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Due to the advantages of high integration, modular steel buildings have recently received extensive attention and research. The connection between modules (inter-module connection) plays an important role in ensuring the safety and integrity of modular steel buildings. However, most of the existing inter-module connections have problems such as insufficient construction space, difficulty in disassembly, lack of design specifications, etc. An innovative inter-module connection with bolt and shear key fitting was proposed. The connection separates the horizontal load-bearing component and the vertical load-bearing component, which greatly reduces the difficulty of analysis and design. The structure of the connection is convenient for construction and insensitive to installation errors. The structure and advantages of the connection were introduced in this paper, and the shear performance of the connection was studied by the monotonic static test. A finite element model verified with the experiment was proposed to simulate the shear performance of the connection. The influence of errors in fabrication on the performance of the connection was studied using the finite element model, and connection's operating status in a modular frame was analyzed. Thus the allowance for the clearance between the shear key and slot was obtained. Based on the experimental and numerical study results, a formula was proposed to predict the shear capacity of the connection for the practical design of inter-module connection in modular steel buildings. [ABSTRACT FROM AUTHOR]

To protect the acceleration-sensitive non-structural components in a multi-story building, a proper seismic design of them against the peak floor acceleration (PFA) is needed. The PFA, therefore, should be estimated in advance. To efficiently estimate the PFA of multi-story buildings at the inelastic stage, a nonlinear modal combination approach is provided in this study. First, for the single-story building (single-degree-of-freedom system), a PFA spectral ratio δ, defined as the ratio between the inelastic PFA and the elastic spectral amplitude, is proposed to measure the nonlinear reductions in PFA. A series of time history analysis (THA) revealed that there is a dependable relationship between the PFA spectral ratio δ, the strength reduction factor R, the post-yield stiffness ratio α, and the period T of the structure. The scatter of δ is notably smaller than the ductility demand, showing the good feasibility of a δ-R-T-α relationship for predicting δ. Second, for the multi-story building (multi-degree-of-freedom system), the floor acceleration is decoupled in the modal space, the contribution of each mode to the elastic PFA is multiplied by δ to account for the nonlinear reduction in PFA at the inelastic stage. Here δ can be determined by an appropriate δ-R-T-α relationship, while R and α are determined via a modal pushover analysis. The modified modal contributions are combined via the complete-quadratic combination (CQC) rule, forming the inelastic CQC approach. The PFAs of some 2-, 5-, and 8-story building structures with different degrees of nonlinearity are computed via the THA and the inelastic CQC approach. Results demonstrate the satisfactory accuracy of the latter. Notably, the effects of nonlinearities associated with the higher-order modes on the nonlinear PFA are well considered in the inelastic CQC, thus the un-acceptable over-estimations of the inelastic PFA, which occurs in conventional methods, are avoided. [ABSTRACT FROM AUTHOR]

To reduce secondary casualties and support firefighters in making scientific decisions in rescue, a real-time early-warning system for fire-induced building collapse, with “real-time measurement, online data analysis, and real-time early warning” being its core, has been developed. To validate the effectiveness of the system, a fire-induced collapse test on a real building with a truss roof was conducted. The basic framework of the early-warning system is firstly reviewed. The test program is then introduced, including basic information about the test structure, loading scheme, measuring points, and equipment. The detailed test phenomena, and the thermal and structural responses are further described and analyzed. The test results revealed that the temperature distribution within the tested building is highly non-uniform, and the maximum temperature of the gas and steel components reached 900°C. The collapse mode of the truss was successfully identified by the early-warning algorithm based on the evolution laws of the displacements at the key joints of the truss, including prominent vertical joint displacements, buckling of the steel member of the truss, and bending failure of the top reinforced concrete chord of the truss. The test building collapsed after 4253 s after the ignition of the fire, and the developed early-warning system issued the remaining time to collapse in real time before the collapse of the test building. It is demonstrated that the predicted remaining time to collapse by the early-warning system was close to the real value. [ABSTRACT FROM AUTHOR]

This study proposes the FRAME-Net, which can extract spatial-temporal features of discrete structures like planar steel frames, for early warning systems of fire-induced building collapse. FRAME-Net, based on graph neural networks and recurrent neural networks, seamlessly integrates feature extraction techniques, advanced graph convolution operations, and an enhanced graph long short-term memory mechanism, resolving the critical issue that the actual state of burning buildings, e.g., fire scenario, load case, material properties, etc., cannot be identified accurately and rapidly. The temperatures of steel components and easy-to-measure displacements are inputs, and hard-to-measure displacements at the top and interior of steel frames under fire can be predicted in real time. Numerical examples indicate that the trained agent accurately predicts the hard-to-measure displacements for the trained steel frame under completely unknown datasets, showcasing its potential in real-world unknown fire scenarios. Besides, the trained intelligent agent can be directly applied to untrained steel frames with different topological forms without re-training, which significantly reduces computational costs and is beneficial for swift emergency responses. Subsequently, satisfactory results predicted by the trained agent without re-training for the steel frame under the real fire test further underscores the promising generalization capability of the trained agent to adapt to unknown fire scenarios in real-world steel frames. Finally, the feasibility of the proposed framework to facilitate the implementation of the early-warning systems of fire-induced collapse is proved by comparing the predicted and actual remaining building collapse time, thereby assisting in reducing casualties during the rescue process and contributing to smart firefighting advancements. • FRAME-Net combines GNN and RNN for spatial-temporal feature extraction. • FRAME-Net can assist the early-warning system of fire-induced collapse. • Accurate predictions for hard-to-measure displacements in unknown scenarios. • Applicable to diverse steel frame topologies without the need for re-training. • Proven robust generalization in real fire tests. [ABSTRACT FROM AUTHOR]

C28H22N2O4Zn, tetragonal, P41212 (no. 92), a = 10.986(5) Å, b = 10.986(5) Å, c = 18.187(8) Å, V = 2195(2) Å3, Z = 4, Rgt(F) = 0.0440, wRref(F2) = 0.0942, T = 293 K.

C22H30N2O4S2, monoclinic, P21/c, a = 13.6316(5) Å, b = 5.7462(2) Å, c = 14.1562(5) Å, β = 91.758(4)°, V = 1108.33(7) Å3, Z = 2, Rgt(F) = 0.0464, wRref(F2) = 0.1249, T = 293 K.