Multi-gene Genetic Programming Approach Research Articles

Determination of a pedotransfer function for specific air–water interfacial area in sandy soils: A pore network‐informed multigene genetic programming approach

AbstractUnderstanding specific air–water interfacial area (SAWIA) is essential for characterizing and modeling various phenomena in vadose zone hydrology, such as virus and colloid transport, contaminant dissolution, evaporation, and the hydro‐mechanical behavior of unsaturated soils. Traditional measurement methods, including X‐ray imaging and tracer techniques, often encounter challenges, leading to a scarcity of studies that provide a reliable relationship for SAWIA. Currently, no pedotransfer function in the literature links SAWIA with saturation and suction using readily measurable soil properties such as median grain size and porosity. In this study, we initially developed a pore network model capable of predicting SAWIA by calibrating it with corresponding soil‐water retention curves (SWRCs). We then used these models to compile a comprehensive database of SAWIA for six sandy soils, for which experimental SWRCs were available, covering a range of median grain sizes and porosities. Utilizing this database, we established a pedotransfer function through multigene genetic programming. The accuracy of this function was validated against experimental data not previously used in its training and testing. Our parametric study indicated that increases in either porosity or median particle size led to a decrease in the regions exhibiting higher SAWIA in terms of saturation and suction.

Multigene genetic programming approach for modelling and optimisation of removal of heavy metals from ash pond water using cyanobacterial-microalgal consortium

ABSTRACT Heavy metals such as Lead(II), Nickel(II), Manganese(II), Cadmium(II), Chromium(VI), and others are leached from coal ash in thermal power plants, contaminating ash pond water and subsequently contaminating groundwater. Phycoremediation using microalgae or cyanobacteria is an emerging technology to remove such heavy metals from ash pond water. The present study aims at development of an accurate data-driven Genetic Programing (GP) approach for modelling of the phycoremediation process for abatement of the above-mentioned heavy metals using a consortium comprising of a cyanobacterium Synechococcus sp. and green algae Chlorella sp. The developed model was used to find a relation between the average percentage removal of metals with all input parameters such as the initial metal concentrations, pH, and days. To maximise metal removal, Genetic Algorithm (GA) optimisation technique was applied to determine optimal values of input parameters. These optimum input parameters are difficult to get through experimentation using the trial and error method. The established modelling and optimisation technique is generic and can be applied to any other experimental study.

Prediction of tapered steel plate girders shear strength using multigene genetic programming

Tapered steel plate girders have been widely used in the construction of bridges and heavy industry structures. However, predicting their structural behavior, particularly in shear, is challenging due to their non-prismatic sections and ill-understood influencing factors. Therefore, simplified analytical and classic regression techniques may not be able to capture the underlying nonlinear relationships controlling the shear behavior of tapered plate girders. In this study, multigene genetic programming (MGGP) was utilized to explore such complex relationships, and subsequently develop robust predictive expressions for the tapered steel plate girders shear strength. Attributed to the lack of a large experimental dataset, a nonlinear finite element model (FEM) was first developed and validated against available experimental results in literature. The FEM was subsequently employed to generate a matrix of 211 numerical results to augment 200 more FEM results compiled from previous studies, to cover a wider range of design parameters. The entire dataset was then used in the training and testing of the MGGP predictive expressions. The prediction accuracy of the developed expressions was evaluated against that of other existing expressions. The results showed that the adopted MGGP approach, guided be mechanics understanding, produced elegant predictive expressions with high level of accuracy and generalizability compared to other existing ones examined herein. As such, the developed expressions present an efficient prediction tool that can be adopted by design standards for estimating the ultimate shear strength of tapered steel girders. Finally, reliability analysis is performed on the developed expressions to introduce strength reduction factors in order to satisfy target design conservatism.

