Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries.

COVID-19 or SARS-Cov-2, affecting 6 million people and more than 300,000 deaths, the global pandemic has engulfed more than 90% countries of the world. The virus started from a single organism and is escalating at a rate of 3% to 5% daily and seems to be a never ending process. Understanding the basic dynamics and presenting new predictions models for evaluating the potential effect of the virus is highly crucial. In present work, an evolutionary data analytics method called as Genetic programming (GP) is used to mathematically model the potential effect of coronavirus in 15 most affected countries of the world. Two datasets namely confirmed cases (CC) and death cases (DC) were taken into consideration to estimate, how transmission varied in these countries between January 2020 and May 2020. Further, a percentage rise in the number of daily cases is also shown till 8 June 2020 and it is expected that Brazil will have the maximum rise in CC and USA have the most DC. Also, prediction of number of new CC and DC cases for every one million people in each of these countries is presented. The proposed model predicted that the transmission of COVID-19 in China is declining since late March 2020; in Singapore, France, Italy, Germany and Spain the curve has stagnated; in case of Canada, South Africa, Iran and Turkey the number of cases are rising slowly; whereas for USA, UK, Brazil, Russia and Mexico the rate of increase is very high and control measures need to be taken to stop the chains of transmission. Apart from that, the proposed prediction models are simple mathematical equations and future predictions can be drawn from these general equations. From the experimental results and statistical validation, it can be said that the proposed models use simple linkage functions and provide highly reliable results for time series prediction of COVID-19 in these countries

Structural Health Monitoring in Composite Structures: A Comprehensive Review.

This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented

Consolidation assessment using Multi Expression Programming

© 2019 Elsevier B.V. In this study, new approximate solutions for consolidation have been developed in order to hasten the calculations. These solutions include two groups of equations, one can be used to calculate the average degree of consolidation and the other one for computing the time factor (inverse functions). Considering the complicated nature of consolidation, an evolutionary computation technique called Multi-Expression Programming was applied to generate several non-piecewise models which are accurate and straightforward enough for different purposes for calculating the degree of consolidation for each depth and its average as well for the whole soil layer. The parametric study was also performed to investigate the impact of each input parameter on the predicted consolidation degree of developed models for each depth. Moreover, the results of the consolidation test carried out on four different clays attained from the literature showed the proper performance of the proposed models

A Shannon entropy approach for structural damage identification based on self-powered sensor data

© 2019 Elsevier Ltd Piezo-floating-gate (PFG) sensors are a class of self-powered sensors fabricated using piezoelectric transducers and p-channel floating-gate metal-oxide-semiconductor (pMOS) transistors. These sensors are equipped with a series of floating-gates that are triggered when the voltage generated by the piezoelectric transducers exceeds one of the specified thresholds. Upon activation, the floating-gates cumulatively store the duration of the applied strain events. Defining optimal voltage thresholds plays a key role in the efficiency of the PFG sensors for structural damage identification. In this paper, symbolic dynamic analysis (SDA) based on Shannon entropy is used to find the effective voltage thresholds that ensure the maximum detectability of the structural damage-related changes. To this end, a baseline is constructed using the strain data obtained from the undamaged structure. These data are used to set the voltage threshold on every floating gate of the sensor. Then the posterior state of the structure is monitored using thresholds set up on the baseline and a cumulative density function (CDF) of strain events. In order to determine the damage severity, a damage index is defined based on the Euclidean norm of the distance between the CDFs for the damaged and healthy structure. The proposed technique is verified using experimental data for a steel plate subjected to an in-plane tension loading. The results confirm the capability of the proposed method in monitoring structures for damage initiation and/or propagation using the PFG sensors, and the CDFs on which the damage sensitive feature (DSF) is based can provide additional insights into the stress distributions

CCSA: Conscious Neighborhood-based Crow Search Algorithm for Solving Global Optimization Problems

© 2019 Elsevier B.V. In this paper, a conscious neighborhood-based crow search algorithm (CCSA) is proposed for solving global optimization and engineering design problems. It is a successful improvement to tackle the imbalance search strategy and premature convergence problems of the crow search algorithm. CCSA introduces three new search strategies called neighborhood-based local search (NLS), non-neighborhood based global search (NGS) and wandering around based search (WAS) in order to improve the movement of crows in different search spaces. Moreover, a neighborhood concept is defined to select the movement strategy between NLS and NGS consciously, which enhances the balance between local and global search. The proposed CCSA is evaluated on several benchmark functions and four applied problems of engineering design. In all experiments, CCSA is compared by other state-of-the-art swarm intelligence algorithms: CSA, BA, CLPSO, GWO, EEGWO, WOA, KH, ABC, GABC, and Best-so-far ABC. The experimental and statistical results show that CCSA is very competitive especially for large-scale optimization problems, and it is significantly superior to the compared algorithms. Furthermore, the proposed algorithm also finds the best optimal solution for the applied problems of engineering design

