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View article: Survival Prediction in Allogeneic Haematopoietic Stem Cell Transplant Recipients Using Pre‐ and Post‐Transplant Factors and Computational Intelligence
Survival Prediction in Allogeneic Haematopoietic Stem Cell Transplant Recipients Using Pre‐ and Post‐Transplant Factors and Computational Intelligence Open
Advancements in artificial intelligence (AI) predictive models have emerged as valuable tools for predicting survival outcomes in allogeneic haematopoietic stem cell transplantation (allo‐HSCT). These models primarily focus on pre‐transpla…
Real‐time monitoring of tunnel structures using digital twin and artificial intelligence: A short overview Open
Tunnels are essential components of contemporary infrastructure, yet guaranteeing their safety, longevity, and efficiency remains a persistent challenge. Recent breakthroughs in artificial intelligence (AI) and digital twin (DT) technology…
A comparative study between LSSVM, LSTM, and ANN in predicting the unconfined compressive strength of virgin fine-grained soil Open
The present investigation introduces a robust soft computing model by comparing twelve least square support vector machine (LSSVM), six long short-term memory (LSTM), and thirty-six artificial neural network (ANN) models to predict the unc…
AI-Powered Simulation Models for Estimating the Consolidation Settlement of Shallow Foundations Open
The shallow foundations are one of the commonly used, cost-effective and versatile substructure in the infrastructure and geotechnical society. The consolidation settlement is one of the influential parameters for the design purpose of the…
Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites Open
This paper explores advanced machine learning approaches to enhance the prediction accuracy of compressive strength (CoS) in geopolymer composites (GePC). Geopolymers, as sustainable alternatives to Ordinary Portland Cement (OPC), offer si…
Optimized neural network-based state-of-the-art soft computing models for the bearing capacity of strip footings subjected to inclined loading Open
Determining the bearing capacity of a strip footing under inclined loading is crucial in designing foundations. Due to the complex correlations, the subject area remains predominantly unexplored, or it has been simulated using only limited…
Design and Reliability analysis of energy pile using soft computing technique and a comparative study between the developed soft compu-ting models Open
Geothermal or energy piles, are environmentally friendly piles that extract heat energy from shallow depths of the earth surface to heat or cool the structures constructed over them, such as multi-storey residential buildings, industrial c…
Liquefaction susceptibility using machine learning based on SPT data Open
Assessing the potential for liquefaction using traditional experimental or empirical analysis procedures is both time-consuming and arduous. Employing a machine learning model that can accurately predict liquefaction potential for a specif…
Soft-Computing Techniques for Predicting Seismic Bearing Capacity of Strip Footings in Slopes Open
In this study, various machine learning algorithms, including the minimax probability machine regression (MPMR), functional network (FN), convolutional neural network (CNN), recurrent neural network (RNN), and group method of data handling…
Hateful Sentiment Detection in Real-Time Tweets: An LSTM-Based Comparative Approach Open
It is undeniable that social media has improved our lives in many ways, like allowing interactions with others all over the world and network expansion for businesses. However, there are detrimental effects of such accessibility, including…
Novel Deep Learning Approaches for Mapping Variation of Ground Level from Spirit Level Measurements Open
This study investigates the use of new machine learning techniques in mapping variation in ground levels based on ordinary spirit levelling (SL) measurements. Convolution Neural Network (CNN), Recurrent Neural Networks (RNN), Long Short-Te…
View article: Reliability analysis of reinforced soil slope stability using GA-ANFIS, RFC, and GMDH soft computing techniques
Reliability analysis of reinforced soil slope stability using GA-ANFIS, RFC, and GMDH soft computing techniques Open
Soil is a heterogeneous medium, the characteristics that determine soil slope stability are highly variable, making the analysis a difficult task. The present research approach is switching from deterministic to probabilistic in order to a…
Bearing Capacity of Eccentrically Loaded Footings on Rock Masses Using Soft Computing Techniques Open
The proposed XGBoost model outperforms other models, such as RF, DNN, and LSTM, and can be used accurately for estimating a strip footing's bearing capacity on rock mass subjected to incline and eccentric loading loads.
Design of an Energy Pile Based on CPT Data Using Soft Computing Techniques Open
The present study focused on the design of geothermal energy piles based on cone penetration test (CPT) data, which was obtained from the Perniö test site in Finland. The geothermal piles are heat-capacity systems that provide both a suppl…
Determination of the size of rock fragments using RVM, GPR, and MPMR Open
For predicting the size of rock fragments during drilling and blasting operations, this article uses GPR, RVM, and MPMR. The current analysis makes use of a blast data set generated in a prior investigation. In this study, a portion of the…
Evaluation of the Compressive Strength of CFRP-Wrapped Circular Concrete Columns Using Artificial Intelligence Techniques Open
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,…
Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models Open
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 eff…
Modeling the confined compressive strength of CFRP-jacketed noncircular concrete columns using artificial intelligence techniques Open
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 …
Application of Artificial Intelligence Techniques in Slope Stability Analysis Open
Artificial intelligence (AI) techniques have become a trusted methodology among researchers in the recent decades for handling a variety of geotechnical and geological problems. Machine learning (ML) algorithms are distinguished by their s…
Stress intensity factor prediction on offshore pipelines using surrogate modeling techniques Open
This study aims to predict the accurate stress intensity factor (SIF) of a crack propagating in offshore piping, which is one of the crucial factors used to assess the remaining fatigue life (RFL) of offshore pipelines. Four soft computing…
A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique Open
In any construction projects,assessment of liquefaction potential induced due to seismic excitation during earthquake is a critical concern.The objective of present model development is to classify and assess liquefaction potential of soil…
Hybrid ensemble soft computing approach for predicting penetration rate of tunnel boring machine in a rock environment Open
This study implements a hybrid ensemble machine learning method for forecasting the rate of penetration (ROP) of tunnel boring machine (TBM), which is becoming a prerequisite for reliable cost assessment and project scheduling in tunnellin…
Rock Strain Prediction Using Deep Neural Network and Hybrid Models of ANFIS and Meta-Heuristic Optimization Algorithms Open
The majority of natural ground vibrations are caused by the release of strain energy accumulated in the rock strata. The strain reacts to the formation of crack patterns and rock stratum failure. Rock strain prediction is one of the signif…
Predicting Probability of Liquefaction Susceptibility Based on a Wide Range of CPT Data Open
In the present study, three efficient soft computing techniques (namely, genetic programming (GP), relevance vector machine (RVM), and multivariate regression splines (MARS)) are utilized to predict the probabilistic liquefaction susceptib…
Probabilistic Design of Retaining Wall Using Machine Learning Methods Open
Retaining walls are geostructures providing permanent lateral support to vertical slopes of soil, and it is essential to analyze the failure probability of such a structure. To keep the importance of geotechnics on par with the advancement…
Reliability Analysis of Pile Foundation Using Soft Computing Techniques: A Comparative Study Open
Uncertainty and variability are inherent to pile design and consequently, there have been considerable researches in quantifying the reliability or probability of failure of structures. This paper aims at examining and comparing the applic…