• Wind and Structures
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• v.37 no.3
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• pp.245-254
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• 2023

In this paper, the wind-induced response of Jiayuguan wooden building (world cultural heritage) in Northwest China was studied. ANSYS finite element software was used to establish four kinds of building models under different working conditions and carry out modal analysis. The simulation results were compared with the field dynamic test results, obtaining the model which reflects the real vibration characteristics of the wooden tower. Time history data of fluctuating wind speed was obtained by MATLAB programming. Time domain method and ANSYS were used to analyze the wind-induced vibration response time history of Jiayuguan wooden building, obtaining the displacement time history curve of the structure. It was suggested that the wind-induced vibration coefficient of Jiayuguan wooden building is 1.76. Through analysis of the performance of the building under equivalent static wind load, the maximum displacement occurs in the three-story wall, gold column and the whole roof area, and the maximum displacement of the building is 5.39 cm. The ratio of the maximum stress value to the allowable value of wood tensile strength is 45 %. The research results can provide reference for the wind resistant design and protection of ancient buildings with similar structure to Jiayuguan wooden tower.

• Computers and Concrete
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• v.32 no.3
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• pp.263-286
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• 2023

Experimenting with concrete to determine its compressive and tensile strengths is a laborious and time-consuming operation that requires a lot of attention to detail. Researchers from all around the world have spent the better part of the last several decades attempting to use machine learning algorithms to make accurate predictions about the technical qualities of various kinds of concrete. The research that is currently available on estimating the strength of concrete draws attention to the applicability and precision of the various machine learning techniques. This article provides a summary of the research that has previously been conducted on estimating the strength of concrete by making use of a variety of different machine learning methods. In this work, a classification of the existing body of research literature is presented, with the classification being based on the machine learning technique used by the researchers. The present review work will open the horizon for the researchers working on the machine learning based prediction of the compressive strength of concrete by providing the recommendations and benefits and drawbacks associated with each model as determining the compressive strength of concrete practically is a laborious and time-consuming task.

• Smart Structures and Systems
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• v.30 no.4
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• pp.397-410
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• 2022

It is essential to monitor the working conditions of bolt-connected joints, which are widely used in various kinds of steel structures. The looseness of bolts may directly affect the stability and safety of the entire structure. In this study, a guided wave-based method for bolt looseness detection and localization is presented for a joint structure with multiple bolts. SH waves generated and received by a small number (two pairs) of magnetostrictive transducers were used. The bolt looseness index was proposed based on the changes in the reconstructed responses excited by the time reversal signals of the measured unit impulse responses. The damage locations and local damage severities were estimated using the damage indices from several wave propagation paths. The back propagation neural network (BPNN) technique was employed to identify the local damages. Numerical and experimental studies were conducted on a lap joint with eight bolts. The results show that the total damage severity can be successfully detected under the effect of external force and measurement noise. The local damage severity can be estimated reasonably for the experimental data using the BPNN constructed by the training patterns generated from the finite element simulations.

• Steel and Composite Structures
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• v.47 no.4
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• pp.523-537
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• 2023

This paper proposed a new self-centering brace (SCB), which consists of four post-tensioned (PT) high strength steel strands and energy absorbing steel plate (EASP) clusters. First, analytical equations were derived to describe the working principle of the SCB. Then, to investigate the hysteretic performance of the SCB, four full-size specimens were manufactured and subjected to the same cyclic loading protocol. One additional specimen using only EASP clusters was also tested to highlight the contribution of PT strands. The test parameters varied in the testing process included the thickness of the EASP and the number of EASP in each cluster. Testing results shown that the SCB exhibited nearly flag-shape hysteresis up to expectation, including excellent recentering capability and satisfactory energy dissipating capacity. For all the specimens, the ratio of the recovered deformation is in the range of 89.6% to 92.1%, and the ratio of the height of the hysteresis loop to the yielding force is in the range of 0.47 to 0.77. Finally, in order to further understand the mechanism of the SCB and provide additional information to the testing results, the high-fidelity finite element (FE) models were established and the numerical results were compared against the experimental data. Good agreement between the experimental, numerical, and analytical results was observed, and the maximum difference is less than 12%. Parametric analysis was also carried out based on the validated FE model to evaluate the effect of some key parameters on the cyclic behavior of the SCB.

• Smart Structures and Systems
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• v.31 no.5
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• pp.455-467
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• 2023

Impact damper is a passive damping system that controls undesirable vibration with mass block impacting with stops fixed to the excited structure, introducing momentum exchange and energy dissipation. However, harmful momentum exchange may occur in the random excitation increasing structural response. Based on the mechanism of impact damping system, a semi-active impact damper (SAID) with controllable impact timing as well as a semi-active control strategy is proposed to enhance the seismic performance of engineering structures in this paper. Comparative experimental studies were conducted to investigate the damping performances of the passive impact damper and SAID. The extreme working conditions for SAID were also discussed and approaches to enhance the damping effect under high-intensity excitations were proposed. A numerical simulation model of SAID attached to a frame structure was established to further explore the damping mechanism. The experimental and numerical results show that the SAID has better control effect than the traditional passive impact damper and can effectively broaden the damping frequency band. The parametric studies illustrate the mass ratio and impact damping ratio of SAID can significantly influence the vibration control effect by affecting the impact force.

