Innovative technologies in manufacturing, mechanics and smart civil infrastructure

An overview on converging technologies that are the primary drivers of the 4th Industrial Revolution is presented, followed by new developments in advanced manufacturing, nano-,information-technologies and smart civil infrastructure technologies. Convergence of these transformative technologies is discussed. Emphases are on advanced manufacturing, nano mechanics/materials, sensors, structural control, smart structures/materials, energy harvesting, multi-scale problems and simulation methods

Adaptive Reconstruction of a Dynamic Force Using Multiscale Wavelet Shape Functions

The shape function-based method is one of the very promising time-domain methods for dynamic force reconstruction, because it can significantly reduce the number of unknowns and shorten the reconstruction time. However, it is challenging to determine the optimum time unit length that can balance the tradeoff between reconstruction accuracy and efficiency in advance. To address this challenge, this paper develops an adaptive dynamic force reconstruction method based on multiscale wavelet shape functions and time-domain deconvolution. A concentrated dynamic force is discretized into units in time domain and the local force in each unit is approximated by wavelet scale functions at an initial scale. Subsequently, the whole response matrix is formulated by assembling the responses induced by the wavelet shape function forces of all time units which are calculated by the structural finite element model (FEM). Then, the wavelet shape function-based force-response equation is established for force reconstruction. Finally, the scale of the force-response equation is lifted by refining the wavelet shape function with high-scale wavelets and dynamic responses with more point data to improve the reconstruction accuracy gradually. Numerical examples of different structural types are analyzed to verify the feasibility and effectiveness of the proposed method

Experimental Study on Impact-Induced Damage Detection Using an Improved Extended Kalman Filter

This paper presents an experimental study on using an improved extended Kalman filter (EKF) to identify impact-induced structural damage. By introducing the optimization of estimated residual error into the classical EKF, this real-time approach demonstrates an excellent capability to identify the abrupt changes of structural parameters instantly and accurately. The optimization procedure is activated when a prescribed threshold is exceeded. A shaking table test of a three-story steel frame subjected to abrupt damage induced by impact load was conducted to validate the improved EKF approach. The results clearly reveal its improved performance and good anti-noise ability in identifying time-variant structural parameters

Damage detection of long-span bridges using stress influence lines incorporated control charts

Numerous long-span bridges have been built throughout the world in recent years. These bridges are progressively damaged by continuous usage throughout their long service life. The failure of local structural components is detrimental to the performance of the entire bridge, furthermore, detecting the local abnormality at an early stage is difficult. This paper explores a novel damage detection method for long-span bridges by incorporating stress influence lines (SILs) in control charts, and validates the efficacy of the method through a case study of the Tsing Ma Suspension Bridge. Damage indices based on SILs are subsequently proposed and applied to hypothetical damage scenarios in which one or two critical bridge components are subjected to severe damage. The comparison study suggests that the first-order difference of SIL change is an accurate indicator for location of the damage. To some extent, different levels of damage can be quantified by using SILs incorporating with X-bar control chart. Results of this study indicate that the proposed SIL-based method offers a promising technique for damage detection in long-span bridges. ? 2014 Science China Press and Springer-Verlag Berlin Heidelberg

Locate damage in long-span bridges based on stress influence lines and information fusion technique

To ensure bridge safety and functionality under in-service conditions, detecting local abnormalities of a long-span bridge at the early stage is always a desirable but challenging task. Stress influence lines (SIL) or its derivatives are recognized as very promising indices for damage detection. Compared with bridge global responses (such as displacement and acceleration), stress/strain can be more conveniently measured and is often more sensitive to local damages. This paper explores a novel damage localization approach by synthesizing SIL measurements from multiple locations, in which Dempster-Shafer data fusion technique is utilized. Compared with the measurement from a single sensor, more reliable damage-related information with the improved sensitivity and capability in damage localization can be obtained by synthesizing the measured SILs from a number of sensors. The effectiveness of the proposed method is validated through a numerical case study of the Tsing Ma Suspension Bridge. Different hypothetical scenarios, including single-damage case, double-damage, and no-damage cases, are considered in the validation. The comparison with the damage detection results using single-sensor data clearly indicates that the data fusion technique effectively enhance the consistency in the information (e.g., damage-induced structural change) and minimize non-consistent information (e.g. "noise" effect) from multiple sensors installed close to damage. The increasing number of sensors benefits the damage detection results. Excellent damage detection accuracy can be achieved, if different types of bridge components are properly selected for the monitoring. Therefore, it is promising to use the proposed approach in this study in the damage localization of real-world long-span bridges. Parametric studies are conducted to examine the effects of parameter selections and noise levels in this approach