Nhat-Duc Hoang

Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam

ABSTRACT This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction, named as DE–LSSVMSLP. The LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM model. In this research, a GIS database with 129 historical landslide records in the Quy Hop area (Central Vietnam) has been collected to establish the hybrid model. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess the performance of the newly constructed model. Experimental results show that the proposed model has high performances with approximately 82% of AUCs on both training and validating datasets. The model’s results were compared with those obtained from other methods, Support Vector Machines, Multilayer Perceptron Neural Networks, and J48 Decision Trees. The result comparison demonstrates that the DE–LSSVMSLP deems best suited for the dataset at hand; therefore, the proposed model can be a promising tool for spatial prediction of rainfall-induced shallow landslides for the study area.

Groutability estimation of grouting processes with cement grouts using Differential Flower Pollination Optimized Support Vector Machine

Display OmittedDifferential Flower Pollination-optimized Support Vector Machine for Groutability Prediction (DFP-SVMGP). A soft computing method for groutability estimation is proposed.A hybrid metaheuristic is constructed to optimize the SVM-based model.The effect of evaluation functions on the model performance is studied.Relevant influencing factors in two datasets have been revealed.The new approach attains high prediction accuracy. This research presents a soft computing methodology for groutability estimation of grouting processes that employ cement grouts. The method integrates a hybrid metaheuristic and the Support Vector Machine (SVM) with evolutionary input factor and hyper-parameter selection. The new prediction model is constructed and verified using two datasets of grouting experiments. The contribution of this study to the body of knowledge is multifold. First, the efficacies of the Flower Pollenation Algorithm (FPA) and the Differential Evolution (DE) are combined to establish an integrated metaheuristic approach, named as Differential Flower Pollenation (DFP). The integration of the FPA and the DE aims at harnessing the strength and complementing the disadvantage of each individual optimization algorithm. Second, the DFP is employed to optimize the input factor selection and hyper-parameter tuning processes of the SVM-based groutability prediction model. Third, this study conducts a comparative work to investigate the effects of different evaluation functions on the model performance. Finally, the research findings show that the new integrated framework can help identify a set of relevant groutability influencing factors and deliver superior prediction performance compared with other state-of-the-art approaches.

A Novel Relevance Vector Machine Classifier with Cuckoo Search Optimization for Spatial Prediction of Landslides

AbstractIn mountainous regions, landslides are the typical disasters that have brought about significant losses of human life and property. Therefore, the capability of making accurate landslide assessments is very useful for government agencies to develop land-use planning and mitigation measures. The research objective of this paper is to investigate a novel methodology for spatial prediction of landslides on the basis of the relevance vector machine classifier (RVMC) and the cuckoo search optimization (CSO). The RVMC is used to generalize the classification boundary that separates the input vectors of landslide conditioning factors into two classes: landslide and nonlandslide. Furthermore, the new approach employs the CSO to fine-tune the basis function’s width used in the RVMC. A geographic information system (GIS) database has been established to construct the prediction model. Experimental results point out that the new method is a promising alternative for spatial prediction of landslides.

A novel time-depended evolutionary fuzzy SVM inference model for estimating construction project at completion

Construction projects frequently face cost overruns during the construction phase. Thus, a proactive approach is essential for monitoring project costs and detection of potential problems. In construction management, Estimate at Completion (EAC) is an indicator for assisting project managers in identifying potential problems and developing appropriate responses. This study utilizes weighted Support Vector Machine (wSVM), fuzzy logic, and fast messy Genetic Algorithm (fmGA) to handle distinct characteristics in EAC prediction. The wSVM is employed as a supervised learning technique that can address the features of time series data. The fuzzy logic is aimed to enhance the model capability of approximate reasoning and to deal with uncertainty in EAC prediction. Moreover, fmGA is utilized to optimize models tuning parameters. Simulation results show that the new developed model has achieved a significant improvement in EAC forecasting.

Risk Score Inference for Bridge Maintenance Project Using Evolutionary Fuzzy Least Squares Support Vector Machine

Bridges are essential infrastructure of the transportation network. Therefore, maintenance tasks are mandatory to prevent these structures from degradation over time. In practice, funding availability for maintenance projects is often confined and this necessitates prioritization of different bridges that are in need of remedial activities. The writers aim to construct an artificial intelligence (AI) approach, evolutionary fuzzy least-squares support-vector machine (LSSVM) inference model (EFLSIM), for prioritizing bridges based on risk scores (RSs). In EFLSIM, fuzzy logic (FL) is utilized to enhance the capability of approximate reasoning and to deal with subjective information, which is obtained from human judgment. The inference model employs LSSVM as a supervised learning technique to infer the fuzzy input-output mapping relationship. Differential evolution (DE) is integrated into the model to optimize its tuning parameters. Experimental results and comparison illustrates that EFLSIM can successfully absorb and simulate human knowledge in the bridge-assessment process. Additionally, the newly built model has outperformed other benchmark approaches in terms of both reliability and accuracy. A 10-fold cross-validation process has demonstrated that the EFLSIM has achieved more than 38% reduction in RMS error compared to other benchmark methods. Thus, the proposed AI approach is a promising tool to support decision-makers in bridge-maintenance planning.

