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Assessment of a Tailings Dam Breach by Experimental, Numerical, and gene-expression programming Model

WATER RESOURCES MANAGEMENT 2025年 1-16页

作者： Eghbali, Arian Soltanabadi, Mehdi Javan, Mitra Mohseni, Omid Razi Univ Dept Civil Engn Univ Ave Kermanshah *** Iran Univ Minnesota Dept Civil Engn St Anthony Falls Lab 2 Third Ave SE Minneapolis MN 55414 USA

Empirical evidence from documented tailings dam failures highlights the severe consequences for economic systems, human lives, and ecological integrity. The spatial distribution and depositional configuration of tailings within the impoundment structure are regarded as the critical factors influencing the heterogeneous behavioral responses during failure events. This study uses experimental and numerical approaches to investigate the influence of a lateral slope of non-liquefied tailings on localized tailings dam breach mechanisms. The HEC-RAS 2D model was employed to simulate failure scenarios, with the numerical model calibrated against experimental data to evaluate flow characteristics and hydrograph profiles under conditions with and without a lateral slope. gene-expression programming (GEP) was successfully applied to predict flood hydrographs at the failure location based on the simulated data. Results indicate that erosion in the direction perpendicular to the dam is more pronounced in the presence of a lateral tailings slope compared to the scenario without a lateral slope. While a 2% lateral slope exerts minimal influence on the outflow hydrograph, it reduces tailings erosion from the reservoir by approximately 1.3 times in localized failure scenarios. The GEP-derived formula demonstrated high accuracy in computing the flood hydrograph, offering a reliable approach for predicting tailings dam breach-induced flooding.

关键词： Numerical Model Tailings Dam Failure Flood Hydrograph Tailings Slope Experiment Sediment gene-expression programming

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gene-expression programming-based models for comprehensive evaluation of bond strength and critical slip of various types of FRP bars in concrete

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STRUCTURAL CONCRETE 2025年第0期

作者： Nguyen, Minh Hai Pham, Van Ngoc Phan, Hoang Nam Do, Viet Hai Huynh, Phuong Nam Mai, Thi Thu Thuy Univ Sci & Technol Univ Danang Fac Rd & Bridge Engn Danang Vietnam PEDI Civil Solut JSC R&D Dept Danang Vietnam

This study aims to present a novel approach to assess the bond behavior of various types of FRP bars in concrete, based on an extensive dataset. The novelty of this research lies in evaluating both the bond strength and the critical slip, which forms the basis for creating a bond model between the bars and concrete. Furthermore, the model is constructed using advanced machine learning techniques, specifically gene-expression programming (GEP), and its reliability is ensured by relying on a more extensive and diverse experimental dataset compared to previous studies. To train and validate the GEP model, data were collected from experiments involving 793 test specimens, categorized into five groups based on surface treatments: smooth (Sm), sand-coated (SC), helically wrapped (HW), grooved (Gr), and a hybrid surface (Hbr) achieved by combining SC and HW. The model also considers a wide range of other parameters, including compressive strength of concrete and diameter, tensile strength, elastic modulus, and embedment length of FRP bars. The verification results using a separate testing dataset demonstrate that the proposed models accurately predict the bond strength and critical slip of FRP bars in concrete, achieving R2 values of 0.794 and 0.88, respectively. Comprehensive parametric analyses are performed to clarify the influence of key parameters on the bond and slip behavior of FRP bars in concrete. The analysis results confirm the critical role of the surface type and elastic modulus in enhancing bond strength;particularly, among the different surface treatment methods, the HW treatment exhibits the highest bond strength, followed by the HBr, Gr, and SC surface types.

