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 model’s 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.
Permeation grouting is a widely used technique for soil improvement in construction engineering. Thus, predicting the results of the grouting activity is a particularly interesting topic that has drawn the attention of researchers both from the academic field and industry. Recent literature has indicated that artificial intelligence (AI) approaches for groutability prediction are capable of delivering better performance than traditional formula-based ones. In this study, a novel AI method, evolutionary Least Squares Support Vector Machine Inference Model for groutability prediction (ELSIM-GP), is proposed to forecast the result of grouting activity that utilizes microfine cement grout. In the model, Least Squares Support Vector Machine (LS-SVM) is a supervised machine learning technique that is employed to learn the decision boundary for classifying high dimensional data. Differential Evolution (DE) is integrated into ELSIM-GP for automatically optimizing its tuning parameters. 240 historical cases of grouting process for sandy silt soil have been collected to train, validate, and test the inference model. Experimental results demonstrated that ELSIM-GP can overcome other benchmark approaches in terms of forecasting accuracy. Therefore, the proposed approach is a promising alternative for predicting groutability.
Abstract In construction engineering, permeation grouting with microfine cement is a widely utilized approach for soil improvement. Hence, estimating groutability is a very important task that should be carried out in the planning phase of any grouting project. This research aims at establishing a novel method for groutability prediction with the utilization of microfine cement in sandy silt soil. The newly proposed approach integrates the Bayesian framework and the K-nearest neighbor (K-NN) density estimation technique. The Bayesian framework is used to achieve probabilistic groutability estimations. Meanwhile, the K-NN method is employed to approximate the conditional probability density functions. Moreover, to establish the new approach, 240 in-situ grouting cases have been recorded during the progress of Mass Rapid Transit and highway projects in Taiwan. Experimental results point out that the proposed method can deliver superior prediction accuracy. Hence, the new groutability estimation approach is a promising alternative to help construction engineers in grouting process assessment.
DP Kozlenko, SE Kichanov, EV Lukin, NT Dang, LS Dubrovinsky, H-P Liermann, W Morgenroth, AA Kamynin, SA Gridnev, BN Savenko, Pressure-induced polar phases in relaxor multiferroic PbFe 0.5 Nb 0.5 O 3, Phys. Rev. B 89 174107.
Abstract In the construction industry, evaluating the financial status of a contractor is a challenging task due to the myriad of the input data as well as the complexity of the working environment. This article presents a novel hybrid intelligent approach named as Evolutionary Least Squares Support Vector Machine Inference Model for Predicting Contractor Default Status (ELSIM-PCDS). The proposed ELSIM-PCDS is established by hybridizing the Synthetic Minority Over-sampling Technique (SMOTE), Least Squares Support Vector Machine (LS-SVM), and Differential Evolution (DE) algorithms. In this new paradigm, the SMOTE is specifically used to deal with the imbalanced classification problem. The LS-SVM acts as a supervised learning technique for learning the classification boundary that separates the default and non-default contractors. Additionally, the DE algorithm automatically searches for the optimal parameters of the classification model. Experimental results have demonstrated that the classification performance of the ELSIM-PCDS is better than that of other benchmark methods. Therefore, the proposed hybrid approach is a promising alternative for predicting contractor default status.
T.H.Tran, A.Govin, R.Guyonnet, P.Grosseau, C.Lors, D.Damidot, O.Devès, B.Ruot. Influence of the intrinsic characteristics of mortars on their biofouling by pigmented organisms: Comparison between laboratory and field-scale experiments. International Biodeterioration and Biodegradation 86, 2014, p334-342. (http://dx.doi.org/10.1016/j.ibiod.2013.10.005)
The pressure effects on melting temperatures of transition metals have been studied based on the combination of the modified Lindemann criterion with statistical moment method in quantum statistical mechanics. Numerical calculations have been performed for five transition metals including Cu, Pd, Pt, Ni, and Mn up to pressure 100 GPa. Our results are in good and reasonable agreements with available experimental data. This approach gives us a relatively simple method for qualitatively calculating high-pressure melting temperature. Moreover, it can be used to verify future experimental and theoretical works. This research proposes the potential of the combination of statistical moment method and the modified Lindemann criterion on predicting high-pressure melting of materials.
D. Quang Dao, T. Rogaume, J. Luche, F. Richard, L. Bustamante Valencia, S. Ruban, (2014), Polyamide 6 and polyurethane used as liner for hydrogen composite cylinder: an estimation of fire behaviours. Fire Technology, DOI: 10.1007/s10694-014-0423-4, (http://link.springer.com/article/10.1007/s10694-014-0423-4)
Quoc Hung Phan. Optimal Liouville-type theorem for a parabolic system Discrete Contin. Dynam. Systems, Series A, 35(1): 399-409, 2015.