Backbone model for reinforced concrete block shear wall components and systems using controlled multigene genetic programming

Reinforced concrete block shear walls (RCBSWs)have been used as an effective seismic force resisting system in low- and medium-rise buildings for many decades. However, attributed to their complex nonlinear behavior and the composite nature of their constituent materials, accurate prediction of their seismic performance, relying solely on mechanics, has been challenging. This study adopts multi-gene genetic programming (MGGP)— a class of bio-inspired artificial intelligence, to uncover the complexity of RCBSW behaviors and develop simplified procedures for predicting the full backbone curve of flexure-dominated RCBSWs under cyclic loading. A piecewise linear backbone curve was developed using five secant stiffness expressions associated with: cracking; yielding; 80% ultimate; ultimate; and 20% strength degradation (i.e., post-peak stage) derived through mechanics-controlled MGGP. Based on the experimental results of large-scale cyclically loaded fully-grouted RCBSWs, compiled from previously reported studies, a variable selection procedure was performed to identify the most influential variable subset governing wall behaviors. Subsequently, the MGGP stiffness expressions were trained and tested, and their accuracy was compared to that of existing models employing various statistical measures. In addition, the predictability of the developed backbone model was assessed at the system-level against experimental results of two two-story buildings available in the literature. This study demonstrates the power of the MGGP approach in addressing the complexity of the cyclic behavior of RCBSWs at both component- and system-level—offering an efficient prediction tool that can be adopted by relevant seismic design standards pertaining to RCBSW buildings.

Prediction of the degree of steel corrosion damage in reinforced concrete using field-based data by multi-gene genetic programming approach

Prediction of the degree of steel corrosion damage in reinforced concrete using field-based data by multi-gene genetic programming approach

Multi-gene genetic programming expressions for simulating solute transport in fractures

In lieu of process-based models, evolutionary artificial intelligence techniques can yield accurate expressions describing complex phenomena. In the current study, closed-form expressions are developed to predict solute transport in a fracture-matrix system as a function of the parameters that describe relevant physical and chemical processes. The study adopts a multi-gene genetic programming approach to approximate a solution of the classical advection–dispersion equation for reactive transport in single, parallel-plate fractures. The approach is employed to obtain an accurate relationship between the hydraulic, geological, and chemical parameters of the fracture-matrix system as inputs and an ensemble of breakthrough curves as outputs. Solutions generated by the developed model showed good agreement with those of corresponding analytical and numerical models. Computationally, the developed approach is highly efficient, particularly when compared with the analytical solution, which typically requires relatively fine discretization to calculate the long-tailed breakthrough curves. Therefore, future work could extend the developed model to simulate field-scale networks and include additional and more complex transport phenomena. This approach advances solute transport behavior predictions through being simpler and computationally more efficient than currently adopted techniques, which is important as the scale of simulation increases from that of a single fracture to a network.

Prediction of Structural and Magnetic Properties of Nanocrystalline Mechanically Alloyed Fe-Ni Powders Using Multi-Gene Genetic Programming Approach

In this paper, a theoretical model based on multi-gene genetic programming (MGGP) approach has been applied to predict the structural and magnetic properties in nanocrystalline Fe–Ni powders prepared by mechanical alloying (MA) using a planetary ball mill. The MGGP model was used to correlate the input parameters (milling speed, chemical composition, and milling time), to output parameters (crystallite size and coercivity) of nanocrystalline Fe–Ni powders. The model obtained was tested with additional data to demonstrate its performance and prediction ability. The MGGP model is a robust and efficient method to find an accurate mathematical relationship between input and output data. A sensitivity analysis study was applied to determine the most influential milling parameters on the crystallite size and coercivity.