关键词： bond strength critical slip FRP bars gene-expression programming machine learning pull-out tests surface treatments

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Estimating Vehicle Delay at Unsignalized Intersections with gene-expression programming

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Transportation Research Procedia 2025年 86卷 175-182页

作者： Sambit Kumar Beura K. Ramachandra Rao Post-Doctoral Fellow Department of Civil Engineering Indian Institute of Technology (IIT) Delhi Hauz Khas New Delhi 110016 Professor Department of Civil Engineering and Head Transportation Research and Injury Prevention Centre (TRIP-C) Indian Institute of Technology (IIT) Delhi Hauz Khas New Delhi 110016

The performance of an unsignalized intersection is typically evaluated based on the delay experienced by vehicles in low-priority movements. Several studies in the literature have examined delays at unsignalized intersections under homogeneous and lane-disciplined traffic conditions. However, it is challenging to find a standard delay model for mixed traffic conditions. To address this gap, the present study proposes mathematical models to estimate average traffic delays for the two least-priority movements: through and right-turning movements (for left-hand drive conditions) from minor streets. The required datasets for this investigation were obtained from thirty unsignalized intersections, encompassing both three-legged and four-legged configurations, with divided and undivided road intersection typologies collected from different parts of India. From data analysis, it was observed that the delay incurred by vehicles in through movements on the minor approach is significantly influenced by the degree of saturation (volume-to-capacity ratio), conflicting traffic volume, crossing pedestrian volume, and intersection crossing distance. Similarly, the delay incurred by vehicles in right-turning movements is significantly influenced by the same list of variables except for the intersection crossing distance. Subsequently, gene-expression programming (GEP), a novel variant of conventional genetic programming, was successfully utilized to develop delay models for the respective movements. The resultant models demonstrated high prediction accuracies, with coefficient of determination ( R 2 ) values between the predicted and observed delays surpassing 0.93. A comparative analysis corroborated the superior predictive capabilities of the developed models when compared to existing ones.

关键词： Delay Unsignalized intersection Heterogeneous traffic gene-expression programming

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gene-expression programming for flip-bucket spillway scour

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WATER SCIENCE AND TECHNOLOGY 2012年第11期65卷 1982-1987页

作者： Guven, Aytac Azamathulla, H. Md Univ Sains Malaysia REDAC George Town Malaysia Gaziantep Univ Dept Civil Engn Gaziantep Turkey

During the last two decades, researchers have noticed that the use of soft computing techniques as an alternative to conventional statistical methods based on controlled laboratory or field data, gave significantly better results. gene-expression programming (GEP), which is an extension to genetic programming (GP), has nowadays attracted the attention of researchers in prediction of hydraulic data. This study presents GEP as an alternative tool in the prediction of scour downstream of a flip-bucket spillway. Actual field measurements were used to develop GEP models. The proposed GEP models are compared with the earlier conventional GP results of others (Azamathulla et al. 2008b;RMSE = 2.347, delta = 0.377, R = 0.842) and those of commonly used regression-based formulae. The predictions of GEP models were observed to be in strictly good agreement with measured ones, and quite a bit better than conventional GP and the regression-based formulae. The results are tabulated in terms of statistical error measures (GEP1;RMSE = 1.596, delta = 0.109, R = 0.917) and illustrated via scatter plots.

关键词： flip-bucket spillway gene-expression programming genetic programming scouring

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gene-expression programming for transverse mixing coefficient

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JOURNAL OF HYDROLOGY 2012年 434卷 142-148页

作者： Azamathulla, H. Md. Ahmad, Z. Univ Sains Malaysia River Engn & Urban Drainage Res Ctr REDAC Nibong Tebal 14300 Pulau Pinang Malaysia Indian Inst Technol Roorkee Dept Civil Engn Roorkee 247667 Uttarakhand India

This study presents gene-expression programming (GEP), which is an extension of genetic programming (GP), as an alternative approach to predict the transverse mixing coefficient in open channel flows. Laboratory data were collected in the present study and also from the literature for the transverse mixing coefficient covering wide range of flow conditions. These data were used for the development and testing of the proposed method. A functional relation for the estimation of transverse mixing coefficient has been developed using GEP. The proposed GEP approach produced satisfactory results compared to the existing predictors for the transverse mixing coefficient. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Transverse mixing Open channel flow gene-expression programming River systems

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gene-expression programming for the assessment of surface mean pressure coefficient on building surfaces

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建筑模拟（英文版） 2020年第2期13卷 401-418页

作者： Monalisa Mallick Abinash Mohanta Awadhesh Kumar Kanhu Charan Patra Department of Civil Engineering National Institute of Technology Rourkela Rourkela Odisha 769008 India Department of Mechanical Engineering Vellore Institute of Technology Vellore Tamilnadu 632014 India

