Stress fibers are contractile bundles in the cytoskeleton that stabilize cell structure by exerting traction forces on extracellular matrix. Individual stress fibers are molecular bundles composed of parallel actin and myosin filaments linked by various actin-binding proteins, which are organized end-on-end in a sarcomere-like pattern within an elongated three-dimensional network. While measurements of single stress fibers in living cells show that they behave like tensed viscoelastic fibers, precisely how this mechanical behavior arises from this complex supramolecular arrangement of protein components remains unclear. Here we show that computationally modeling a stress fiber as a multi-modular tensegrity network can predict several key behaviors of stress fibers measured in living cells, including viscoelastic retraction, fiber splaying after severing, non-uniform contraction, and elliptical strain of a puncture wound within the fiber. The tensegrity model also can explain how they simultaneously experience passive tension and generate active contraction forces; in contrast, a tensed cable net model predicts some, but not all, of these properties. Thus, tensegrity models may provide a useful link between molecular and cellular scale mechanical behaviors, and represent a new handle on multi-scale modeling of living materials.

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Summary Identification of earthquake ground motion from structural health monitoring (SHM) data provides a good means to reconstruct seismic loads that are essential for postearthquake safety assessments and disaster simulations of structures. Because the data measured by an SHM system are structural absolute response, they cannot be directly applied to the structural motion equation, which is established in relative coordinate system. As such, this paper originally derives the motion equation in absolute coordinate system and then expands the equation into modal space. In addition, the proposed method allows for identifying earthquake ground motion using incomplete modal information and limited measurements through the standard Kalman filter. Subsequently, a numerical two‐dimensional frame is used to validate the feasibility of the proposed method, and the influences of modal parameters and measurement noise on the identification accuracy are also fully investigated. The results show that the proposed method is sensitive to frequency and measurement noise but insensitive to modal shape and damping ratio. It is also found that the identified ground motion subjected to certain measure noise can still be reliably employed for postseismic response calculations of medium‐ and long‐period structures. Finally, a shaking table test performing on a five‐floor frame further demonstrates the effectiveness and accuracy of the proposed identification algorithm for practical application.

A multitype wireless sensor network (WSN) for structural health monitoring is developed for the National Stadium in China (generally known as "Bird's Nest"). The stadium is a super large-scale building built for the 2008 Beijing Olympic Games and can house more than 90,000 occupants. The structure is very rigid and weighs more than 40,000 tons in total. Considering the structural features and on-site environment, the system takes multitype sensors as measurement components including stress, displacement, acceleration, wind, and temperature. The monitoring module design consists of four functions: sensing, processing, wireless communication, and energy management. The communication between each sensor node is realized by using an adjustable and artificial-control chain-type network. A total of 290 sensors were installed on the structure, and the data collection work has been carried out for more than one year. This paper mainly focuses on the system development and project application, while the data analysis work is briefly discussed as well. It can be concluded that the customized WSN is robust and durable, which well satisfies the requirement of plenty multitype sensors working in a large-area distribution. The data analysis results reveal that the super large-scale structure is very sensitive to the temperature effect.

Tensegrity-based locomotive robots have attracted more and more interests from multidisciplinary engineering community. To realize long distance locomotion for tensegrity robots in a given land, path planning is usually needed. This paper proposes a path planning approach for rolling locomotion of polyhedral tensegrity robots. Given the start vertex, target vertex and the directed graph G which indicates the possible paths, the optimal path with lowest cost can be found by Dijkstra algorithm. Numerical and experimental examples are carried out with a six-bar tensegrity robot prototype. Both motion distance and terrain characteristics are considered within the cost. The proposed approach is generally verified by the examples. A comparison between the numerical result and the experimental result is also presented.