil consolidation parameter

Abstract: The study proposes an improved Harris hawks optimization (IHHO) algorithm by integrating the standard Harris hawks optimization (HHO) algorithm and mutation-based search mechanism for developing a high-performance machine learning solution for predicting soil compression index. HHO is a newly introduced meta-heuristic optimization algorithm (MOA) used to solve continuous search problems. Compared to the original HHO, the proposed IHHO can evade trapping in local optima, which in turn raises the search capabilities and enhances the search mechanism relying on mutation. Subsequently, a novel meta-heuristic-based soft computing technique called ELM-IHHO was established by integrating IHHO and extreme learning machine (ELM) to estimate soil compression index. A sum of 688 consolidation test data was collected for this purpose from an ongoing dedicated freight corridor railway project. To evaluate the generalization capability of the proposed ELM-IHHO model, a detailed comparison between ELM-IHHO and other well-established MOAs, such as particle swarm optimization, genetic algorithm, and biogeography-based optimization integrated with ELM, was performed. Based on the outcomes, the ELM-IHHO model exhibits superior performance over the other MOAs in predicting soil compression index.

Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models

Abstract: Abstract The use of three artificial neural network (ANN)-based models for the prediction of unconfined compressive strength (UCS) of granite using three non-destructive test indicators, namely pulse velocity, Schmidt hammer rebound number, and effective porosity, has been investigated in this study. For this purpose, a sum of 274 datasets was compiled and used to train and validate three ANN models including ANN constructed using Levenberg–Marquardt algorithm (ANN-LM), a combination of ANN and particle swarm optimization (ANN-PSO), and a combination of ANN and imperialist competitive algorithm (ANN-ICA). The constructed ANN-LM model was proven to be the most accurate based on experimental findings. In the validation phase, the ANN-LM model has achieved the best predictive performance with R = 0.9607 and RMSE = 14.8272. Experimental results show that the developed ANN-LM outperforms a number of existing models available in the literature. Furthermore, a Graphical User Interface (GUI) has been developed which can be readily used to estimate the UCS of granite through the ANN-LM model. The developed GUI is made available as a supplementary material.

Probabilistic slope stability analysis of Heavy-haul freight corridor using a hybrid machine learning paradigm

Abstract: With the rising freight demand, specialized heavy-haul railway corridors allow heavier trains to transport heavy freight, improving productivity and lowering unit costs. Generally, a heavy-haul corridor necessitates a significant investment, thus the risk assessment of a rail-track system must be extensively evaluated during the design phase. From the standpoint of serviceability, this study presents a probabilistic slope stability analysis of heavy-haul freight corridor using an efficient hybrid computational technique. The present approach, i.e., ANN-MPA, is an amalgamation of an artificial neural network (ANN) and marine predators algorithm (MPA). The newly constructed ANN-MPA was used to perform probabilistic analysis of a 12.293 m high embankment of heavy-haul freight corridor of Indian Railways with a design axle load of 32.5 MT. The concept of probability theory and statistics were used to map the soil uncertainties through the first-order second-moment method. The results of the proposed ANN-MPA model were evaluated and compared with other hybrid ANNs constructed with seven distinct swarm intelligence algorithms. In the validation phase, the proposed ANN-MPA outperformed (R2 = 0.9931 and RMSE = 0.0233) other hybrid ANNs and was used to perform probabilistic analysis of a 12.293 m high embankment. The reliability index and the probability of failure were computed under seismic and non-seismic conditions, taking into consideration the influence of uncertainties in soil parameters. Using the proposed approach, the failure probability of the 12.293 m high soil slope under different seismic conditions can be evaluated rationally and efficiently.