Wind surface mean pressure coefficient ((C)p) is an essential parameter for the assessment of windinduced forces that is a must input to all structural designs. An extensive experimentation is carried out to obtain pressure coefficient data over the surfaces of C-shaped building models of varying aspect ratio, corner curvature and angle of incidence in a sub-sonic wind tunnel. The studies also include models without corner curvature. In this study, a technique known as gene-expression programming (GEP) is used to develop a model equation using experimental values of pressure coefficient data collected at the grid points of the frontal surface under varying conditions. And this developed model is used to predict surface mean pressure coefficients ((C)p). The predictedvalues of (C)p using the developed model are compared with the corresponding (C)p values obtained by Swami and Chandra (S&C) equation and Muehleisen and Patrizi (M&P) equations. The prediction made by the developed GEP model is also validated with the actual building data of Tokyo Polytechnic University (TPU). The results signify the ability of the model to predict the (C)p valuesfor practical purposes. The error analysis of the results show that the predicted values of (C)p using developed GEP correlation are more close to the experimental values than those obtained by using other two methods.

关键词： C-shaped building models wind tunnel test surface mean pressure coefficient gene-expression programming error analysis

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PREDICTION OF SCOUR DEPTH AT CULVERT OUTLETS USING gene-expression programming

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2012年第7B期8卷 5045-5054页

作者： Azamathulla, H. Md Haque, A. A. M. Univ Sains Malaysia Sch Civil Engn Nibong Tebal 14300 Pulau Pinang Malaysia Univ Sains Malaysia River Engn & Urban Drainage Res Ctr Nibong Tebal 14300 Pulau Pinang Malaysia

The processes involved in local scour at culverts are so complex that make it difficult to establish a general empirical (regression) model to provide accurate estimation for scour. This study presents gene-expression programming (GEP), which is an extension to genetic programming (GP), as an alternative approach to estimating the scour depth at culvert outlets. The data sets of laboratory measurements of scour depths at culverts were compiled from published literature and used to train the GEP network or evolve the program. The developed network and evolved programs were validated using a random subset of the scour observations that were not used in GEP training. The GEP was found to be more effective in predicting the scour depth at culvert outlets (R-2 = 0.989, RMSE = 0.0678), compared with the regression equations and artificial neural networks (ANN) modelling.

关键词： Local scour genetic programming gene-expression programming Artificial neural networks Radial basis function Culverts

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Effects of binder types and other significant variables on the unconfined compressive strength of chemical-stabilized clayey soil using gene-expression programming

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NEURAL COMPUTING & APPLICATIONS 2022年第11期34卷 9103-9121页

作者： Van-Ngoc Pham Oh, Erwin Ong, Dominic E. L. Griffith Univ Sch Engn & Built Environm Gold Coast Australia Univ Danang Univ Sci & Technol Fac Rd & Bridge Engn Danang Vietnam

Soil stabilization is an in situ soil treatment in which soils are mixed with cementitious or other chemical stabilizing agents. Determining the unconfined compressive strength (UCS) of stabilized soil is a principal task in the design and construction of the ground improvement. Hence, this study aims to develop a reliable predictive model for the UCS of clay stabilization with common cementitious binders using the gene-expression programming (GEP) technique. Eleven parameters, including the soil characteristics, the binder types, the binder contents, the mixing method, and the curing period, were considered as the independent variables in the model. The research results show that the selected optimal GEP-based model performs well with an acceptable correlation coefficient (R = 0.951) and low errors (e.g., RMSE and MAE). Besides, parametric analyses indicate that the plastic index, the percentage of clay, and the total water content have a negative effect on the UCS of stabilized soil. In contrast, the percentage of silt and sand, the binder types, the binder contents, and the curing time show a positive effect on the strength of stabilized soil. In addition, the strength of stabilized clay could be significantly enhanced by combining cement with slag, lime, or fly ash with a reasonable ratio, or by reducing the natural water content in the soil. The research findings could help engineers choose suitable binder types and cost-effective methods to optimize the UCS of stabilized clay.