Wood hole-damage detection and classification via contact ultrasonic testing

Damage detection in wood materials has numerous applications in different industries, such as construction and forestry. Wood is generally a complex medium due to its orthotropic and random properties, which increases the difficulty of non-destructive damage testing. However, machine learning algorithms can be employed to overcome this problem. In this paper, hole-defect classification problems of two common types of wood materials, namely hard (marbau) and soft (pine) wood, are studied using a naive Bayes classification technique. To this end, the results of contact ultrasonic tests conducted on these types of woods in different directions, i.e. tangential and radial to the growth rings of wood, were investigated. The various states of the intact, small defect, and large defect of each type of wood were considered in the testing regime. It is known that contact ultrasonic tests are highly sensitive to different aspects of the test, such as the amount of couplant gel applied to surfaces, the amount of pressure applied to the transducer and receiver, and misalignment of the transducer and receiver. Therefore, 50 replicates of each test were implemented. First, an advanced signal decomposition algorithm termed Variational Mode Decomposition (VMD) was exploited to derive some features from the recorded ultrasonic signals. Then, the derived features were used in a set of classification problems using a naive Bayes classifier to classify the damage state of the specimens. Different types of naive Bayes classifiers, namely Gaussian and kernel, along with combinations of different types of features were employed to improve the results, ultimately achieving nearly 100% 10-fold cross-validation accuracy in all cases individually. However, when cases from different types of wood and direction of the tests were mixed, 93.6% 10-fold cross-validation accuracy was achieved for the classification problem based on the health state of the cases, using kernel naive Bayes classifier and a mixture of two types of features

Experimental dataset on water levels, sediment depths and wave front celerity values in the study of multiphase shock wave for different initial up- and down-stream conditions

This data article presents a rich original experimental video sources and wide collections of laboratory data on water levels, sediment depths and wave front celerity values arose from different multiphase dam-break scenarios. The required data of dam-break shock waves in highly silted-up reservoirs with various initial up- and down-stream hydraulic conditions is obtained directly from high-quality videos. The multi-layer shock waves were recorded by three professional cameras mounted along the laboratory channel. The extracted video images were rigorously scrutinized, and the datasets were obtained through the images via image processing method. Different sediment depths in the upstream reservoir and dry- or wet-bed downstream conditions were considered as initial conditions, compromising a total of 32 different scenarios. A total of 198 original experimental videos are made available online in the public repository "Mendeley Data" in 8 groups based on 8 different initial upstream sediment depths [1], [2], [3], [4], [5], [6], [7], [8]. 20 locations along the flume and 15 time snaps after the dam breaks were considered for data collecting. Consequently, a total of 18,000 water level and sediment depth data points were collected to prepare four datasets, which are uploaded in the public repository "Mendeley Data". A total of 9600 water level data points could be accessed in [9], [10], while 8400 sediment depth data points are available online in [11], [12] and could be utilized for validation and practical purposes by other researchers. This data article is related to another research article entitled "Experimental study and numerical verification of silted-up dam-break" [13]

Prediction of peak ground acceleration of Iran's tectonic regions using a hybrid soft computing technique

A new model is derived to predict the peak ground acceleration (PGA) utilizing a hybrid method coupling artificial neural network (ANN) and simulated annealing (SA), called SA-ANN. The proposed model relates PGA to earthquake source to site distance, earthquake magnitude, average shear-wave velocity, faulting mechanisms, and focal depth. A database of strong ground-motion recordings of 36 earthquakes, which happened in Iran's tectonic regions, is used to establish the model. For more validity verification, the SA-ANN model is employed to predict the PGA of a part of the database beyond the training data domain. The proposed SA-ANN model is compared with the simple ANN in addition to 10 well-known models proposed in the literature. The proposed model performance is superior to the single ANN and other existing attenuation models. The SA-ANN model is highly correlated to the actual records (R = 0.835 and ρ = 0.0908) and it is subsequently converted into a tractable design equation