No abstract

The terms “hotspots,” “blackspot,” or “high‐risk” are normally used for an area on a road where the number of car accidents are far beyond the normal level and, more importantly, the causes of the incident always link to one or many specific reasons. Identifying these spots is helpful and may assist people in authority to implement corrective actions. To do the aforementioned, some decision‐support tools could be useful since they enable us to monitor the indicators that may lead to a car accident, same as the procedure engineers use to control production lines in factories. In this regard, control charts are utilized to identify blackspots. Although the method is low cost, and provides quick detection and statistical reliability level and, more importantly, uses online applications; however, it advised that the rate quality control method could be used on roads with the same geometry and traffic conditions. This research develops a hybrid method, based on accident type, for improving rate quality control method to overcome the shortcomings. Furthermore, the suggested framework considers crash severity and traffic volume that have not been taken into account in the rate quality control method yet. The case study evaluates how this framework is successful to identify blackspots, and based on the result in the real environment, it reveals that the proposed method could detect and identify the hotspots.

In inverse methods of damage detection, it is generally assumed that the damage values and imperfect vibrational information are predefined scenarios. In fact, imperfect vibrational information is obtained based on acquired data from installed sensors on real structures. Then the location and intensity value of damage is calculated using inverse optimisation methods. In this paper, a two-stage method is proposed to eliminate the assumed imperfection of the first stage and accurately calculate its required damage information using a numerical–experimental method. Then, with the information on the damaged pier, the mode shapes are obtained and the total degrees of freedom is compacted as needed to specific degrees of freedom (having installed sensors on the real structure). Finally, introducing the objective function in Matlab for the bridge pier, the variable (stiffness reduction percentage) is inversely calculated using the linear optimisation simplex method. Agreement between the experimental and numerical results shows that the proposed method can be precisely applied for damage detection and quantification of reinforced concrete bridge piers. The validation of the proposed method indicates 0·4% error considering three expected vibrational modes.

Today, environmental health needs more attention than in the past. Objects consisting of plastic materials such as bottles take many years to decompose. Therefore, in the recent years, a lot of research has been done to reuse them and one of which is use in the concrete mixes. In previous research, very low percentages of these materials (between 0.5% and 1.5%) has been used as fine aggregate substitutes in the concrete and evaluations have more focused on the flexural strength. In this research, Polyethylene terephthalate (PET) as a substitute for sand and Polypropylene fibers (PP) as an additive material are used in the concrete mixes. 14 concrete beams in both reinforced and unreinforced states have been evaluated. The replacement percentage of the PET is 10, 30 and 60% and additive percentage of the PP is 0.5, 1.5 and 3% of the total weight of concrete. The dimensions of the beams have been chosen in such a way to categorized in the deep beams. So, rectangular cross-section has been considered (width = 100mm and height = 400mm). Shear strength and ductility are two important parameters that have been evaluated in this research.