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Cracks of civil infrastructures, including bridges, dams, roads, and skyscrapers, potentially reduce local stiffness and cause material discontinuities, so as to lose their designed functions and threaten public safety. This inevitable process signifier urgent maintenance issues. Early detection can take preventive measures to prevent damage and possible failure. With the increasing size of image data, machine/deep learning based method have become an important branch in detecting cracks from images. This study is to build an automatic crack detector using the stateoftheart technique referred to as Mask Regional Convolution Neural Network (RCNN), which is kind of deep learning. Mask RCNN technique is a recently proposed algorithm not only for object detection and object localization but also for object instance segmentation of natural images. It is found that the built crack detector is able to perform highly effective and efficient automatic segmentation of a wide range of images of cracks. In addition, this proposed automatic detector could work on videos as well; indicating that this detector based on Mask RCNN provides a robust and feasible ability on detecting cracks exist and their shapes in real time onsite.

Bridge WeighinMotion (BWIM) is the concept of using measured strains on a bridge to calculate the axle weights of trucks as they pass overhead at full highway speed. There exist a consensus that conventional instrumentation faces substantial practical problems that halts the feasibility of this theory, namely cost, installation time and complexity. This article will go through a new concept by moving toward the first Portable Bridge WeighInMotion (PBWIM) system. The system introduce flying sensor concept which consist of a swarm of drones that have accelerometers and able to latch bridge girders to record acceleration data. Some perching mechanisms have been introduce in this paper to allow drones to latch bridges girders. At the same time, a new algorithm is developed to allow the BWIM system to use the acceleration data to estimate the truck weigh instead of the strain measurements. The algorithm uses the kalmanfilterbased estimation algorithm to estimate the state vectors (displacement and velocities) using limited measured acceleration response (from drones). The estimated state vector is used to feed a moving force identification (MFI) algorithm that shows good results in estimating a quarter car model weight.

Bridge WeighinMotion (BWIM) is the concept of using measured strains on a bridge to calculate the static weights of passing traffic loads as they pass overhead at full highway speed. Weight calculations should have a high level of accuracy to enable the BWIM system from being a tool for direct overload enforcement. This paper describes the experimental testing of the BWIM system based on moving force identification (MFI) theory. The bridge was instrumented by wireless accelerometers and strain gages attached to the girders to measure the dynamics response when the calibrated trucks pass the bridge. LSDyna finite element program is used to imitate the 3D bridge model, which validated utilizing the collected acceleration data. Then measurements from the wireless strain sensors are utilized to run the (MFI) algorithm and calculate the truck weight.

Bridge WeighinMotion (BWIM) is the technology of using the bridge as a weigh scales to find the weights of passing trucks. Weight calculations should have a high level of accuracy to enable the BWIM system from being a tool for direct overload enforcement. This paper focuses on improving the accuracy of the BWIM system when a passenger vehicle travels over the bridge side by side with the target truck. A solution has been suggested to remove this effect by considering measurements on girders under the truck lane only and exclude the ones under the passenger vehicle lane. Since using the measurements under the vehicle lane will reduce measurements number and thus affect results accuracy, an approximate method are developed to deal with the lower number of measurements. The method has been discussed analytically and experimentally.

Bridge WeighinMotion (BWIM) is the concept of using measured response on a bridge to calculate the static weights of passing traffic loads as they pass overhead at full highway speed. This paper describes an enhancement to the Moving Force Identification (MFI) algorithm by estimating the response of some DOFs using limited number of measurements in order to increase measurements number (Input). The pseudoinverse of the mode shape matrix has been utilized to approximately calculate the modal response using limited measured response. Then the calculated modal response has been used to estimate more DOFs that are different from the measured one. The proper orthogonal decomposition (POD) technique is employed to determine the governing modes that increase the modal response accuracy. Numerical example for quarter car model passing over simply supported bridge has been established to demonstrate the idea.

Bridge WeighinMotion (BWIM) is the concept of using measured strains on a bridge to calculate the static weights of passing traffic loads as they pass overhead at full highway speed. Weight calculations should have a high level of accuracy to enable the BWIM system from being a tool for direct overload enforcement. This paper describes the experimental testing of the BWIM system based on moving force identification (MFI) theory. The bridge was instrumented by wireless accelerometers and strain gages attached to the girders to measure the dynamics response when the calibrated trucks pass the bridge. LSDyna finite element program is used to imitate the 3D bridge model, which validated utilizing the collected acceleration data. Then measurements from the wireless strain sensors are utilized to run the (MFI) algorithm and calculate the truck weight.

Bridge WeighinMotion (BWIM) is the technology of using the bridge as a weigh scales to find the weights of passing trucks. Weight calculations should have a high level of accuracy to enable the BWIM system from being a tool for direct overload enforcement. This paper focuses on improving the accuracy of the BWIM system when a passenger vehicle travels over the bridge side by side with the target truck. A solution have been suggested to remove this effect by considering measurements on girders under the truck lane only and exclude the ones under the passenger vehicle lane. Since using the measurements under the vehicle lane will reduce measurements number and thus affect results accuracy, an approximate method are developed to deal with the lower number of measurements. The method has been discussed analytically and experimentally.

The current methods to identify the bridge damage depend on timeconsuming visual inspection and/or based on the data collected from sensorbased monitoring, which make the assessment process very expensive. In this paper, the bridge damage is identified using the data collected from an ordinary strain transducer. In order to demonstrate the new method, 3D finite element models followed by the Inverse Dynamics Optimization Algorithm are performed. The inverse algorithm utilized to calculate the weight of the force that passes on the bridge. Any change in the bridge stiffness by damage will influence the force history which calculated by the inverse algorithm. The proposed method divided into two stages: in the first one, two finite element models are used to simulate the bridge displacement due to quarter car model one representing the healthy bridge and the other for the damage one. In the second stage, the inverse dynamics optimization algorithm used to identify the damage locations.