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Crack detection is a vital component of structural health monitoring. Several computer vision-based studies have been proposed to conduct crack detection on concrete surfaces, but most cases have difficulties in detecting fine cracks. This study proposes a deep learning-based model for automatic crack detection on the concrete surface. Our proposed model is an encoder–decoder model which uses EfficientNet-B7 as the encoder and U-Net's modified expansion path as the decoder. To overcome the challenges in the detection of fine cracks, we trained our model with a new training strategy on images extracted from an open-access dataset and achieved a 96.98% F1 score for unseen test data. Moreover, we evaluated our method on CrackForest Dataset and achieved a 97.06% F1 score which outperforms all the existing methods. The robustness of the proposed model is investigated using the various numbers of training data, and the optimal data size for training this model is presented. The results show that although deep learning models acquire a large number of data, this model works with limited data, without any degradation in its performance. Furthermore, the novel training strategy used in this study, significantly improves the model's accuracy in detecting different types of cracks. [ABSTRACT FROM AUTHOR]

Introduction. Recognizing factors affecting job motivation can be very helpful in increasing efficiency and job satisfaction of university faculty members. In order to Identify the internal and external factors of job motivation, this study was conducted to determine the effective factors on job motivation of faculty members in Rafsanjan Medical University. Methods. In a descriptive study, all faculty members of Rafsanjan Medical University (n=82) completed a questionnaire developed by the investigator based on Herzberg’s theory and consisted of 40 questions. The validty and relability of the quessionnaire was confirmed by content validity and test re-test. The data was analyzed by SPSS software using frequency distribution and chi2. Results. Eighty one percent of faculty members considered external factors and 72% considered internal factors important in job motivation. Among the external factors, wage and sallary, job security, work environment, communication with others, supervision and the work policy had the most important role, respectively. The most important internal factors included nature of the job, appreciation of employees, professional development and job success, respectively. Conclusion. Based on the faculty members’ view points, the external factors (hygiene factors) compared with the internal factors (motivation factors) had a more important role in job motivation which does not conform with the Herzberg’s view. Sallary and job security were the most important motivation factors.

Structural control using energy dissipater devices is emerging as a heavily researched strategy in earthquake engineering. Among several control systems, semiactive control is usually possible and efficient. In this research, a semiactive energy dissipating bracing system based on a viscous damper is proposed. In the conventional bracing systems, it is assumed that the braces can buckle under compression. Therefore, a semiactive on-off brace strategy is implemented to improve the conventional brace performance. Further, an energy absorbing mechanism is implemented. In the proposed system, the buckling of the member is prevented by implementing a one-way valve device. The permanent story drifts at the end of excitations are negligible. In this mechanism, any actuator and/or large power supply are not needed, instead, just battery-size power supplies to switch the one-way valve mechanical system on or off. The developed mechanism is composed of a viscous damper, a length correction control system, and a normal wind bracing on each floor. Despite the fact that this system does not get control command from the outside, it is called a semiactive control system. The hysteresis loop of the braces is almost similar to ductile tensile steel members. Therefore, the system is full capacity design. Nonlinear dynamic analysis of the system has been carried out and structural behavior has been investigated. In addition, damage indices are determined in order to compare the viscous tensile bracing system with the conventional bracing systems. CE Database subject headings: Structural control; Earthquake loads; Damage; Deformation; Buckling; Ductility; Damping; Bracing.

Online selection of the appropriate model and identifying its parameters based on measured vibrational data are among the challenging issues in dynamic system identification. After a severe earthquake, quick monitoring and assessment of structural health status play a crucial role in effective critical risk management for the building owners and decision-makers. The Bayesian multiple modeling approach is a suitable tool for optimal model class selection, which is used in this article mainly for improving data fitting precision, decreasing dimensions of structural unknown vector through removing unnecessary parameters, detecting the occurrence and type of predominant phenomenon related to degradation and pinching, and finally increasing stability and convergence rate of the used identification algorithm. In this study, a reliable and effective time-domain method is proposed for online probabilistic model class selection and joint estimation of structures using the central difference Kalman filter and Bayesian multiple modeling approach. Also, in contrast to existing studies, the performance and efficiency of the proposed algorithm are investigated in nonlinear system identification with a large number of unknowns. The proposed algorithm is implemented on three moment-frame buildings with 8, 54, and 60 unknowns. In addition, a numerical example is provided to analyze the case in which the exact model is not among the plausible models. To verify the performance of the hybrid identification method, robust simulations with synthetic measurement noises and modeling errors were generated using the Monte Carlo random simulation method. The result shows the method can be used to simultaneously select the optimal model class and identify its unknown states and parameters in an online manner. [ABSTRACT FROM AUTHOR]

