Structural assessment under uncertain parameters via the interval optimization method using the slime mold algorithm

Damage detection of civil and mechanical structures based on measured modal parameters using model updating schemes has received increasing attention in recent years. In this study, for uncertainty-oriented damage identification, a non-probabilistic structural damage identification (NSDI) technique based on an optimization algorithm and interval mathematics is proposed. In order to take into account the uncertainty quantification, the elastic modulus is described as unknown-but-bounded interval values and the proposed new scheme determines the upper and lower bounds of the damage index. In this method, the interval bounds can provide supports for structural health diagnosis under uncertain conditions by considering the uncertainties in the variables of optimization algorithm. The model updating scheme is subsequently used to predict the interval-bound of the Elemental Stiffness Parameter (ESP). The slime mold algorithm (SMA) is used as the main algorithm for model updating. In addition, in this study, an enhanced variant of SMA (ESMA) is developed, which removes unchanged variables after a defined number of iterations. The method is implemented on three well-known numerical examples in the domain of structural health monitoring under single damage and multi-damage scenarios with different degrees of uncertainty. The results show that the proposed NSDI methodology has reduced computation time, by at least 30%, in comparison with the probabilistic methods. Furthermore, ESMA has the capability to detect damaged elements with higher certainty and lower computation cost in comparison with the original SMA..Peer ReviewedPostprint (published version

From model-driven to data-driven : a review of hysteresis modeling in structural and mechanical systems

Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems. The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous methods have been developed to describe hysteresis. In this paper, a review of the available hysteretic modeling methods is carried out. Such methods are divided into: a) model-driven and b) datadriven methods. The model-driven method uses parameter identification to determine parameters. Three types of parametric models are introduced including polynomial models, differential based models, and operator based models. Four algorithms as least mean square error algorithm, Kalman filter algorithm, metaheuristic algorithms, and Bayesian estimation are presented to realize parameter identification. The data-driven method utilizes universal mathematical models to describe hysteretic behavior. Regression model, artificial neural network, least square support vector machine, and deep learning are introduced in turn as the classical data-driven methods. Model-data driven hybrid methods are also discussed to make up for the shortcomings of the two methods. Based on a multi-dimensional evaluation, the existing problems and open challenges of different hysteresis modeling methods are discussed. Some possible research directions about hysteresis description are given in the final section

Determination of water quality characteristics of Shahid Rajaei reservoir (Sari) based on physic-chemical parameters

The water quality provides the valuable information about the available resources for human usage. The reservoirs are the important resources of surface water which could be considered as an appropriate water resource for irrigation, drinking water and also fish culturing. The Shahid Rajaei Reservoir- Sari is an important reservoir in Iran, which conducted to study on its water quality in this survey. In this study, some of the physicochemical parameters and Chlorophyl- a of Shahid Rajaei reservoir were measured at 4 stations (Shirin Roud branch, Sefid Roud branch, the crossing point of branches, near the tower) during six sampling months (June, July, August, September, November and February) in 2012-2013. The water quality and trophic status of reservoir calculated based on some reference values and the modified Carlson formula. The results showed that the mean (±Standard Error) of temperature, dissolved oxygen, pH, phosohate, amonium and nitrate concentrations and Chlorophyl a were 21.35 (±1.30) ºC, 10.48 (±0.37), 8.54 (±0.04), 0.050 (±0.004), 0.036 (±0.004), 0.75 (±0.03) mg/l and 18.00 (±7.23) mg/m^3 , respectively. In the present study, temperature between surface and deep layer was stratified in June and July, which the stratification was registerd 0.47 and 0.69 °C decreases with increasing of each meter depth in 15 to 30 meter culumn. But, these changes for each increasing meter of water depth were 0.2 to 0.26 °C in August and September, respectively, and finally was close to zero in November. In the warm months (July, August and September) with the formation of thermal stratification in the reservoir was formed oxygen stratification, but in the cold season (November and February), with vertical mixing of water oxygen and percent saturation of the reservoir was nearly homogeneous. The results showed that the European authorities (OECD) trophic status varied between mezotrophic to hypertrophic during the sampling period at all stations. The comparison with the values listed in the references of Iranian dams based on transparency and chlorophyll variables showed similar results. However, phosphorus variable (due to limited for phytoplankton) was not showing the true conditions of trophic status. As a conlusion, trophic status of Shahid Rajaei dam based on Carlson trophic index (TSI) was obtained oligotrophic (May and October), mezotrophic (February) and eutrophic (August and September) condintion during diferent months. Therefore, water management of the reservoir was more attention during warm months

Trout farms and other human activities effects on Cheshmehkileh river ecosystem in Tonekabon