Prediction of low-temperature fracture resistance curves of unmodified and crumb rubber modified hot mix asphalt mixtures using a machine learning approach

Fracture resistance curves (R-curves) provide a robust tool for a comprehensive insight into the crack propagation regime in engineering materials. In this paper, an extensive research program is conducted to determine R-curves for hot mix asphalt (HMA) mixtures with varying properties. The experimental results are then used to develop R-curve prediction models following a machine learning approach. Three-point single-edge notched beam (SE(B)) experiments were conducted on HMA mixtures incorporating 0%, 5%, 10%, 15%, and 20% crumb rubber at low temperatures. The temperature ranged from + 5 °C to −20 °C while limestone and siliceous aggregate with two gradations were used in developing mixtures with two base bitumen having performance grades of PG58-22 and PG64-22. It was observed that as the temperature is declined to −20 °C, the stable crack growth region is significantly diminished in the R-curves, and the mixtures undergo a brittle fracture with abrupt failure of the specimen. A temperature of −15 °C could be determined where the transition from quasi-brittle to brittle fracture occurs. Mixtures fabricated incorporating 20% crumb rubber exhibited a progressively rising R-curve at the lowest test temperature (−20 °C) even in the unstable crack propagation phase, which is a desirable material characteristic. Two prediction models were developed for R-curves. Artificial neural networks (ANN) were used in the first model resulting in an R-square value of 0.965. Due to the black-box nature of the ANN, the multi-gene genetic programming approach was also applied in the prediction of the R-curves to derive a mathematical equation between the input data and the outputs. The R-square equaled 0.870 in this method. R-curves could successfully be predicted by both methods considering the negligible to fair errors.

Solar radiation prediction using multi-gene genetic programming approach

Accurate estimation of solar radiation both spatially and temporally is important for engineering studies related to climate and energy. The multi-gene genetic programming (MGGP) is proposed as a new compact method for this purpose, which is verified to yield more accurate solar radiation estimations in Turkey. Meteorological data such as extraterrestrial solar radiation, sunshine duration, mean of monthly maximum sunny hours, long-term mean of monthly maximum air temperatures, long-term mean of monthly minimum air temperatures, monthly mean air temperature, and monthly mean moisture data are selected as the MGGP model inputs. In the prediction models, the meteorological data measured from 163 stations in seven climate areas of Turkey over the period 1975–2015 are used. The MGGP model results for solar radiation prediction are found to be more accurate than the values given by some conventional empirical equations such as Abdalla, Angstrom, and Hargreaves–Samani. The performance of MGGP is also assessed for Turkey by single-data and multi-data models. The multi-data models of MGGP and the calibrated empirical equations are found to be more successful than the single-data models for solar radiation prediction.

Hydrodynamics Modeling of an LSCFB Reactor Using Multigene Genetic Programming Approach: Effect of Particles Size and Shape

AbstractThe multigene genetic programming (MGGP) technique based hydrodynamics models were developed to predict the solids holdups of a liquid-solid circulating fluidized bed (LSCFB) riser. Four different particles were considered to investigate the effects of particle size, shape and density on hydrodynamics behavior of the LSCFB riser. In this regard, two spherical shape glass bead particles (500 and 1200 μm), two irregular shape lava rock particles (500 and 920 μm) were employed as solid phase and water as liquid phase. The MGGP models were developed, relating the solids holdup (${\varepsilon _s}$, output parameter) with eight input parameters. The developed models were first validated by comparing the model predicted and experimental data of solids holdups. The average solids holdups decreased with the increase of net superficial liquid velocity (${U_l} - {U_t}$) and normalized superficial liquid velocity$\left( {\frac{{{U_l}}}{{{U_t}}}} \right)$. Uniform axial solids holdups observed in axial locations (H) except close to the liquid-solid distributor of the riser. The radial non-uniformity of solids holdup observed all radial positions (r/R). In the central region almost flat but increased toward the wall region. The radial profiles of the solid holdup are approximately identical at a fixed average cross-sectional solid holdup for all of the three LSCFB systems of this study. The statistical performance indicators such as the mean absolute percentage error and correlation coefficient are also found to be within acceptable range. All these findings of suggest that the MGGP modeling approach is suitable for predicting effect of particle size and shape on hydrodynamics behavior of the LSCFB system

Low-frequency oscillation damping in the electric network through the optimal design of UPFC coordinated PSS employing MGGP

