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Quantitative structure-activity relationship and molecular docking studies on designing inhibitors of the perforin

CHEMICAL BIOLOGY & DRUG DESIGN 2017年第4期90卷 535-544页

作者： Song, Fucheng Cui, Lianhua Piao, Jinmei Liang, Hui Si, Hongzong Duan, Yunbo Zhai, Honglin Qingdao Univ Med Coll Dept Publ Hlth Qingdao Shandong Peoples R China Qingdao Univ Growing Base State Key Lab Lab New Fibrous Mat & Modern Text Inst Computat Sci & Engn Qingdao Shandong Peoples R China Lanzhou Univ Dept Chem Lanzhou Gansu Peoples R China

Quantitative structure-activity relationship (QSAR) studies were performed on a series of 5-arylidene-2thioxoimidazolidin-4-ones derivatives as the inhibitors of perforin and to gain insights about the structural determinants for designing new drug molecules. The heuristic method could explore the descriptors responsible for bioactivity and gain a best linear model with R-2 .82. gene expression programming method generated a novel nonlinear function model with R-2 .92 for training set and R-2 .85 for test set. The predicted IC50 by QSAR, molecular docking analysis, and property explorer applet show that 42a acts as a well-pleasing potent inhibitor for perforin. This study may lay a reliable theoretical foundation for the development of designing perforin inhibitor structures.

关键词： gene expression programming heuristic method molecular docking perforin inhibitor quantitative structure-activity relationship

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Rigorous silica solubility estimation in superheated steam: Smart modeling and comparative study

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ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY 2019年第4期38卷 13089-13089页

作者： Rostami, Alireza Shokrollahi, Amin Esmaeili-Jaghdan, Zohre Ghazanfari, Mohammad Hossein Islamic Azad Univ Ahwaz Branch Young Researchers & Elites Club Ahvaz Iran Sharif Univ Technol Chem & Petr Engn Dept Tehran Iran PUT Gas Engn Dept Ahvaz Iran

One of the main issues of wastewater treatment is the silica deposition in steam turbines. Evaporation of silica with the steam in adequate concentration is one of the main sources of scale formation in steam turbines. In this study, the authors introduce the utilization of a genetic-based approach-gene expression programming (GEP)-for solubility prognostication of the silica in superheated steam of boilers with respect to water silica content and pressure. The result of GEP mathematical approach is a new algebraic formula to achieve our goals. Developed model predicts the silica solubility in the range of 0.8-22.1 MPa and 1-500 mg/kg for pressure and boiler water silica content, respectively. The results show that the constructed model outperforms pre-existing method and provides acceptable predictions with average absolute relative deviation (AARD%) of 9.46% and determination coefficient (R-2) of 0.98. Outcomes of the performed sensitivity analysis reveal that the effect of pressure is more pronounced than the impact of the boiler water silica content on the estimation of the silica solubility. Moreover, an outlier analysis based on Leverage approach was performed confirming the validity of the used database in this study. Finally, it can be concluded that the recommended tool in this study can be a suitable candidate for prognosticating the silica scaling in industries dealing with steam. (c) 2018 American Institute of Chemical Engineers Environ Prog, 38:e13089, 2019

关键词： wastewater treatment silica solubility steam turbines gene expression programming

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Compressive strength prediction of eco-efficient GGBS-based geopolymer concrete using GEP method

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JOURNAL OF BUILDING ENGINEERING 2020年 31卷

作者： Shahmansouri, Amir Ali Bengar, Habib Akbarzadeh Ghanbari, Saeed Univ Mazandaran Dept Civil Engn Babol Sar Iran

Geopolymer concrete (GPC) could be used as an environmental-friendly alternative solution for concrete production due to the detrimental impacts of cement production on the environment. Since obtaining an optimal mix design, and subsequently, predicting the compressive strength of mortar using experimental means is costly and time-consuming, employing soft-computing techniques could facilitate and accelerate this approach. In this research, gene expression programming (GEP) was used to develop numerical models for predicting the compressive strength of GPC based on ground granulated blast-furnace slag (GGBS). Through an experimental program, an extensive database of the compressive strength of GGBS-based GPC consisting of 351 specimens obtained from 117 different mixtures was generated. The five most effective parameters, including specimen age, sodium hydroxide (NaOH) solution concentration, natural zeolite (NZ), silica fume (SF), and GGBS content, were considered as the modeling input parameters. Using GEP algorithm, simplified and practical mathematical equations were proposed to predict the compressive strength of GGBS-based GPC mortar. Performance, high accuracy, and predictability of the proposed equations were validated by the conducted sensitivity and parametric analyses. The obtained results could promote the re-use of GGBS for GPC development, which in turn will lead to environmental and economic advantages.