Rapid assessment of structural safety and performance right after the occurrence of significant earthquake shaking is crucial for building owners and decision-makers to make informed risk management decisions. Hence, it is vital to develop online and pseudo-online health monitoring methods to quantify the health of the building right after significant earthquake shaking. Many Bayesian inference--based methods have been developed in the past which allow the users to estimate the unknown states and parameters. However, one of the most challenging part of the Bayesian inference--based methods is the determination of the parameter noise covariance matrix. It is especially difficult when the number of unknown states and parameters is large. In this study, an effective online joint estimation method for nonlinear hysteretic structures, with consideration of degradation and pinching phenomena, is proposed. Simultaneous estimation of states and parameters is conducted using the combination of a central difference Kalman filter as an effective estimator and the Robbins--Monro stochastic approximation technique as the parameters noise covariance matrix regulator. The proposed algorithm is implemented on three shear buildings with 36, 54, and 90 unknown states and parameters. To verify the performance of the system identification method, robust simulations with synthetic measurement noises and modeling errors were generated using the Monte Carlo random simulation method. The result shows the proposed method can be used to estimate the unknown parameters and states of highly nonlinear systems efficiently and effectively. [ABSTRACT FROM AUTHOR]

This paper presents the design and the application of a new self-powered hybrid electromagnetic damper that can harvest energy while mitigating the vibration of a structure. The damper is able to switch between an energy harvesting passive mode and a semi-active mode depending on the amount of energy harvested and stored in the battery. The energy harvested in the passive mode resulting from the suppression of vibration is employed to power up the monitoring and electronic components necessary for the semi-active control. This provides a hybrid control capability that is autonomous in terms of its power requirement. The proposed hybrid circuit design provides two possible options for the semi-active control: without energy harvesting and with energy harvesting. The device mechanism and the circuitry that can drive this self-powered electromagnetic damper are described in this paper. The parameters that determine the device feasible force-velocity region are identified and discussed. The effectiveness of this hybrid damper is evaluated through a numerical simulation study on vibration mitigation of a bridge stay cable under wind excitation. It is demonstrated that the proposed hybrid design outperforms the passive case without external power supply. It is also shown that a broader force range, facilitated by decoupled passive and semi-active modes, can improve the vibration performance of the cable. [ABSTRACT FROM AUTHOR]

This paper presents the results of an experimental work in order to evaluate the performance of a novel proposed retrofitting technique on a typical dome-roof adobe building by shaking table tests. For this purpose, two specimens, scaled 2:3, were subjected to a total of nine shaking table tests. The unretrofitted specimen, constructed by common practice, is designed to evaluate seismic performance and vulnerability of dome-roof adobe houses. The retrofitted specimen, exactly duplicating the first specimen, is retrofitted based on the results obtained from unretrofitted specimen tests, and the improvement in seismic behavior of the structure is investigated. Zarand earthquake (2005) Chatrood Station is selected as the input ground motion that was applied consecutively at 25, 100, 125, 150 and 175% of the design-level excitation. At 125% excitation level, the roof of the unretofitted specimen collapsed due to the walls' out-of-plane action and imbalanced forces. The retrofitting elements consist of eight horizontal steel rods drilled into the walls, passed through the specimen and bolted on the opposite wall surfaces. To improve walls in-plane seismic performance, welded steel mesh without using mortar, covered less than half area of walls on the external face of the walls, is used. In addition to strain gauges for recording steel rod responses, several instrumentations including acceleration and displacement transducers are implemented to capture response time histories of different parts of the specimens. The corresponding full-scaled retrofitted prototype tolerated peak acceleration of 0.62 g almost without any serious damage. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study investigated the factor structure and measurement invariance of the Mroczek and Kolarz's scales of positive and negative affect in Iran (N - 2,391) and the USA (N = 2,154), and across gender groups. The two-factor model of affect was supported across the groups. The results of measurement invariance testing confirmed full metric and partial scalar invariance of the scales across cultural groups, and full metric and full scalar invariance across gender groups. The results of latent mean analysis revealed that Iranians scored lower on positive affect and higher on negative affect than Americans. The analyses also showed that American men scored significantly lower than American women on negative affect. The significance and implications of the results are discussed. [ABSTRACT FROM AUTHOR]