The coordination of flexible AC transmission systems (FACTS) with power system stabilizers (PSS) can significantly enhance the overall network stability by damping out the low-frequency oscillations (LFO). This paper proposes a multi-gene genetic programming (MGGP) approach to optimize PSS parameters coordinated with unified power flow controller (UPFC) to enhance power system stability by damping out LFO. The obtained results (minimum damping ratio, eigenvalues and time domain simulations) of the proposed MGGP approach are compared with the results of the conventional fixed gain model, the single-gene genetic programming (SGGP) approach and the referenced work to inquire the efficacy of the proposed approach for various operating conditions of the considered electric network. Besides, the acceptable values of standard statistical performance measures in estimating UPFC-PSS parameters provide confidence on the evolved MGGP models. Furthermore, the proposed approach requires a minimal time (∼less than a cycle) to estimate the key parameters that signal the real-time application of the proposed technique.

Experimental and theoretical study of gas/oil relative permeability

Gas injection is a proven economical process that significantly increases oil and condensate recovery from hydrocarbon reservoirs. Relative permeability is an important parameter in modeling, simulation, and evaluation of gas injection. In this study, new empirical-mathematical models are developed to predict the relative permeability of gas-oil systems in different rock and fluid systems. The Multi-Gene Genetic Programming (MGGP) technique is employed to develop relative permeability equations. The smart correlations presented in this work require capillary number, interfacial tension, API, gas molecular weight, and viscosity ratio as extra input variables to improve its precision and accuracy. The significance of each parameter is evaluated by statistical approach and Taguchi experimental design. The new models are evaluated by experimental data extracted from literature and validated by extensive error analysis. Additionally, laboratory tests were performed for determining gas-oil relative permeability which were used to check the accuracy of new developed correlations. In relative permeability curves for different gas flow rates, the coupling effect becomes strong and, in competition with the inertia effect, causes changes in the relative permeability with flow rate. The comparison of the experimental results of this study with the new proposed correlation resulted in a root-mean-square error (RMSE) of 0.0796 for gas relative permeability and 0.0564 for oil relative permeability. The results strongly admit the MGGP approach and high accuracy of developed mathematical correlations.

A Multigene Genetic Programming approach for modeling effect of particle size in a liquid–solid circulating fluidized bed reactor

This communication presents the application of Multigene Genetic Programming, a new soft computing technique to investigate the effects of particle size on hydrodynamics behavior of a liquid–solid circulating fluidized bed (LSCFB) riser. The Multigene Genetic Programming based model is developed/trained based on experimental data collected from a pilot scale LSCFB reactor using two different size glass beads (500 & 1200μm) as solid phase and water as liquid phase. The trained Genetic Programming model successfully predicted experimental phase holdups of the LSCFB riser under different operating parameters. It is observed that the model predicted cross-sectional average of solids holdups in the axial directions and radial flow structure are well agreement with the experimental values. The statistical performance indicators including the mean absolute error (∼5.89%) and the correlation coefficient (∼0.982) also show favorable indications of the suitability of Genetic Programming modeling approach in predicting the solids holdup of the LSCFB system.

Developing a bio-inspired multi-gene genetic programming based intelligent estimator to reduce speckle noise from ultrasound images

The speckle noise commonly occurs in ultrasound imaging based applications. Due to the multiplicative nature, speckle noise deteriorates the visual quality of ultrasound images. This affects the performance of radiologists and practitioners for disease diagnosis and/or patient treatment. The current study proposes a bio-inspired multi-gene genetic programming (MGGP) based intelligent estimator to reduce the speckle noise from ultrasound images. The proposed MGGP approach is based on the parallel framework of multiple genes and has effectively utilized the evolutionary learning capabilities to develop an intelligent estimator, by exploiting the useful statistical features extracted from local neighboring pixels. The performance of the proposed novel approach is evaluated on ultrasound images of common carotid artery corrupted with different noise levels. Further, the robust performance was validated on several diverse types of ultrasound images of Breast Cyst, Kidney Cancer, Liver, Liver Cyst, and Fetal Head. The proposed bio-inspired approach showed superior denoising performance over existing approaches. The proposed intelligent estimator is capable of removing speckle noise effectively while preserving the fine lines and edges. During evolution, the MGGP framework automatically selects the useful statistical features and primitive functions from a wider solution space to develop the intelligent estimator. Further, the proposed approach does not require image-dependent optimal threshold values, as conventional speckle denoising approaches required.