Cheshmehkileh River and adjacent mountainous streams, play a strategic role as a historical axis for anthropogenic civilization, human welfare also habitat and migration pathway of commercial – biologic valuable fishes e.g. Caspian trout, Caspian kuttum, members of Cyprinidae family in south Caspian Sea drainage. Treats such as overfishing of Caspian trout and Red spotted trout stocks in mountainous headwaters, barriers construction and manipulations those are out of river carrying capacity developed by human activities, affected normal function of river as well. Sand mining big factories establishment next to the river, legal and illegal trade of river sediments, direct entry of Tonekabon landfill leakage into the river, development of Rainbow trout farms since 3 decades and huge effluents into the river containing dead fish and types of solids, escapement of cultured Rainbow trouts into the river, … are major minimum factors which needs basic information for integrating inclusively drainage management system. Cheshmehkileh River contains Headwaters of Dohezar (Daryasar & Nusha), Sehezar and Valamroud rivers during 13 monthly sampling phases between September 2009 and October 2010 based on macrozoobenthoses investigations by EPT, EPT/C EPA protocols, measurements of nominated physic-chemical and microbiologic parameters. Probability of Rainbow trouts escapement and invasion, existence, nutrition in Cheshmehkileh environment indeed investigated. Data analysis explained significant differences (P<0.05) between groups of measured parameters in different sampling stations. Dendogram of clustered analysis based on consolidation of major biologic/ physic-chemical and microbiologic parameters, separated stations No. 1, 3, 2, 4 in one group and remained classified in different groups. Station 8 and 9 similarly separated which expressed general similarities according to Sehezar river environment which were differs in comparison with other stations. Station 11 separated according to its natural quality of water and environment. Similarities between station 10 to Sehezar river stations 8 and 9 expressed general influence of Sehezar River more than Dohezar River in Cheshmehkileh condition especially in station No. 10. High scores of EPT and EPT/C indices in upstream stations 1, 3 and 8 also low score of indices in stations 7, 13 and 6 expressed levels of environment quality between these groups of stations. Maximum average biomass of macroinvertebrates belongs to Trichoptera order in Cheshmehkileh River. Significant decrease of biomass in stations 11, 12 and 13 in comparison with other stations stated environment degradation in mentioned stations relevant to excessive sand mining as well. Pollution resistant groups of invertebrates significantly increased in downstreams against upstream stations. Also disappearing of Plecoptera order in station No. 7, 9, 10 and 13 stated low quality of environment in comparison with upstream stations. Confirmation of effects quality and quantity for point and non-point sources of imported pollutants require specific management considerations in order to present exploitations, pollutants control and emergencies for river monitoring in forthcoming years

Optimum feature selection for SHM of benchmark structures using efficient AI mechanism

Structural Health Monitoring (SHM) is rapidly developing as a multi-disciplinary technology solution for condition assessment and performance evaluation of civil infrastructures. It consists of three parts: Data collection, data processing (feature extraction/selection), and decision-making (feature classification). In this research, for effectively reducing a dimension of SHM data, various methods are proposed such as advanced feature extraction, feature subset selection using optimization algorithm, and effective surrogate model based on artificial intelligence methods. These frameworks enhance the capability of the SHM process to tackle with uncertainties and big data problem. To reach such goals, a framework based on three main blocks are proposed here: Feature extraction block using wavelet pocket relative energy (WPRE), feature selection block using improved version of binary harmony search algorithm and finally feature classification block using wavelet weighted least square support vector machine (WWLS-SVM). The capability of the proposed framework is compared with various well known methods for each block. Results will be presented using metrics of precision, recall, accuracy and feature-reduction. Furthermore, to show the robustness of the proposed methods, six well-known benchmark datasets of SHM domain are studied. The results validate the suitability of the proposed methods in providing data reduction and accelerating damage detection process.</p

Deep Learning-Based Damage, Load and Support Identification for a Composite Pipeline by Extracting Modal Macro Strains from Dynamic Excitations

Modal macro strain-based damage identification is a promising approach since it has the advantages of high sensitivity and effectiveness over other related methods. In this paper, a basalt fiber-reinforced polymer (BFRP) pipeline system is used for analysis by using long-gauge distributed fiber Bragg grating (FBG) sensors. Dynamic macro strain responses are extracted to form modal macro strain (MMS) vectors. Both longitudinal distribution and circumferential distribution plots of MMS are compared and analyzed. Results show these plots can reflect damage information of the pipeline based on the previous work carried out by the authors. However, these plots may not be good choices for accurate detection of damage information since the model is 3D and has different flexural and torsional effects. Therefore, by extracting MMS information in the circumferential distribution plots, a novel deep neural network is employed to train and test these images, which reflect the important and key information of modal variance in the pipe system. Results show that the proposed Deep Learning based approach is a promising way to inherently identify damage types, location of the excitation load and support locations, especially when the structural types are complicated and the ambient environment is changing

A data-driven approach for scour detection around monopile-supported offshore wind turbines using Naive Bayes classification

This paper proposes a novel data-driven framework for scour detection around offshore wind turbines (OWTs), where damage features are derived from wind and wave-induced acceleration signals collected along the tower. A numerical model of the NREL 5 MW wind turbine, which considers aerodynamic and hydrodynamic loading with soil-structure interaction (SSI) and servo-dynamics, is developed. The model is used to simulate the acceleration responses along the tower for a healthy structure, and a structure affected by progressive scour. A data segmentation process is initially performed on the collected data, which is followed by a feature selection scheme based on the analysis-of-variance (ANOVA) algorithm, to eliminate irrelevant characteristics from the time domain feature set of responses. The proposed framework consists of two main components: (a) offline training, and (b) real-time classification. The acceleration responses collected from the healthy structure and the structure subjected to three different damage scenarios (different scour depths) and under various load conditions, are used in the offline training mode. The selected feature vector from the feature extraction process is used as input to a Naive Bayes classifier (NBC) algorithm to train the model. In the real-time classification, a prediction of the scour depth affecting the structure is performed using a new dataset simulated from unseen load cases and scour conditions of the OWT. The results show that the model trained in the offline stage can predict the scour depth in the real-time monitoring stage with performance measures over approximately 94%