Assessment of expected damage on buildings subjected to Lorca earthquake through an energy-based seismic index method and nonlinear dynamic response analyses

The city of Lorca (Spain) was hit on May 11th, 2011, by two consecutive earth-quakes of magnitudes 4.6 and 5.2 Mw, causing casualties and important damage in buildings. Many of the damaged structures were reinforced concrete frames with wide beams. This study quantifies the expected level of damage on this structural type in the case of the Lorca earth-quake by means of a seismic index Iv that compares the energy input by the earthquake with the energy absorption/dissipation capacity of the structure. The prototype frames investigated represent structures designed in two time periods (1994–2002 and 2003–2008), in which the applicable codes were different. The influence of the masonry infill walls and the proneness of the frames to concentrate damage in a given story were further investigated through nonlinear dynamic response analyses. It is found that (1) the seismic index method predicts levels of damage that range from moderate/severe to complete collapse; this prediction is consistent with the observed damage; (2) the presence of masonry infill walls makes the structure very prone to damage concentration and reduces the overall seismic capacity of the building; and (3) a proper hierarchy of strength between beams and columns that guarantees the formation of a strong column-weak beam mechanism (as prescribed by seismic codes), as well as the adoption of counter-measures to avoid the negative interaction between non-structural infill walls and the main frame, would have reduced the level of damage from Iv=1 (collapse) to about Iv=0.5 (moderate/severe damage)

Moldels for reproducing the damage scenario of the Lorca earthquake

A damage scenario modelling is developed and compared with the damage distribution observed after the 2011 Lorca earthquake. The strong ground motion models considered include five modern ground motion prediction equations (GMPEs) amply used worldwide. Capacity and fragility curves from the Risk-UE project are utilized to model building vulnerability and expected damage. Damage estimates resulting from different combinations of GMPE and capacity/fragility curves are compared with the actual damage scenario, establishing the combination that best explains the observed damage distribution. In addition, some recommendations are proposed, including correction factors in fragility curves in order to reproduce in a better way the observed damage in masonry and reinforce concrete buildings. The lessons learned would contribute to improve the simulation of expected damages due to future earthquakes in Lorca or other regions in Spain with similar characteristics regarding attenuation and vulnerability.

Long term trends in the evolution of H(3) HA1 human influenza type A

We have studied the HA1 domain of 254 human influenza A(H3N2) virus genes for clues that might help identify characteristics of hemagglutinins (HAs) of circulating strains that are predictive of that strain’s epidemic potential. Our preliminary findings include the following. (i) The most parsimonious tree found requires 1,260 substitutions of which 712 are silent and 548 are replacement substitutions. (ii) The HA1 portion of the HA gene is evolving at a rate of 5.7 nucleotide substitutions/year or 5.7 × 10−3 substitutions/site per year. (iii) The replacement substitutions are distributed randomly across the three positions of the codon when allowance is made for the number of ways each codon can change the encoded amino acid. (iv) The replacement substitutions are not distributed randomly over the branches of the tree, there being 2.2 times more changes per tip branch than for non-tip branches. This result is independent of how the virus was amplified (egg grown or kidney cell grown) prior to sequencing or if sequencing was carried out directly on the original clinical specimen by PCR. (v) These excess changes on the tip branches are probably the result of a bias in the choice of strains to sequence and the detection of deleterious mutations that had not yet been removed by negative selection. (vi) There are six hypervariable codons accumulating replacement substitutions at an average rate that is 7.2 times that of the other varied codons. (vii) The number of variable codons in the trunk branches (the winners of the competitive race against the immune system) is 47 ± 5, significantly fewer than in the twigs (90 ± 7), which in turn is significantly fewer variable codons than in tip branches (175 ± 8). (viii) A minimum of one of every 12 branches has nodes at opposite ends representing viruses that reside on different continents. This is, however, no more than would be expected if one were to randomly reassign the continent of origin of the isolates. (ix) Of 99 codons with at least four mutations, 31 have ratios of non-silent to silent changes with probabilities less than 0.05 of occurring by chance, and 14 of those have probabilities <0.005. These observations strongly support positive Darwinian selection. We suggest that the small number of variable positions along the successful trunk lineage, together with knowledge of the codons that have shown positive selection, may provide clues that permit an improved prediction of which strains will cause epidemics and therefore should be used for vaccine production.

Short-term Load Forecasting Using Neural Network For Future Smart Grid Application

Short-term load forecasting of power system has been a classic problem for a long time. Not merely it has been researched extensively and intensively, but also a variety of forecasting methods has been raised. This thesis outlines some aspects and functions of smart meter. It also presents different policies and current statuses as well as future projects and objectives of SG development in several countries. Then the thesis compares main aspects about latest products of smart meter from different companies. Lastly, three types of prediction models are established in MATLAB to emulate the functions of smart grid in the short-term load forecasting, and then their results are compared and analyzed in terms of accuracy. For this thesis, more variables such as dew point temperature are used in the Neural Network model to achieve more accuracy for better short-term load forecasting results.

