Dynamics of structures

Moth flame optimization (MFO) is a swarm-based algorithm with mediocre performance and marginal originality proposed in recent years. It tried to simulate the fantasy navigation mode of moth lateral positioning. The basic MFO has no specific, deep strategies in different periods of the algorithm and a fragile evolutionary basis, which may lead to the problem of falling into local optimum and slow convergence trend. Therefore, this paper introduces a double adaptive weight mechanism into the MFO algorithm, termed as WEMFO, to boost the search capability of the basic MFO and provide a more efficient tool for optimization purposes. The proposed WEMFO adjusts the search strategy adaptively in different periods of the algorithm, making it more flexible between global search (diversification) and local search (intensification). The WEMFO algorithm is compared with some illustrious metaheuristic solvers and advanced metaheuristic methods developed in recent years on thirty benchmark functions. The experimental results expose that the developed WEMFO has apparent compensations in terms of convergence speed and solution accuracy. Moreover, this paper analyzes the diversity and balance of WEMFO and applies the algorithm to several engineering problems. The experimental results show that the WEMFO algorithm has good performance in engineering problems. Additionally, the proposed WEMFO was also applied to train Kernel Extreme Learning Machine (KELM), the resultant optimized WEMFO-KELM model was applied to six clinical disease classification problems. By comparing with MFO-KELM and other five classification models, the experimental results showed that the proposed algorithm had shown better performance in practical problems. An online guide for the algorithm in this research WEMFO and proposed classifier WEMFO-KELM will be publicly available at https://aliasgharheidari.com .

Double adaptive weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical diagnosis

A sensor is a tool used to directly measure the test compound (analyte) in a sample. Ideally, such a device is capable of continuous and reversible response and should not damage the sample. Nanosensor refers to a system in which at least one of the nanostructures is used to detect gases, chemicals, biological agents, electric fields, light, heat, etc. in its construction. The use of nanomaterials significantly increases the sensitivity of the system. In biosensors, the part of the system used to attach to the analyte and specifically detect it is a biological element (such as a DNA strand, antibody, enzyme, whole cell). The “Nano Biosensors” series reviews various types of biosensors and biochips (including an array of biosensors), emphasizing the role of nanostructures, developed for medical and biological applications. Nano Biosensors Electrochemical sensors are sensors that use the biological element as a diagnostic component and the electrode as a transducer. The use of nanostructures in these systems is usually done to fill the gap between the converter and the bioreceptor, which is at the nanoscale. Given the nature of the biomaterial detection process, electrochemical biosensors are divided into catalytic and propulsion. Common electrochemical techniques common in sensors include potentiometric, chronometry, voltammetry, impedance measurement, and field effect transistor (FET). Simultaneous use of the advantages of nanostructures and electrochemical techniques has led to the emergence of sensors with high sensitivity and decomposition power. The use of nanostructures in these sensors is usually done to fill the gap between the converter and the bioreceptor, which is at the nanoscale. Various types of nanostructures including nanoparticles, nanotubes and nanowires, nanopores, self-adhesive monolayers and nanocomposites can be used to improve the performance and efficiency of sensors in their structure. Simultaneous use of the advantages of nanostructures and electrochemical techniques has led to the emergence of sensors with high sensitivity and decomposition power.

Nano Biosensors: Properties, applications and electrochemical techniques

Uncertainty is a significant challenge in tsunami hazard analysis. Tsunami heights are affected by complex factors and change constantly during propagation. The heights of tsunami have random characteristics. This study proposes that the water depths (related to seabed topography) are the most important factors that affect tsunami height. But across the globe, a considerable area of seabed topography has not been measured. So it is necessary to use the method of uncertainty to consider the water depth. The Wiener process is utilized to quantify the random changes of the water depth, which can better describe the situation that water depths change in a non-monotonic way. Considering the uncertainty of water depth, a Weiner process-based probabilistic model was established for predicting the maximum tsunami height, which is different from the maximum tsunami height deterministic or stochastic model previously studied with higher prediction efficiency and good prediction accuracy. The probability distribution of maximum tsunami heights was calculated using the stochastic model. The mean value of the maximum tsunami heights was very similar to the average value of 165 actual observations of maximum tsunami heights collected from 1997 to 2017.

Application of probabilistic method in maximum tsunami height prediction considering stochastic seabed topography

There are many factors leading to construction safety accident. The rule presented under the influence of these factors should be a statistical random rule. To reveal those random rules and study the probability prediction method of construction safety accident, according to stochastic process theory, general stochastic process, Markov process and normal process are respectively used to simulate the risk-accident process in this paper. First, in the general-random-process-based analysis the probability of accidents in a period of time is calculated. Then, the Markov property of the construction safety risk evolution process is illustrated, and the analytical expression of probability density function of first-passage time of Markov-based risk-accident process is derived to calculate the construction safety probability. In the normal-process-based analysis, the construction safety probability formulas in cases of stationary normal risk process and non-stationary normal risk process with zero mean value are derived respectively. Finally, the number of accidents that may occur on construction site in a period is studied macroscopically based on Poisson process, and the probability distribution of time interval between adjacent accidents and the time of the nth accident are calculated respectively. The results provide useful reference for the prediction and management of construction accidents.

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Analysis of stochastic process to model safety risk in construction industry

Dynamic reliability analysis of nonlinear structures using a Duffing-system-based equivalent nonlinear system method

Time interval of multiple crossings of the Wiener process and a fixed threshold in engineering

In this paper, a new method for durability life prediction of reinforced concrete structures in a chlorine-eroded environment is presented based on the Gamma process. The Gamma process mode...

Zhenhao Zhang

Papers

Application of probabilistic method in maximum tsunami height prediction considering stochastic seabed topography

Analysis of stochastic process to model safety risk in construction industry

Dynamic reliability analysis of nonlinear structures using a Duffing-system-based equivalent nonlinear system method

Time interval of multiple crossings of the Wiener process and a fixed threshold in engineering

Durability Life Prediction of Reinforced Concrete Structure Corroded by Chloride Based on the Gamma Process