关键词： Geopolymer concrete Ground granulated blast-furnace slag Compressive strength Strength prediction gene expression programming

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Data-driven prediction method for flexural performance of ECC composite sandwich panels

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STRUCTURES 2024年 70卷

作者： Xiong, Feng Bian, Yu Liu, Ye Ge, Qi Deng, Chubing Sichuan Univ Coll Architecture & Environm Key Lab Deep Underground Sci & Engn Minist Educ Chengdu 610065 Peoples R China

To address the inadequate load-carrying capacity and ductility of precast concrete sandwich panels (PCSPs), as well as their inability to meet the current trend of lightweight and high-strength structures, this study proposes a novel engineered cementitious composite (ECC) composite sandwich panel (ESP) by applying ECC to the outer wythe. Existing design methods are unable to accurately quantify either the composite action between the inner and outer wythes or fully consider the complex design parameters when predicting the flexural performance of this new type of sandwich panel. In response, two normal concrete sandwich panels (NSPs) and two ESPs were first tested to reveal their flexural performance using a four-point bending test. Test results indicate that the application of ECC to the outer wythe can significantly improve the flexural performance of the sandwich panel, especially in terms of crack resistance and ductility. Subsequently, finite element parametric analysis demonstrated the impact of connector arrangement on the flexural performance, and design formulas for the degree of composite action (DCA) based on connector arrangement were proposed. Finally, a data-driven prediction model integrating experiments, finite element analysis, and neural networks was developed to accurately predict the flexural carrying capacity and ductility of the sandwich panel. By utilizing gene expression programming, the explicit function can be derived for the prediction model, which provides practical guidance for engineering applications.

关键词： Engineered cementitious composite (ECC) Sandwich panel Finite element analysis Data-driven Neural network gene expression programming

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Spectral acceleration prediction using genetic programming based approaches

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APPLIED SOFT COMPUTING 2021年 106卷 107326-107326页

作者： Gandomi, Mostafa Kashani, Ali R. Farhadi, Ali Akhani, Mohsen Gandomi, Amir H. Univ Tehran Dept Civil Engn Tehran Iran Univ Memphis Dept Civil Engn Memphis TN USA Univ Technol Sydney Fac Engn & IT Ultimo Australia

Evolutionary computation (EC) is a widely used computational intelligence that facilitates the formulation of a range of complex engineering problems. This study tackled two hybrid EC techniques based on genetic programming (GP) for ground motion prediction equations (GMPEs). The first method coupled regression analysis with multi-objective genetic programming. In this way, the strategy was maximizing the accuracy and minimizing the models' complexity simultaneously. The second approach incorporated mesh adaptive direct search (MADS) into gene expression programming to optimize the obtained coefficients. A big data set provided by the Pacific Earthquake Engineering Research Centre (PEER) was used for the model development. Two explicit formulations were developed during this effort. In those formulae, we correlated spectral acceleration to a set of seismological parameters, including the period of vibration, magnitude, the closest distance to the fault ruptured area, shear wave velocity averaged over the top 30 meters, and style of faulting. The GP-based models are verified by a comprehensive comparison with the most well-known methods for GMPEs. The results show that the proposed models are quite simple and straightforward. The high degrees of accuracy of the predictions are competitive with the NGA complex models. Correlations of the predicted data using GEP-MADs and MOGP-R models with the real observations seem to be better than those available in the literature. Three statistical measures for GMPEs, such as E (%), LLH, and EDR index, confirmed those observations. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Spectral acceleration Ground-motion models Multi-gene genetic programming gene expression programming Multi-objective genetic programming