Landslide Susceptibility Research Combining Qualitative Analysis and Quantitative Evaluation: A Case Study of Yunyang County in Chongqing, China

Abstract: Machine learning-based methods are commonly used for landslide susceptibility mapping. Most of the recent publications focused on quantitative analysis, i.e., improving data processing methods, comparing and perfecting the data-driven model itself, but rarely taking the qualitative aspects of the local landslide occurrences into consideration and the further analysis of the key features was always lacking. This study aims to combine qualitative and quantitative analysis and examine its effect on mapping accuracy; based on the feature importance ranks and the related literature, the key features for identifying landslide/non-landslide points of different sub-zones were further analyzed. Before modeling, the study area Yunyang County, Chongqing City, China, was manually divided into four sub-zones based on the information from geological hazards exploration in Chongqing, including the mechanism of landslide formation and sliding failure and geomorphic unit characteristics. Upon the qualitative analysis basis, five grid searches tuned random forest models (one for the whole region and four for the sub-zones independently) were established by 1654 data points and 20 conditioning features. Compared with the conventional data-driven method, the integrated quantitative evaluation based on the qualitative analysis results showed higher reliability, which not only improved the mapping accuracy but also increased the AUC values of all four sub-models, which were 8.8%, 2.3%, 1.9% and 9.1% higher than that of the parent model. Moreover, the quantitative evaluation based on the qualitative analysis revealed the key factors affecting local landslide formation. Therefore, qualitative analysis is recommended in future landslide susceptibility modeling with the additional combination of data-driven methods.

Prediction of rock strain using soft computing framework

Abstract: This study presents a comparative analysis of conventional soft computing techniques in predicting strain of a rock sample fitted with several strain gauges in horizontal and vertical directions. For this purpose, a total of 2040 experimental test data was obtained from an experimental setup. Six conventional soft computing techniques, namely relevance vector machine, genetic programming, multivariate adaptive regression spline, minimax probability machine regression, emotional neural network, and extreme learning machine were used. These models were trained and validated with 70% and 30% observations of the main dataset, respectively. Experimental results demonstrate that most of the employed models have attained the most accurate prediction of rock strain. Overall, the result of the RVM model is significantly better than those obtained from other soft computing methods employed in this study. In the testing phase, the RVM model attained 94.0% and 99.8% accuracies (in terms of R2 value) against horizontal and vertical directions, respectively. Based on the experimental results, the RVM model has the potential to be a new alternative to assist geological/geotechnical engineers to estimate the rock strain in the design phase of civil engineering projects.

Hybrid ELM and MARS-Based Prediction Model for Bearing Capacity of Shallow Foundation

Abstract: The nature of soil varies horizontally as well as vertically, owing to the process of the formation of soil. Thus, ensuring the safe design of geotechnical structures has been a major challenge. In shallow foundations, conducting field tests is expensive and time-consuming and often conducted on significantly scaled-down models. Empirical models, too, have been found to be the least reliable in the literature. The study proposes AI-based techniques to predict the bearing capacity of a shallow foundation, simulated using the datasets obtained in experiments conducted in different laboratories in the literature. The results of the ELM-EO and ELM-PSO hybrid models are compared with that of the ELM and MARS models. The performance of the models is analyzed and compared with each other using various performance parameters. The models are graded to each other using rank analysis and the visual interpretations are provided using error matrices and REC curves. ELM-EO is concluded to be the best performing model (R2 and RMSE equal to 0.995 and 0.01, respectively, in the testing phase), closely followed by ELM-PSO, MARS, and ELM. The performance of MARS is better than ELM (R2 equals 0.97 and 0.5, respectively, in the testing phase); however, hybridization greatly enhances the performance of the ELM and the hybrid models perform better than MARS. The paper concludes that AI-based models are robust and hybridization of regression models with optimization techniques should be encouraged in further research. Sensitivity analysis suggests that all the input parameters have a significant influence on the output, with friction angle being the highest.