Chloride Penetration Prediction in Concrete through an Empirical Model Based on Constant Flux Diffusion

An empirical model based on constant flux is presented for chloride transport through concrete in atmospherical exposure conditions. A continuous supply of chlorides is assumed as a constant mass flux at the exposed concrete surface. The model is applied to experimental chloride profiles obtained from a real marine structure, and results are compared with the classical error-function model. The proposed model shows some advantages. It yields a better predictive capacity than the classical error-function model. The previously observed chloride surface concentration increases are compatible with the proposed model. Nevertheless, the predictive capacity of the model can fail if the concrete microstructure changes with time. The model seems to be appropriate for well-maturated concretes exposed to a marine environment in atmospherical conditions.

Impact of gender on risk stratification by exercise and dobutamine stress echocardiography: long-term mortality in 4234 women and 6898 men

Aims Prior research is limited with regard to the diagnostic and prognostic accuracy of commonplace cardiac imaging modalities in women. The aim of this study was to examine 5-year mortality in 4234 women and 6898 men undergoing exercise or dobutamine stress echocardiography at three hospitals. Methods and results Univariable and multivariable Cox proportional hazards models were used to estimate time to cardiac death in this multi-centre, observational registry. Of the 11 132 patients, women had a greater frequency of cardiac risk factors (P < 0.0001). However, men more often had a history of coronary disease including a greater frequency of echocardiographic wall motion abnormalities (P < 0.0001). During 5 years of follow-up, 103 women and 226 men died from ischaernic heart disease (P < 0.0001). Echocardiographic estimates of left ventricular function (P < 0.0001) and the extent of ischaernic watt motion abnormalities (P < 0.0001) were highly predictive of cardiac death. Risk-adjusted 5-year survival was 99.4, 97.6, and 95% for exercising women with no, single, and multi-vessel ischaemia (P < 0.0001). For women undergoing dobutamine stress, 5-year survival was 95, 89, and 86.6% for those with 0, 1, and 2-3 vessel ischaemia (P < 0.0001). Exercising men had a 2.0-fold higher risk at every level of worsening ischaemia (P < 0.0001). Significantly worsening cardiac survival was noted for the 1568 men undergoing dobutamine stress echocardiography (P < 0.0001); no ischaemia was associated with 92% 5-year survival as compared with death rates of &GE; 16% for men with ischaemia on dobutamine stress echocardiography (P < 0.0001). Conclusion Echocardiographic measures of inducible wall motion abnormalities and global and regional left ventricutar function are highly predictive of long-term outcome for women and men alike.

Physical and psychological factors maintain long-term predictive capacity post-whiplash injury

Higher initial levels of pain and disability, older age, cold hyperalgesia, impaired sympathetic vasoconstriction and moderate post-traumatic stress symptoms have been shown to be associated with poor outcome 6 months following whiplash injury. This study prospectively investigated the predictive capacity of these variables at a long-term follow-up. Sixty-five of an initial cohort of 76 acutely injured whiplash participants were followed to 2-3 years post-accident. Motor function (ROM; kinaesthetic sense; activity of the superficial neck flexors (EMG) during cranio-cervical flexion), quantitative sensory testing (pressure, thermal pain thresholds and brachial plexus provocation test), sympathetic vasoconstrictor responses and psychological distress (GHQ-28, TSK and IES) were measured. The outcome measure was Neck Disability Index (NDI) scores. Participants with ongoing moderate/severe symptoms at 2-3 years continued to manifest decreased ROM, increased EMG during cranio-cervical flexion, sensory hypersensitivity and elevated levels of psychological distress when compared to recovered participants and those with milder symptoms. The latter two groups showed only persistent deficits in cervical muscle recruitment patterns. Higher initial NDI scores (OR 1.00-1.1), older age (OR 1.00-1.13), cold hyperalgesia (OR 1.1-1.13) and post-traumatic stress symptoms (OR 1.03-1.2) remained significant predictors of poor outcome at long-term follow-up (r(2) = 0.56). The robustness of these physical and psychological factors suggests that their assessment in the acute stage following whiplash injury will be important. (c) 2006 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

Efffluent flux prediction in variably saturated soil zones within a septic tank-soil absorption trench

The treatment and hydraulic mechanisms in a septic tank-soil absorption system ( SAS) are highly influenced by the clogging layer or biomat zone which develops on bottom and lower sidewall surfaces within the trench. Flow rates through the biomat and sub-biomat zones are governed largely by the biomat hydraulic properties (resistance and hydraulic conductivity) and the unsaturated hydraulic conductivity of the underlying soil. One- and 2-dimensional models were used to investigate the relative importance of sidewall and vertical flow rates and pathways in SAS. Results of 1-dimensional modelling show that several orders of magnitude variation in saturated hydraulic conductivity (Ks) reduce to a 1 order of magnitude variation in long-term flow rates. To increase the reliability of prediction of septic trench hydrology, HYDRUS-2D was used to model 2-dimensional flow. In the permeable soils, under high trench loading, effluent preferentially flowed in the upper region of the trench where no resistant biomat was present (the exfiltration zone). By comparison, flow was more evenly partitioned between the biomat zones and the exfiltration zones of the low permeability soil. An increase in effluent infiltration corresponded with a greater availability of exfiltration zone, rather than a lower resistance of biomat. Results of modelling simulations demonstrated the important role that a permeable A horizon may play in limiting surface surcharge of effluent under high trench hydraulic loading.