Compressive strength analysis of soil reinforced with fiber extracted from water hyacinth

PurposeThe purpose of this study is to measure the effects of density, moisture, fiber content on unconfined compressive strength (UCS) of soil by formulating the models based on evolutionary approach and artificial neural networks (ANN).Design/methodology/approachThe present work proposes evolutionary approach of multi-gene genetic programming (MGGP) to formulate the functional relationships between UCS of reinforced soil and four inputs (soil moisture, soil density, fiber content and unreinforced soil strength) of the silty sand. The hidden non-linear relationships between UCS of reinforced soil and the four inputs are determined by sensitivity and parametric analysis of the MGGP model.FindingsThe performance of MGGP is compared to those of ANN and the statistical analysis indicates that the MGGP model is the best and is able to generalize the UCS of reinforced soil satisfactorily beyond the given input range.Research limitations/implicationsThe explicit MGGP model will be useful to provide optimum input values for design and analysis of various geotechnical infrastructures. In addition, utilization of Water hyacinth reinforced fiber reinforced soil will minimize negative impact of this species on environment and may generate rural employment.Originality/valueThis work is first of its kind in application and development of explicit holistic model for evaluating the compressive strength of heterogeneous soil blinded with fiber content. This includes the experimental and cross-validation for testing robustness of the model.

Experimental- and numerical-based studies for magnetically impelled arc butt welding of T11 chromium alloy tubes

Magnetically impelled arc butt (MIAB) welding is a pressure welding process used for joining of pipes and tubes with an external magnetic field affecting arc rotation along the tube circumference. Past studies based on experiments and finite element analysis (FEA) suggested that the weld quality can be significantly improved by proper adjustment of its input parameters such as current, voltage, flux density, etc. Experiments are useful for understanding the physics behind the process, but the quantification and optimization of the MIAB process needs a thorough numerical investigation. Therefore, the present work presents a combined experimental and numerical study for investigating the arc speed indicator of the T11 grade chromium alloy tubes of the MIAB process. Firstly, experiments on the MIAB process are conducted, and the arc speed is measured based on the four inputs (welding current, welding voltage, magnetic coil current, and voltage). This is followed by introduction of a numerical approach of multi-gene genetic programming (MGGP) to formulate a functional relationship between arc speed and the four design variables. The statistical error metrics based on the square of correlation coefficient, relative error (%), and root mean square were used to validate the model performance against the experimental data. The proposed model can be utilized to select the best combination of input variables to optimize the arc speed, which has a vital role in governing the weld quality. In addition, 2-D analysis conducted reveals that the welding current influence the arc speed of the clay the most followed by coil current and coil voltage.

Dew point pressure model for gas condensate reservoirs based on multi-gene genetic programming approach

One of the most critical parameters in characterization of gas condensate reservoirs is dew point pressure (DPP), and its accurate determination is a challenging task in development and management of these reservoirs. Experimental measurement of DPP is a costly and time consuming method. Therefore, searching for a quick, reliable, inexpensive, and robust algorithm for determination of DPP is of great importance. In this paper, first, a new approach based on multi-gene genetic programming (MGGP) to determine DPP of gas condensate reservoirs is presented. Then, a correlation for DPP calculation using MGGP has been developed for gas condensate reservoirs. Finally, the efficiency of the proposed DPP model has been validated by comparing its predictions with the results of other conventional models. It is found that the correlation developed in this work is capable of predicting more accurate values of DPP, with the lowest average relative and absolute errors with respect to the experimental results, and also higher correlation coefficient among the results of all the evaluated DPP correlations. Therefore, it is suggested that the proposed model can be applied effectively for DPP prediction for a wide range of gas properties and reservoir temperatures.

