Gene expression programming
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A Comparative Study of Random Forest and Genetic Engineering Programming for the Prediction of Compressive Strength of High Strength Concrete (HSC) Open
Supervised machine learning and its algorithm is an emerging trend for the prediction of mechanical properties of concrete. This study uses an ensemble random forest (RF) and gene expression programming (GEP) algorithm for the compressive …
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Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm Open
Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for…
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[Retracted] Improved Artificial Neural Network with State Order Dataset Estimation for Brain Cancer Cell Diagnosis Open
Brain cancer is one of the cell synthesis diseases. Brain cancer cells are analyzed for patient diagnosis. Due to this composite cell, the conceptual classifications differ from each and every brain cancer investigation. In the gene test, …
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Applications of Gene Expression Programming for Estimating Compressive Strength of High‐Strength Concrete Open
The experimental design of high‐strength concrete (HSC) requires deep analysis to get the target strength. In this study, machine learning approaches and artificial intelligence python‐based approaches have been utilized to predict the mec…
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Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model Open
In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settl…
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Compressive Strength Prediction via Gene Expression Programming (GEP) and Artificial Neural Network (ANN) for Concrete Containing RCA Open
To minimize the environmental risks and for sustainable development, the utilization of recycled aggregate (RA) is gaining popularity all over the world. The use of recycled coarse aggregate (RCA) in concrete is an effective way to minimiz…
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Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete Open
Compressive strength is one of the important property of concrete and depends on many factors. Most of the concrete compressive strength predictive models mainly rely on available literature data, which are too simple to consider all the c…
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Prediction of pavement roughness using a hybrid gene expression programming-neural network technique Open
Effective prediction of pavement performance is essential for transportation agencies to appropriately strategize maintenance, rehabilitation, and reconstruction of roads. One of the primary performance indicators is the international roug…
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Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches Open
Foamed concrete is special not only in terms of its unique properties, but also in terms of its challenging compositional mixture design, which necessitates multiple experimental trials before obtaining the desired property like compressiv…
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New Prediction Model for the Ultimate Axial Capacity of Concrete-Filled Steel Tubes: An Evolutionary Approach Open
The complication linked with the prediction of the ultimate capacity of concrete-filled steel tubes (CFST) short circular columns reveals a need for conducting an in-depth structural behavioral analyses of this member subjected to axial-lo…
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Estimating Daily Dew Point Temperature Using Machine Learning Algorithms Open
In the current study, the ability of three data-driven methods of Gene Expression Programming (GEP), M5 model tree (M5), and Support Vector Regression (SVR) were investigated in order to model and estimate the dew point temperature (DPT) a…
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Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials Open
The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, and decision …
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A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash Open
Artificial intelligence and machine learning are employed in creating functions for the prediction of self-compacting concrete (SCC) strength based on input variables proportion as cement replacement. SCC incorporating waste material has b…
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Prediction of maximum scour depth around piers with debris accumulation using EPR, MT, and GEP models Open
Pier scour phenomena in the presence of debris accumulation have attracted the attention of engineers to present a precise prediction of the local scour depth. Most experimental studies of pier scour depth with debris accumulation have bee…
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Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material Open
Structures located on the coast are subjected to the long-term influence of chloride ions, which cause the corrosion of steel reinforcements in concrete elements. This corrosion severely affects the performance of the elements and may shor…
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Prediction of Complex Stock Market Data Using an Improved Hybrid EMD-LSTM Model Open
Because of the complexity, nonlinearity, and volatility, stock market forecasting is either highly difficult or yields very unsatisfactory outcomes when utilizing traditional time series or machine learning techniques. To cope with this pr…
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Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete Open
For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to…
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A Gene Expression Programming Model for Predicting Tunnel Convergence Open
Underground spaces have become increasingly important in recent decades in metropolises. In this regard, the demand for the use of underground spaces and, consequently, the excavation of these spaces has increased significantly. Excavation…
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Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer Open
This study used three artificial intelligence-based algorithms – adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANNs), and gene expression programming (GEP) – to develop empirical models for predicting the compr…
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Runoff forecasting using hybrid Wavelet Gene Expression Programming (WGEP) approach Open
This study presents a novel approach of using the hybrid Wavelet Gene Expression Programming (WGEP) model to forecast the runoff using rainfall data. The rainfall-runoff data from four different catchments located in the different Hydro-Cl…
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Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches Open
To avoid time-consuming, costly, and laborious experimental tests that require skilled personnel, an effort has been made to formulate the depth of wear of fly-ash concrete using a comparative study of machine learning techniques, namely r…
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Predictive modeling for compressive strength of 3D printed fiber-reinforced concrete using machine learning algorithms Open
Three-dimensional (3D) printing in the construction industry is growing rapidly due to its inherent advantages, including intricate geometries, reduced waste, accelerated construction, cost-effectiveness, eco-friendliness, and improved saf…
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Predicting ultra-high-performance concrete compressive strength using gene expression programming method Open
There have been extensive experimental studies available on the composition and characteristics of Ultra-High-Performance concrete (UHPC). However, the relation between UHPC characteristics and mixture content, on the other hand, is extrem…
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Evaluation of Artificial Intelligence Methods to Estimate the Compressive Strength of Geopolymers Open
The depletion of natural resources and greenhouse gas emissions related to the manufacture and use of ordinary Portland cement (OPC) pose serious concerns to the environment and human life. The present research focuses on using alternative…
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Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate Open
Solar radiation, moisture and temperature are the most vital meteorological variables which affect plant growth. Due to the fact that the global solar radiation (GSR) is scarcely gauged at meteorological stations in developing countries, i…
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Traveling-Salesman-Problem Algorithm Based on Simulated Annealing and Gene-Expression Programming Open
The traveling-salesman problem can be regarded as an NP-hard problem. To better solve the best solution, many heuristic algorithms, such as simulated annealing, ant-colony optimization, tabu search, and genetic algorithm, were used. Howeve…
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Predictive Modeling Approach for Surface Water Quality: Development and Comparison of Machine Learning Models Open
Water pollution is an increasing global issue that societies are facing and is threating human health, ecosystem functions and agriculture production. The distinguished features of artificial intelligence (AI) based modeling can deliver a …
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Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality Open
The prediction accuracies of machine learning (ML) models may not only be dependent on the input parameters and training dataset, but also on whether an ensemble or individual learning model is selected. The present study is based on the c…
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Modelling Reservoir Turbidity Using Landsat 8 Satellite Imagery by Gene Expression Programming Open
This study aimed to develop a reliable turbidity model to assess reservoir turbidity based on Landsat-8 satellite imagery. Models were established by multiple linear regression (MLR) and gene-expression programming (GEP) algorithms. Totall…
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Municipal solid waste higher heating value prediction from ultimate analysis using multiple regression and genetic programming techniques Open
Municipal solid waste (MSW) management presents an important challenge for all countries. In order to exploit them as a source of energy, a knowledge of their calorific value is essential. In fact, it can be experimentally measured by an o…