Reliability Analysis of Gravity Retaining Wall Using Hybrid ANFIS

Abstract: Gravity retaining walls are a vital structure in the area of geotechnical engineering, and academicians in earlier studies have conveyed substantial uncertainties involved in calculating the factor of safety against overturning, using a deterministic approach. Hence, to enhance the accuracy and eliminate the uncertainties involved, artificial intelligence (AI) was used in the present research. The main aim of this study is to propose a high-performance machine learning (ML) model to determine the factor of safety (FOS) of gravity retaining walls against overturning. The projected methodology included a novel hybrid machine learning model that merged with an adaptive neuro-fuzzy inference system (ANFIS) and meta-heuristic optimization techniques (particle swarm optimization (PSO), genetic algorithm (GA), firefly algorithm (FFA) and grey wolf optimization (GWO)). In this research, four hybrid models, namely ANFIS-PSO, ANFIS-FFA, ANFIS-GA and ANFIS-GWO, were created to estimate the factor of safety against overturning. The proposed hybrid models were evaluated on two distinct datasets (training 70% and testing 30%) with three input combinations, namely cohesion (c), unit weight of soil (Υ) and angle of shearing resistance (φ). To access the prediction power of different hybrid models, various statistical parameters such as R2, AdjR2, VAF, WI, LMI, a-20 index, PI, KGE, RMSE, SI, MAE, NMBE and MBE were computed for training (TR) and testing (TS) datasets. The overall performance of the models indicated that ANFIS-PSO provided better results among all four models. The reliability index was computed using the first-order second-moment (FOSM) method for all models, and the probability of failure was also computed. A Williams plot was drawn to check the applicability domain of the hybrid model and to check the influence of different input parameters on the prediction of the factor of safety, and the Gini index was also computed.

Evaluation of the Compressive Strength of CFRP-Wrapped Circular Concrete Columns Using Artificial Intelligence Techniques

Abstract: The wrapping of concrete structures with fiber polymers has been an essential part of concrete technology aimed at the improvement of concrete performance indices during the construction and lifelong usage of the structures. In this paper, a universal representative database was collected from multiple literature materials on the effect of different fiber-reinforced polymers on the confined compressive strength of wrapped concrete columns (Fcc). The collected data show that the Fcc value depends on the FRP thickness (t), tensile strength (Ftf), and elastic modulus (Ef), in addition to the column diameter (d) and the confined compressive strength of concrete (Fco). Five AI techniques were applied on the collected database, namely genetic programming (GP), three artificial neural networks (ANN) trained using three different algorithms, “back Propagation BP, gradually reduced gradient GRG and genetic algorithm GA”, and evolutionary polynomial regression (EPR). The results of the five developed predictive models show that (t) and Ftf have a major impact on the Fcc value, which presents the effect of confinement stress (t. Ftf/d) on the confined compressive strength (Fcc). Comparing the predicted values with the experimental ones showed that the GP model is the least accurate one, and the EPR model is the next least accurate, while the three ANN models have almost the same level of high accuracy, with an average error percentage of 5.8% and a coefficient of determination R2 of 0.961. The ANN model is more accurate than the EPR and GP predictive models, but they are suitable for manual calculation because they are closed-form equations.

Modeling the confined compressive strength of CFRP-jacketed noncircular concrete columns using artificial intelligence techniques

Abstract: Abstract In this paper, an extensive literature search has been employed to extract multiple data on the confined compressive strength of carbon fiber reinforced polymer (CFRP) concrete columns with noncircular cross-sections. The values collected are related to width (b), length (h), radius of corner (r), thickness of fiber (t), unconfined concrete strength (f’co), tensile strength of fiber (ftf), elastic modulus of fiber (Ef) and the confined strength of the CFRP-jacketed concrete columns (f’cc). The database was used to propose predictive models by artificial neural network (ANN-BP, -GA & -GRG), genetic programming (GP) and the evolutionary polynomial regression (EPR) techniques. The sum of squares errors (SSE), root mean square errors (RMSE) and coefficient determination (R2) performance indices were used to evaluate the performance accuracy and efficiency of the models. At the end of the exercise, the GP and EPR produced closed form equation with performance indices of 0.623 (28%) and 0.815 (20.9%), respectively, and these did not come close to the performance of ANN-BP, -GRG and GA which performed in that order with 0.967 (9.4%), 0.960 (10.3%) and 0.957 (10.6%), respectively. Last, the relative importance of the parameters conducted showed that f’co has the greatest influence on the f’cc of the concrete structure.