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A genetic-based model to predict maximum lateral displacement of retaining wall in granular soil

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SCIENTIA IRANICA 2016年第1期23卷 54-65页

作者： Johari, A. Javadi, A. A. Najafi, H. Shiraz Univ Technol Dept Civil & Environm Engn Shiraz Iran Univ Exeter Coll Engn Math & Phys Sci Computat Geomech Grp Exeter Devon England Bushehr Grad Azad Univ Dept Civil Engn Bushehr Iran

Retaining walls are one of the most common geotechnical structures. Horizontal displacement at the top of the retaining wall is an important parameter in design of retaining structures because of serviceability of the wall and adjacent structures. In this research, the gene expression programming (GEP) is used for developing a model to predict this design parameter of retaining wall. The input parameters of the model consist of effective period of adjacent structure, horizontal and rocking stiffness of the foundation of adjacent structure, density, Young's modulus, and friction angle of granular soil as well as the thickness and height of retaining wall. The output of the model is maximum lateral displacement of retaining wall. A database including 240 cases, created from 3D finite element modeling of a soil-retaining wall with an adjacent steel structure modeled as surcharge, is employed to develop the model. Comparison of the GEP-based model predictions with the simulated data indicates a very good performance and ability of the developed models in predicting maximum lateral displacement of retaining walls. Sensitivity and parametric analyses are conducted to verify the results. It is shown that soil density is the most influential parameter in the maximum lateral displacement of retaining wall. (C) 2016 Sharif University of Technology. All rights reserved.

关键词： Retaining wall Lateral displacement gene expression programming Soil-structure interaction

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Water Quality Index Estimations Using Machine Learning Algorithms: A Case Study of Yazd-Ardakan Plain, Iran

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WATER 2023年第10期15卷 1876页

作者： Goodarzi, Mohammad Reza Niknam, Amir Reza R. Barzkar, Ali Niazkar, Majid Mehrjerdi, Yahia Zare Abedi, Mohammad Javad Pour, Mahnaz Heydari Yazd Univ Dept Civil Engn Yazd *** Iran Yazd Univ Dept Civil Engn Water Resources Management Engn Yazd *** Iran Free Univ Bozen Bolzano Fac Sci & Technol Piazza Univ 5 I-39100 Bolzano Italy Yazd Univ Dept Ind Engn Yazd *** Iran

Excessive population growth and high water demands have significantly increased water extractions from deep and semi-deep wells in the arid regions of Iran. This has negatively affected water quality in different areas. The Water Quality Index (WQI) is a suitable tool to assess such impacts. This study used WQI and the fuzzy hierarchical analysis process of the water quality index (FAHP-WQI) to investigate the water quality status of 96 deep agricultural wells in the Yazd-Ardakan Plain, Iran. Calculating the WQI is time-consuming, but estimating WQI is inevitable for water resources management. For this purpose, three Machine Learning (ML) algorithms, namely, gene expression programming (GEP), M5P Model tree, and Multivariate Adaptive Regression Splines (MARS), were employed to predict WQI. Using Wilcox and Schoeller charts, water quality was also investigated for agricultural and drinking purposes. The results demonstrated that 75% and 33% of the study area have good quality, based on the WQI and FAHP-WQI methods, respectively. According to the results of the Wilcox chart, around 37.25% of the wells are in the C3S2 and C3S1 classes, which indicate poor water quality. Schoeller's diagram placed the drinking water quality of the Yazd-Ardakan plain in acceptable, inadequate, and inappropriate categories. Afterwards, WQI, predicted by means of ML models, were compared on several statistical criteria. Finally, the comparative analysis revealed that MARS is slightly more accurate than the M5P model for estimating WQI.

