Reliability-based failure cause assessment of collapsed bridge during construction

Until now, in many forensic reports, the failure cause assessments are usually carried out by a deterministic approach so far. However, it may be possible for the forensic investigation to lead to unreasonable results far from the real collapse scenario, because the deterministic approach does not systematically take into account any information on the uncertainties involved in the failures of structures.Reliability-based failure cause assessment (reliability-based forensic engineering) methodology is developed which can incorporate the uncertainties involved in structural failures and structures, and to apply them to the collapsed bridge in order to identify the most critical failure scenario and find the cause that triggered the bridge collapse. Moreover, to save the time and cost of evaluation, an algorithm of automated event tree analysis (ETA) is proposed and possible to automatically calculate the failure probabilities of the failure events and the occurrence probabilities of failure scenarios. Also, for reliability analysis, uncertainties are estimated more reasonably by using the Bayesian approach based on the experimental laboratory testing data in the forensic report. For the applicability, the proposed approach is applied to the Hang-ju Grand Bridge, which collapsed during construction, and compared with deterministic approach.     

The role of N,N-dimethylaniline in the formation of titania gel monolith by sol-gel method

19950405 Our investigation on the rheological aspects such as gelling time, specific density, fluidity, viscosity and surface tension of the sol derived from titanium-tetra-isopropoxide (TTIP), water and isopropanol (i-PrOH) has been reviewed in the presence of n,n-dimethylaniline (DMA) as a drying control chemical agent (DCCA). Dry gels derived from DMA stabilized sol were found to be crack free with a larger surface area. The titania gel monolith obtained in the presence of DMA could be processed into rutile at much lower temperatures.     

Electrical and optical studies of ZnO:Ga thin films fabricated via the sol–gel technique

The effects of dopant (Ga) concentration, post-heat treatment temperatures, and different heat treatment environments on the morphology, electrical and optical properties of ZnO films were studied. ZnO films doped with Ga are derived from non-alkoxide zinc acetate via the alcoholic route by a sol–gel dip-coating technique. Introduction of Ga, as a dopant, can reduce crystallite size, which is attributed to the increased number of nucleation centers. Grain growth was also observed at high post-heat treatment temperature. It was discovered that different post-heat treatment environments (air or reduced atmosphere) did not change the structure orientation, microstructure shapes and size. It was found that the sheet resistance, R_s could be reduced by an order of magnitude, using post-heat treatment in reduced atmosphere (4%H₂–96%N₂).     

Is there a role for continuation phase cognitive therapy for depressed outpatients?

Two pilot studies evaluated the rate of relapse or recurrence (i.e., major depressive disorder) after cognitive therapy (CT). Two sequential cohorts included outpatients who responded to acute phase CT (A-CT) and who agreed to monthly, treatment-free follow-up. In Study 1, the Kaplan-Meier technique estimated relapse and recurrence rates of 40% at 6 months, 45% at 8 months, 50% at 12 months, 67% at 18 months, and 74% at 24 months. In Study 2, responders to A-CT received 8 months (10 sessions) of continuation phase CT (C-CT). In Study 2, relapse or recurrence was 20% at 6 and 8 months, 27% at 12 months, and 36% at 18 and 24 months after A-CT. An exploratory log-rank test showed that relapse or recurrence-free survival was greater in Study 2 than in Study 1. If replicated, this result suggests that C-CT can reduce depressive relapse or recurrence. Alternative explanations are presented. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Average-case analysis of the Modified Harmonic algorithm

In this paper we analyze the average-case performance of the Modified Harmonic algorithm for on-line bin packing. We first analyze the average-case performance for arbitrary distribution of item sizes over (0,1]. This analysis is based on the following result. Letf ₁ andf ₂ be two linear combinations of random variables {N _i } _i=1 ^k where theN _i 's have a joint multinomial distribution for eachn= _i=1 ^k ,N _i . LetE(f ₁) O andE(f ₂) 0. Then lim_n E(max(_{f 1},f ₂ ))/n = lim _n max(E(f ₁),E(f ₂))/n. We then consider the special case when the item sizes are uniformly distributed over (0,1]. For specific values of the parameters, the Modified Harmonic algorithm turns out to be better than the other two linear-time on-line algorithms—Next Fit and Harmonic—in both the worst case as well as the average case. We also obtain optimal values for the parameters of the algorithm from the average-case standpoint. For these values of the parameters, the average-case performance ratio is less than 1.19. This compares well with the performance ratios 1.333. and 1.2865. of the Next Fit algorithm and the Harmonic algorithm, respectively.     

Average-case analysis of the Modified Harmonic algorithm

In this paper we analyze the average-case performance of the Modified Harmonic algorithm for on-line bin packing. We first analyze the average-case performance for arbitrary distribution of item sizes over (0,1]. This analysis is based on the following result. Letf ₁ andf ₂ be two linear combinations of random variables {N _i } _i=1 ^k where theN _i 's have a joint multinomial distribution for eachn=σ _i=1 ^k ,N _i . LetE(f ₁) ≠ O andE(f ₂)≠ 0. Then lim_{n →}∞E(max(_{f 1},f ₂ ))/n = lim _n →∞ max(E(f ₁),E(f ₂))/n. We then consider the special case when the item sizes are uniformly distributed over (0,1]. For specific values of the parameters, the Modified Harmonic algorithm turns out to be better than the other two linear-time on-line algorithms—Next Fit and Harmonic—in both the worst case as well as the average case. We also obtain optimal values for the parameters of the algorithm from the average-case standpoint. For these values of the parameters, the average-case performance ratio is less than 1.19. This compares well with the performance ratios 1.333. and 1.2865. of the Next Fit algorithm and the Harmonic algorithm, respectively.     

Silo music and silo quake: granular flow-induced vibration

Acceleration and sound measurements during granular discharge from silos are used to show that silo music is a sound resonance produced by silo quake. In tall and narrow silos, the latter is produced by stick-slip friction between the wall and the granular material. For the discharge rates studied, the occurrence of flow pulsations is determined primarily by the surface properties of the granular material and the silo wall. The measurements show that the pulsating motion of the granular material drives the oscillatory motion of the silo.     

A non-parametric method to determine basic probability assignment for classification problems

As an important tool for knowledge representation and decision-making under uncertainty, Dempster-Shafer evidence theory (D-S theory) has been used in many fields. The application of D-S theory is critically dependent on the availability of the basic probability assignment (BPA). The determination of BPA is still an open issue. A non-parametric method to obtain BPA is proposed in this paper. This method can handle multi-attribute datasets in classification problems. Each attribute value of the dataset sample is treated as a stochastic quantity. Its non-parametric probability density function (PDF) is calculated using the training data, which can be regarded as the probability model for the corresponding attribute. The BPA function is then constructed based on the relationship between the test sample and the probability models. The missing attribute values in datasets are treated as ignorance in the framework of the evidence theory. This method does not have the assumption of any particular distribution. As a result, it can be flexibly used in many engineering applications. The obtained BPA can avoid high conflict between evidence, which is desired in data fusion. Several benchmark classification problems are used to demonstrate the proposed method and to compare against existing methods. The constructed classifier based on the proposed method compares well to the state-of-the-art algorithms.