A Bio-inspired Parallel-Framework Based Multi-gene Genetic Programming Approach to Denoise Biomedical Images

The occurrence of noise is a common problem in biomedical imaging applications. The denoising of corrupted biomedical images is a challenging task. In this paper, we present a biologically inspired parallel-framework based multi-gene genetic programming (MGGP) approach for denoising biomedical images from mixed impulse noise. Our biologically inspired approach has achieved an improved denoising performance by exploiting its parallel framework of multiple genes modeling capability in noise detection and removal stages. In the detection stage, we developed MGGP-based noise detector using rank-ordered and robust statistical features to effectively locate the corrupted pixels and generate noise map. In the noise removal stage, the detected noisy pixels are denoised by developing a bio-inspired MGGP-based estimator using statistical features of only noise-free pixels in their neighborhood. Extensive experimentation is carried out to demonstrate the robust performance of the proposed approach on diverse types of biomedical images corrupted with different noise densities. As a test case, we evaluated the performance of the proposed bio-inspired approach for benchmark biomedical images of Algae, C05c, Celulas, Crm04280, Crm05210, Nemacb1, Nemacl2, MRI, X-ray, Heart and microscopic images of fungal spores causing wheat rust. The proposed parallel-framework based bio-inspired approach has demonstrated an improved performance over other existing conventional and bio-inspired learning approaches.

Laser power based surface characteristics models for 3-D printing process

Selective laser melting (SLM) is one of the important 3-D Printing processes that builds components of complex 3D shapes directly from the metal powder. It is widely used in manufacturing industries and is operated on significant amount of laser power drawn from the electric grid. The literature reveals that the properties such as surface roughness, waviness, tensile strength and dimensional accuracy of an SLM fabricated parts, depend on the laser power and can be improved by its appropriate adjustment. Determination of accurate values of laser power and the other inputs could lead to an improvement in energy efficiency and thus contributing to a clean and healthy environment. For determining the accurate value of laser power in achieving the required surface characteristics, the formulation of generalized mathematical models is an essential pre-requisite. In this context, an artificial intelligence approach of multi-gene genetic programming (MGGP) which develops the functional expressions between the process parameters automatically can be applied. The present work introduces an ensemble-based-MGGP approach to model the SLM process. Experiments on the SLM process with measurement of surface characteristics, namely surface roughness and waviness, based on the variations of laser power and other inputs are conducted, and the proposed ensemble-based-MGGP approach is applied. Statistical evaluation concludes that the performance of the proposed approach is better than that of the standardized MGGP approach. Sensitivity and parametric analysis conducted reveals the hidden relationships between surface characteristics and the laser power, which can be used to optimize the SLM process both economically and environmentally.

Energy conservation in manufacturing operations: modelling the milling process by a new complexity-based evolutionary approach

From the perspective of energy conservation, the notion of modelling of energy consumption as a vital element of environmental sustainability in any manufacturing industry remains a current and important focus of study for climate change experts across the globe. Among the manufacturing operations, machining is widely performed. Extensive studies by peer researchers reveal that the focus was on modelling and optimizing the manufacturing aspects (e.g. surface roughness, tool wear rate, dimensional accuracy) of the machining operations by computational intelligence methods such as analysis of variance, grey relational analysis, Taguchi method, and artificial neural network. Alternatively, an evolutionary based multi-gene genetic programming approach can be applied but its effective functioning depends on the complexity measure chosen in its fitness function. This study proposes a new complexity-based multi-gene genetic programming approach based on orthogonal basis functions and compares its performance to that of the standardized multi-gene genetic programming in modelling of energy consumption of the milling process. The hidden relationships between the energy consumption and the input process parameters are unveiled by conducting sensitivity and parametric analysis. From these relationships, an optimum set of input settings can be obtained which will conserve greater amount of energy from these operations. It was found that the cutting speed has the highest impact on the milling process followed by feed rate and depth of cut.