Hybrid artificial intelligence approach based on metaheuristic and machine learning for slope stability assessment

This research proposes an AI approach for slope evaluation.The method is based on the Least Squares Support Vector Classification.The Firefly Algorithm is used to optimize the assessment model.A dataset that contains 168 real slopes is utilized to construct the AI model.Experiments prove that the new method is a superior tool for slope evaluation. Slope stability assessment is a critical research area in civil engineering. Disastrous consequences of slope collapse necessitate better tools for predicting their occurrences. This research proposes a hybrid Artificial Intelligence (AI) for slope stability assessment based on metaheuristic and machine learning. The contribution of this study to the body of knowledge is multifold. First, advantages of the Firefly Algorithm (FA) and the Least Squares Support Vector Classification (LS-SVC) are combined to establish an integrated slope prediction model. Second, an inner cross-validation with the operating characteristic curve computation is embedded in the training process to reliably construct the machine learning model. Third, the FA, an effective and easily implemented metaheuristic, is employed to optimize the model construction process by appropriately selecting the LS-SVMs hyper-parameters. Finally, a dataset that contains 168 real cases of slope evaluation, recorded in various countries, is used to establish and confirm the proposed hybrid approach. Experimental results demonstrate that the new hybrid AI model has achieved roughly 4% improvement in classification accuracy compared with other benchmark methods.

Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study

Assessment of the earthquake-induced liquefaction potential is a critical concern in design processes of construction projects. This study proposes a novel soft computing model with a hierarchical structure for evaluating earthquake-induced soil liquefaction. The new approach, named KFDA-LSSVM, combines kernel Fisher discriminant analysis (KFDA) with a least squares support vector machine (LSSVM). Based on the original data set, KFDA is used as a first-level analysis to construct an additional feature that best represents the data structure with consideration of different class labels. In the next level of analysis, based on such additional features and the original features, LSSVM generalizes a classification boundary that separates the learning space into two decision domains: “liquefaction” and “non-liquefaction.” Three data sets of liquefaction records have been used to train and verify the proposed method. The model performance is reliably assessed via a repeated sub-sampling process. Experimental results supported by the Wilcoxon signed-rank test demonstrate significant improvements of the hybrid framework over other benchmark approaches.

A Swarm-Optimized Fuzzy Instance-based Learning approach for predicting slope collapses in mountain roads

Due to the disastrous consequences of slope failures, forecasting their occurrences is a practical need of government agencies to develop strategic disaster prevention programs. This research proposes a Swarm-Optimized Fuzzy Instance-based Learning (SOFIL) model for predicting slope collapses. The proposed model utilizes the Fuzzy k-Nearest Neighbor (FKNN) algorithm as an instance-based learning method to predict slope collapse events. Meanwhile, to determine the models hyper-parameters appropriately, the Firefly Algorithm (FA) is employed as an optimization technique. Experimental results have pointed out that the newly established SOFIL can outperform other benchmarking algorithms. Therefore, the proposed model is very promising to help decision-makers in coping with the slope collapse prediction problem.

Interval estimation of construction cost at completion using least squares support vector machine

AbstractCompleting a project within the planned budget is the bottom-line of construction companies. To achieve this goal, periodic cost estimation is vitally important not only in the planning phase, but also in the execution phase. Due to high uncertainty in operational environment, point estimation of project cost is oftentimes not sufficient to assist the decision-making process. This study utilizes Least Squares Support Vector Machine (LS-SVM), machine learning based interval estimation (MLIE), and Differential Evolution (DE) to establish a novel model for predicting construction project cost. LS-SVM is a supervised learning technique used for regression analysis. MLIE is employed for inference of prediction intervals. Moreover, our model deploys DE in the cross validation process to search for the optimal values of tuning parameters. The newly developed model, named as EAC-LSPIM, yields results consisting of a point estimate coupled with lower and upper prediction limits, at a certain level of confi...

Predicting Compressive Strength of High-Performance Concrete Using Metaheuristic-Optimized Least Squares Support Vector Regression

AbstractThis research establishes a novel model for predicting high-performance concrete (HPC) compressive strength, which hybridizes the firefly algorithm (FA) and the least squares support vector regression (LS-SVR). The LS-SVR is utilized to discover the functional relationship between the compressive strength and HPC components. To achieve the most desirable prediction model that features both modeling accuracy and generalization capability, the FA is employed to optimize the LS-SVR. To construct and verify the proposed model, this study has collected a database consisting of 239 HPC strength tests from an infrastructure development project in central Vietnam. Experimental results have demonstrated that the new model is a promising alternative to predict HPC strength.