关键词： water quality index machine learning fuzzy-AHP gene expression programming M5P MARS

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Accurate performance prediction model for impact hammer developed using customized evolutionary algorithm

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2021年 109卷 103773-103773页

作者： Hojjati, Shahabedin Tumac, Deniz Jeon, Seokwon Seoul Natl Univ Dept Energy Syst Engn 1 Gwanak Ro Seoul 08826 South Korea Istanbul Tech Univ Dept Min Engn TR-34469 Istanbul Turkey

In past decades, impact hammers have played a key role in underground construction. Simply in Istanbul, impact hammers have been used to excavate more than 20 km of metro tunnels. Thus, determining the instantaneous breaking rate (IBR) of an impact hammer is attracting increasing attention. A number of IBR prediction models have been developed for impact hammers. However, there is still a demand for models that require a smaller number of easy-to-obtain rock properties as inputs and provide a reasonable level of accuracy, a wide range of applications, and high reliability. This study had the goal of developing such a prediction model based on an investigation of two subway tunnels built in Istanbul. In order to enhance the results generated by multiple linear regression analysis, a customized tool was developed for the non-linear analysis of a relatively large set of data collected for the present research. gene expression programming (GEP) and particle swarm optimization (PSO) were merged to create a non-linear analysis tool. The GEP-PSO algorithm was trained using 80% of the available data, with the remaining 20% reserved to validate the results. The output of the algorithm was presented in the form of a mathematical equation that predicted the IBR using the uniaxial compressive strength, rock quality designation, Schmidt hammer rebound value, and machine power as input parameters. The predicted IBR values were in remarkable agreement with the recorded values. In order to verify the efficiency of the proposed prediction model, it was successfully tested against previously developed models for which input parameters were available. In addition, the proposed model was investigated under hypothetical circumstances to ensure that it legitimately described the performance changes due to changes in the input parameters. The model developed in this research is proposed as an accurate and reliable tool for predicting the performance of impact hammers over a wide applicati

关键词： Tunnel construction Impact hammer Performance prediction gene expression programming Particle swarm optimization Rock properties

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Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material

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MATERIALS 2021年第9期14卷 2297页

作者： Ahmad, Ayaz Farooq, Furqan Ostrowski, Krzysztof Adam Sliwa-Wieczorek, Klaudia Czarnecki, Slawomir COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Islamabad 22060 Pakistan Cracow Univ Technol Fac Civil Engn 24 Warszawska Str PL-31155 Krakow Poland Wroclaw Univ Sci & Technol Fac Civil Engn Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland

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 shorten the lifespan of an entire structure. Even though experimental activities in laboratories might be a solution, they may also be problematic due to time and costs. Thus, the application of individual machine learning (ML) techniques has been investigated to predict surface chloride concentrations (C-c) in marine structures. For this purpose, the values of C-c in tidal, splash, and submerged zones were collected from an extensive literature survey and incorporated into the article. gene expression programming (GEP), the decision tree (DT), and an artificial neural network (ANN) were used to predict the surface chloride concentrations, and the most accurate algorithm was then selected. The GEP model was the most accurate when compared to ANN and DT, which was confirmed by the high accuracy level of the K-fold cross-validation and linear correlation coefficient (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) parameters. As is shown in the article, the proposed method is an effective and accurate way to predict the surface chloride concentration without the inconveniences of laboratory tests.

关键词： surface chloride concentration individual algorithm aggressive ions environment gene expression programming concrete artificial neural networks

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Application of Soft Computing Techniques for Particle Froude Number Estimation in Sewer Pipes

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JOURNAL OF PIPELINE SYSTEMS ENGINEERING AND PRACTICE 2020年第2期11卷

作者： Mehr, Ali Danandeh Safari, Mir Jafar Sadegh Antalya Bilim Univ Dept Civil Engn TR-07190 Antalya Turkey Yasar Univ Dept Civil Engn TR-35100 Izmir Turkey

Sedimentation in sewer networks is a major problem in urban hydrology. In comparison to the well-known classic sediment transport models, this study investigates the capabilities of soft computing methods, including multigene genetic programming (MGGP), gene expression programming, and multilayer perceptron to derive accurate sewer design models. A wide range of experimental data sets comprising fluid, flow, sediment, and pipe features was used to develop new models under the nondeposition with a deposited bed self-cleansing condition. The results showed better performances of the new models compared to the conventional ones in terms of statistical performance indices. The proposed MGGP model was found superior to its counterparts. It is an explicit model motivated to be used for self-cleansing sewer pipes design in practice.

关键词： Bed load Sediment transport Sewer network Multigene genetic programming gene expression programming

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