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Job stress is considered one of the critical causes of construction workers’ unsafe behaviors. As a mainstay industry in many countries, the construction industry has a considerable number of employees and the research on how job stress affects workers’ unsafe behaviors has important theoretical and practical significance to improve construction safety performance through better job stress management. In this study, the authors thoroughly reviewed the literature and conducted semi-structured interviews to identify the dimensions of job stress, designed the job stress scale and cited the safety behavior measurement scale. After that, a questionnaire survey was developed using the proposed measurement scale and distributed to the construction employees from a project in Beijing. One hundred fifty responses were collected and analyzed using reliability analysis to validate the scale’s internal consistency. Results from factor analysis indicate that the scales of job stress measurement can be grouped into six dimensions. To demonstrate the applicability of the developed scale on construction safety management research, the collected data was used to test the hypothesis that job stress has a negative correlation with safety behavior. Results show that the hypothesis is valid, and there is a negative correlation between job stress and safety behavior. In addition, finer results of the relationship between the six dimensions of job stress and safety behavior can be obtained. In summary, this study developed an improved stress scale for construction workers in China, and the proposed scale was validated by analyzing the data from an empirical study in Beijing.

An improved formula for critical buckling forces has been derived. This formula, which takes the well curvature into account, has been verified in small scale laboratory tests. The theory has been applied to survey data from a real horizontal well and it predicts that the well curvature substantially affects the critical force for helical buckling, and thereby also the maximum run-in length of coiled tubing. Criteria for operational limits, such as lock-up and tubing failure, are also discussed in the paper. Introduction Coiled tubing has numerous applications in well technology. Coiled tubing has been found useful for logging, well clean outs, well stimulation, gas lift and cementing. Encouraging attempts at drilling with coiled tubing have recently been carried out. Coiled tubing is also used as a stiff wireline for a number of tool operations in highly deviated wells. One of the main limitations associated with coiled tubing is assumed to be helical buckling and the additional wall friction force generated by buckling. When axial compression forces over a critical value are applied on coiled tubing, the coiled tubing will buckle. The coiled tubing will first buckle into a sinusoidal wave shape. As the compression force increases further, it will ultimately deform into a helix. Confined to the wellbore, the helically buckled coiled tubing will be forced against the wall of wellbore and additional contacting forces developed. The force needed to push coiled tubing into a well increases dramatically once the coiled tubing is forced into a helix. The frictional drag developed as coiled tubing is forced against the hole or casing wall will ultimately overcome the pushing forces. This phenomena is called lock-up. The critical buckling force in inclined well sections is currently determined by the Dawson formula for sinusoidal buckling and the Chen et al formula for helical buckling. These formula are currently used as the operational criteria for coiled tubing. In practice, it is often found that the operational force can be significantly larger than the theoretical buckling force, and the operations still be successful. There is considerable field evidence that using the critical buckling force as operational limit is too conservative. Two major shortcomings exist for these formulas and the way buckling analysis is performed:–The well curvature effects on the critical buckling force are not considered in these formulas.–The previously published analysis method is restricted to subcritical forces and no post-buckling is considered. It is incorrectly assumed that operations are not feasible if the axial compression force anywhere exceeds the critical buckling force. The above two points have been addressed in a recent publication, but no detailed information is given.

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The intelligent crack detection method is an important guarantee for the realization of intelligent operation and maintenance, and it is of great significance to traffic safety. In recent years, the recognition of road pavement cracks based on computer vision has attracted increasing attention. With the technological breakthroughs of general deep learning algorithms in recent years, detection algorithms based on deep learning and convolutional neural networks have achieved better results in the field of crack recognition. In this paper, deep learning is investigated to intelligently detect road cracks, and Faster R-CNN and Mask R-CNN are compared and analyzed. The results show that the joint training strategy is very effective, and we are able to ensure that both Faster R-CNN and Mask R-CNN complete the crack detection task when trained with only 130+ images and can outperform YOLOv3. However, the joint training strategy causes a degradation in the effectiveness of the bounding box detected by Mask R-CNN.

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