关键词： Clay stabilization UCS model Chemical binder types gene-expression programming Parametric study

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Regional-Specific Numerical Models of Evapotranspiration Using gene-expression programming Interface in Sahel

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WATER RESOURCES MANAGEMENT 2012年第15期26卷 4367-4380页

作者： Traore, Seydou Guven, Aytac Gaziantep Univ Dept Civil Engn TR-27310 Gaziantep Turkey UNECA African Climate Policy Ctr ACPC FSSDD Addis Ababa Ethiopia INERA GRN SP Minist Res & Sci Innovat Farako Ba Bobo Dioulasso Burkina Faso

An accurate and simple Reference Evapotranspiration (ETo) numerical model eases to use for supporting irrigation planning and its effective management is highly desired in Sahelian regions. This paper investigates the performance ability of the gene-expression programming (GEP) for modeling ETo using decadal climatic data from a Sahelian country;Burkina Faso. For the study;important data are collected from six synoptic meteorological stations located in different regions;Gaoua, P, Boromo, Ouahigouya, Bogand, and Dori. The climatic data combinations are used as inputs to develop the GEP models at regional-specific data basis for estimating ETo. GEP performances are evaluated with the root mean square error (RMSE), and coefficient of correlation (R) between estimated and targeted Penman-Monteith FAO56 set as the true reference values. Obviously;from the statistical viewpoint;GEP computing technique has showed a good ability for providing numerical models on a regional data basis. The performances of GEP based on temperatures data are quite good able to substitute empirical equations at regional level to some extent. It is found that the models with wind velocity yield high accuracies by causing radical improve of the performances with R-2 (0.925-0.961) and RMSE (0.131-0.272 mm day(-1));while relative humidity may cause only (R-2 = 0.801-0.933 and RMSE = 0.370-0.578 mm day(-1)). Statistically;GEP is an effectual modeling tool for computing successfully evapotranspiration in Sahel.

关键词： Evapotranspiration modeling gene-expression programming Sahel-specific data Irrigation planning

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Use of gene-expression programming to Estimate Manning's Roughness Coefficient for High Gradient Streams

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WATER RESOURCES MANAGEMENT 2013年第3期27卷 715-729页

作者： Azamathulla, H. Md Jarrett, Robert D. Univ Sains Malaysia River Engn & Urban Drainage Res Ctr REDAC Nibong Tebal 14300 Pulau Pinang Malaysia US Geol Survey Natl Res Program Lakewood CO 80225 USA

Manning's roughness coefficient (n) has been widely used in the estimation of flood discharges or depths of flow in natural channels. Therefore, the selection of appropriate Manning's n values is of paramount importance for hydraulic engineers and hydrologists and requires considerable experience, although extensive guidelines are available. generally, the largest source of error in post-flood estimates (termed indirect measurements) is due to estimates of Manning's n values, particularly when there has been minimal field verification of flow resistance. This emphasizes the need to improve methods for estimating n values. The objective of this study was to develop a soft computing model in the estimation of the Manning's n values using 75 discharge measurements on 21 high gradient streams in Colorado, USA. The data are from high gradient (S > 0.002 m/m), cobble- and boulder-bed streams for within bank flows. This study presents gene-expression programming (GEP), an extension of genetic programming (GP), as an improved approach to estimate Manning's roughness coefficient for high gradient streams. This study uses field data and assessed the potential of gene-expression programming (GEP) to estimate Manning's n values. GEP is a search technique that automatically simplifies genetic programs during an evolutionary processes (or evolves) to obtain the most robust computer program (e.g., simplify mathematical expressions, decision trees, polynomial constructs, and logical expressions). Field measurements collected by Jarrett (J Hydraulic Eng ASCE 110: 1519-1539, 1984) were used to train the GEP network and evolve programs. The developed network and evolved programs were validated by using observations that were not involved in training. GEP and ANN-RBF (artificial neural network-radial basis function) models were found to be substantially more effective (e.g., R-2 for testing/validation of GEP and RBF-ANN is 0.745 and 0.65, respectively) than Jarrett's (J Hydraulic Eng A

关键词： Open-channel flow Manning's roughness coefficient gene-expression programming Artificial neural networks High gradient streams Mountain Rivers

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