Shaking table experimental study of recycled concrete frame-shear wall structures

In this study, four 1/5 scaled shaking table tests were conducted to investigate the seismic performance of recycled concrete frame-shear wall structures with different recycled aggregates replacement rates and concealed bracing detail. The four tested structures included one normal concrete model, one recycled coarse aggregate concrete model, and two recycled coarse and fi ne aggregate concrete models with or without concealed bracings inside the shear walls. The dynamic characteristics, dynamic response and failure mode of each model were compared and analyzed. Finite element models were also developed and nonlinear time-history response analysis was conducted. The test and analysis results show that the seismic performance of the recycled coarse aggregate concrete frame-shear wall structure is slightly worse than the normal concrete structure. The seismic resistance capacity of the recycled concrete frame-shear wall structure can be greatly improved by setting up concealed bracings inside the walls. With appropriate design, the recycled coarse aggregate concrete frame-shear wall structure and recycled concrete structure with concealed bracings inside the walls can be applied in buildings.

Seismic risk assessment for developing countries: Pakistan as a case study

Modern Earthquake Risk Assessment（ERA） methods usually require seismo-tectonic information for Probabilistic Seismic Hazard Assessment（PSHA） that may not be readily available in developing countries. To bypass this drawback, this paper presents a practical event-based PSHA method that uses instrumental seismicity, available historical seismicity, as well as limited information on geology and tectonic setting. Historical seismicity is integrated with instrumental seismicity to determine the long-term hazard. The tectonic setting is included by assigning seismic source zones associated with known major faults. Monte Carlo simulations are used to generate earthquake catalogues with randomized key hazard parameters. A case study region in Pakistan is selected to demonstrate the effectiveness of the method. The results indicate that the proposed method produces seismic hazard maps consistent with previous studies, thus being suitable for generating such maps in regions where limited data are available. The PSHA procedure is developed as an integral part of an ERA framework named EQRAM. The framework is also used to determine seismic risk in terms of annual losses for the study region.

A hybrid artificial bee colony algorithm and support vector machine for predicting blast-induced ground vibration

Because nearby construction has harmful effects,precisely predicting blast-induced ground vibration is critical.In this paper,a hybrid artificial bee colony(ABC)and support vector machine(SVM)model was proposed for predicting the value of peak particle velocity(PPV),which is used to describe blast-induced ground vibration.To construct the model,5 potentially relevant factors,including controllable and uncontrollable parameters,were considered as input parameters,and PPV was set as the output parameter.Forty-five samples were recorded from the Hongling lead-zinc mine.An ABC-SVM model was developed and trained on 35 samples via 5-fold cross-validation(CV).A testing set(10 samples)was used to evaluate the prediction performance of the ABC-SVM model.SVM and four empirical models(United States Bureau of Mines(USBM),Amraseys-Hendron(A-H),Langefors-Kihstrom(L-K),and Central Mining Research Institute(CMRI))also were introduced for comparison.Next,the performances of the models were analyzed by using 3 statistical parameters:the correlation coefficient(R2),root-mean-square error(RMSE),and variance accounted for(VAF).ABC-SVM had the highest R2 and VAF values followed by the SVM,A-H,USBM,CMRI,and L-K methods.The results demonstrated that ABC-SVM outperformed SVM and the empirical predictors for predicting PPV.Moreover,the best results from the R2,RMSE,and VAF indices were 0.9628,0.2737,and 96.05%for the ABC-SVM model.The sensitivities of the parameters also were investigated,and the height difference between the blast point and the monitoring station was found to be the parameter that had the most influence on PPV.