The displacement and strain field of three-dimensional rheologic model of earthquake preparation

Based on the three-dimensional elastic inclusion model proposed by Dobrovolskii, we developed a rheological inclusion model to study earthquake preparation processes. By using the Corresponding Principle in the theory of rheologic mechanics, we derived the analytic expressions of viscoelastic displacement U(r, t) , V(r, t) and W(r, t), normal strains epsilon(xx) (r, t), epsilon(yy) (r, t) and epsilon(zz) (r, t) and the bulk strain theta (r, t) at an arbitrary point (x, y, z) in three directions of X axis, Y axis and Z axis produced by a three-dimensional inclusion in the semi-infinite rheologic medium defined by the standard linear rheologic model. Subsequent to the spatial-temporal variation of bulk strain being computed on the ground produced by such a spherical rheologic inclusion, interesting results are obtained, suggesting that the bulk strain produced by a hard inclusion change with time according to three stages (alpha, beta, gamma) with different characteristics, similar to that of geodetic deformation observations, but different with the results of a soft inclusion. These theoretical results can be used to explain the characteristics of spatial-temporal evolution, patterns, quadrant-distribution of earthquake precursors, the changeability, spontaneity and complexity of short-term and imminent-term precursors. It offers a theoretical base to build physical models for earthquake precursors and to predict the earthquakes.

Prediction of steady-state flux through variably saturated zones within a septic absorption trench

This paper describes effluent flow dynamics within a septic absorption system and the prediction of flow through the biomat and sub-biomat zone. Using soil hydraulic properties in a one dimensional model we demonstrate how soil hydraulic properties interact with biomat resistances to determine long-term acceptance rate (LTAR). The LTAR is a key parameter used in the Australian and New Zealand Standard AS1547:2000 to calculate the area of trench required to ensure trenches are not overloaded. Results show that several orders of magnitude variation in saturated hydraulic conductivity (Ks) collapse to a one order of magnitude variation in LTAR. These results are calculated from a model using basic flow theory, allowing LTAR to be estimated for any combination of biomat resistance and soil hydraulic properties. To increase the reliability of prediction of septic trench hydrology, HYDRUS 2D was used to model two dimensional flow. For more permeable soils, the exfiltration zone above sidewall biomat growth is shown to be a key pathway for excess effluent flow.

Leading edge forecasting techniques for exchange rate prediction

This paper describes how modern machine learning techniques can be used in conjunction with statistical methods to forecast short term movements in exchange rates, producing models suitable for use in trading. It compares the results achieved by two different techniques, and shows how they can be used in a complementary fashion. The paper draws on experience of both inter- and intra-day forecasting taken from earlier studies conducted by Logica and Chemical Bank Quantitative Research and Trading (QRT) group's experience in developing trading models.

Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively.

Short-term wind power forecasting using Gaussian processes

Since wind has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safety and economics of wind energy utilization. In this paper, we investigate a combination of numeric and probabilistic models: one-day-ahead wind power forecasts were made with Gaussian Processes (GPs) applied to the outputs of a Numerical Weather Prediction (NWP) model. Firstly the wind speed data from NWP was corrected by a GP. Then, as there is always a defined limit on power generated in a wind turbine due the turbine controlling strategy, a Censored GP was used to model the relationship between the corrected wind speed and power output. To validate the proposed approach, two real world datasets were used for model construction and testing. The simulation results were compared with the persistence method and Artificial Neural Networks (ANNs); the proposed model achieves about 11% improvement in forecasting accuracy (Mean Absolute Error) compared to the ANN model on one dataset, and nearly 5% improvement on another.

Pavement performance prediction using adaptive logic networks

Pavement performance is one of the most important components of the pavement management system. Prediction of the future performance of a pavement section is important in programming maintenance and rehabilitation needs. Models for predicting pavement performance have been developed on the basis of traffic and age. The purpose of this research is to extend the use of a relatively new approach to performance prediction in pavement performance modeling using adaptive logic networks (ALN). Adaptive logic networks have recently emerged as an effective alternative to artificial neural networks for machine learning tasks. ^ The ALN predictive methodology is applicable to a wide variety of contexts including prediction of roughness based indices, composite rating indices and/or individual pavement distresses. The ALN program requires key information about a pavement section, including the current distress indexes, pavement age, climate region, traffic and other variables to predict yearly performance values into the future. ^ This research investigates the effect of different learning rates of the ALN in pavement performance modeling. It can be used at both the network and project level for predicting the long term performance of a road network. Results indicate that the ALN approach is well suited for pavement performance prediction modeling and shows a significant improvement over the results obtained from other artificial intelligence approaches. ^

Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^