• Advances in concrete construction
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• v.13 no.2
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• pp.153-162
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• 2022

Prefabricated exterior wall panel is the main non-load-bearing component of assembly building, which affects the comprehensive performance of thermal insulation and durability of the building. It is of great significance to develop new prefabricated exterior wall panel with durable and lightweight characteristics for the development of energy-saving and assembly building. In the prefabricated sandwich insulation hanging wall panel, the selection of material for the outer layer and the arrangement of the connector of the inner and outer wall layers affect the mechanical performance and durability of the wall panels. In this paper, high performance cement-based composites (HPFRC) are used in the outer layer of the new type wall panel. FRP bars are used as the interface connector. Through experiments and analysis, the influence of the arrangement of connectors on the mechanical behaviors of thin-walled composite wall panel and the panel with window openings under two working conditions are investigated. The failure modes and the role of connectors of thin-walled composite wallboard are analyzed. The influence of the thickness of the wall layer and their combination on the strain growth of the control section, the initial crack resistance, the ultimate bearing capacity and the deformation of the wall panels are analyzed. The research work provides a technical reference for the engineering design of the light-weight thin-walled and durable composite sandwich wall panel.

• Structural Engineering and Mechanics
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• v.84 no.4
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• pp.503-515
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• 2022

Based on the sliding isolation concrete LSS (liquid-storage structure), the specific seismic vulnerability is analyzed according to the general failure mode. In this study, 12 seismic inputs with different characteristics are used, and their acceleration peak values are modulated. By inputting these waves to the sliding isolation concrete storage structure, the finite-element models of different concrete rectangular LSSs are obtained and analyzed, and the failure probabilities are obtained according to the IDA (incremental dynamic analysis) curves of the structure. The results show that when the seismic acceleration peak value gradually increases from 0.1 g to 1.0 g, the failure probability of LSS gradually increases with the increase in friction coefficient. However, the failure probability of a sliding isolation LSS is less than 100% and far less than the failure probability of a non-isolated rectangular LSS, which shows that an isolated liquid storage structure continues working under a big earthquake. Thus, the sliding isolation for the concrete LSS has a significant damping effect.

• Smart Structures and Systems
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• v.30 no.5
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• pp.433-446
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• 2022

Despite efforts to reduce unexpected accidents at confined construction sites, choking accidents continue to occur. Because of the poorly ventilated atmosphere, particularly in long, confined underground spaces, workers are subject to dangerous working conditions despite the use of artificial ventilation. Moreover, the traditional monitoring methods of using portable gas detectors place safety inspectors in direct contact with hazardous conditions. In this study, a long-range (LoRa)-based wireless safety monitoring system that features the network organization, fault-tolerant, power management, and a graphical user interface (GUI) was developed for underground construction sites. The LoRa wireless data communication system was adopted to detect hazardous gases and oxygen deficiency within a confined underground space with adjustable communication range and low power consumption. Fault tolerance based on the mapping information of the entire wireless sensor network was particularly implemented to ensure the reliable operation of the monitoring system. Moreover, a sleep mode was implemented for the efficient power management. The GUI was also developed to control the entire safety-monitoring system and to manage the measured data. The developed safety-monitoring system was validated in an indoor testing and at two full-scale water pipeline construction sites.

• Advances in aircraft and spacecraft science
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• v.10 no.3
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• pp.245-256
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• 2023

In order to improve aerodynamic performance of multi-stage axial flow turbines used in aircraft engines, a one-dimensional aerodynamic design and optimization framework is constructed. In the method, flow path is generated by solving mass continuation and energy conservation with loss computed by the Craig & Cox model; Also real gas properties has been taken into consideration. To obtain an optimal result, a multi-objective genetic algorithm is used to optimize the efficiencies and determine values of various design variables; Final design can be selected from obtained Pareto optimal solution sets. A three-stage axial turbine is used to verify the effectiveness of the developed optimization framework, and designs are checked by three-dimensional CFD simulation. Results show that the aerodynamic performance of the optimized turbine has been significantly improved at design point, with the total-to-total efficiency increased by 1.17% and the total-to-static efficiency increased by 1.48%. As for the off-design performance, the optimized one is improved at all working points except those at small mass flow.

• Computers and Concrete
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• v.33 no.2
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• pp.137-145
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• 2024

The technique of experimentally determining concrete's compressive strength for a given mix design is time-consuming and difficult. The goal of the current work is to propose a best working predictive model based on different machine learning algorithms such as Gradient Boosting Machine (GBM), Stacked Ensemble (SE), Distributed Random Forest (DRF), Extremely Randomized Trees (XRT), Generalized Linear Model (GLM), and Deep Learning (DL) that can forecast the compressive strength of ternary geopolymer concrete mix without carrying out any experimental procedure. A geopolymer mix uses supplementary cementitious materials obtained as industrial by-products instead of cement. The input variables used for assessing the best machine learning algorithm not only include individual ingredient quantities, but molarity of the alkali activator and age of testing as well. Myriad statistical parameters used to measure the effectiveness of the models in forecasting the compressive strength of ternary geopolymer concrete mix, it has been found that GBM performs better than all other algorithms. A sensitivity analysis carried out towards the end of the study suggests that GBM model predicts results close to the experimental conditions with an accuracy between 95.6 % to 98.2 % for testing and training datasets.