• Computers and Concrete
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• 제10권2호
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• pp.197-217
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• 2012

This research deals with the prediction of compressive strength of normal and high strength concrete using neural networks. The compressive strength was modeled as a function of eight variables: quantities of cement, fine aggregate, coarse aggregate, micro-silica, water and super-plasticizer, maximum size of coarse aggregate, fineness modulus of fine aggregate. Two networks, one using raw variables and another using grouped dimensionless variables were constructed, trained and tested using available experimental data, covering a large range of concrete compressive strengths. The neural network models were compared with regression models. The neural networks based model gave high prediction accuracy and the results demonstrated that the use of neural networks in assessing compressive strength of concrete is both practical and beneficial. The performance of model using the grouped dimensionless variables is better than the prediction using raw variables.

• Structural Engineering and Mechanics
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• 제63권2호
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• pp.259-268
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• 2017

In this paper, the incremental nonlinear dynamic analysis is used to evaluate the seismic performance of steel moment frame structures. To this purpose, three special moment frame structure with 5, 10 and 15 stories are designed according to the Iran's national building code for steel structures and the provisions for design of earthquake resistant buildings (2800 code). Incremental Nonlinear Analysis (IDA) is performed for 15 different ground motions, and responses of the structures are evaluated. For the immediate occupancy and the collapse prevention performance levels, the probability that seismic demand exceeds the seismic capacity of the structures is computed based on FEMA350. Also, fragility curves are plotted for three high-code damage levels using HASUS provisions. Based on the obtained results, it is evident that increase in the height of the frame structures reduces the reliability level. In addition, it is concluded that for the design earthquake the probability of exceeding average collapse prevention level is considerably larger than high and full collapse prevention levels.9.

• Smart Structures and Systems
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• 제12권3_4호
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• pp.399-414
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• 2013

The smart sensor technology has opened new horizons for assessing and monitoring structural health of civil infrastructure. Smart sensor's unique features such as onboard computation, wireless communication, and cost effectiveness can enable a dense network of sensors that is essential for accurate assessment of structural health in large-scale civil structures. While most research efforts to date have been focused on realizing wireless smart sensor networks (WSSN) on bridge structures, relatively less attention is paid to applying this technology to buildings. This paper presents a decentralized damage detection using the WSSN for building structures. An existing flexibility-based damage detection method is extended to be used in the decentralized computing environment offered by the WSSN and implemented on MEMSIC's Imote2 smart sensor platform. Numerical simulation and laboratory experiment are conducted to validate the WSSN for decentralized damage detection of building structures.

• Advances in materials Research
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• 제12권2호
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• pp.161-177
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• 2023

This paper proposes an analytical method to investigate the free vibration behaviour of new functionally graded (FG) carbon nanotubes reinforced composite beams based on a higher-order shear deformation theory. Cosine functions represent the material gradation and material properties via the thickness. The kinematic relations of the beam are proposed according to trigonometric functions. The equilibrium equations are obtained using the virtual work principle and solved using Navier's method. A comparative evaluation of results against predictions from literature demonstrates the accuracy of the proposed analytical model. Moreover, a detailed parametric analysis checks for the sensitivity of the vibration response of FG nanobeams to nonlocal length scale, strain gradient microstructure-scale, material distribution and geometry.

• Earthquakes and Structures
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• 제6권2호
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• pp.141-161
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• 2014

This article presents a new generation of empirical ground motion models for the prediction of response spectral accelerations in soil conditions, specifically developed for the Vrancea intermediate-depth seismic source. The strong ground motion database from which the ground motion prediction model is derived consists of over 800 horizontal components of acceleration recorded from nine Vrancea intermediate-depth seismic events as well as from other seventeen intermediate-depth earthquakes produced in other seismically active regions in the world. Among the main features of the new ground motion model are the prediction of spectral ordinates values (besides the prediction of the peak ground acceleration), the extension of the magnitudes range applicability, the use of consistent metrics (epicentral distance) for this type of seismic source, the extension of the distance range applicability to 300 km, the partition of total standard deviation in intra- and inter-event standard deviations and the use of a national strong ground motion database more than two times larger than in the previous studies. The results suggest that this model is an improvement of the previous generation of ground motion prediction models and can be properly employed in the analysis of the seismic hazard of Romania.

• Smart Structures and Systems
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• 제6권5_6호
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• pp.641-659
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• 2010

Structural Health Monitoring (SHM) gradually becomes a technique for ensuring the health and safety of civil infrastructures and is also an important approach for the research of the damage accumulation and disaster evolving characteristics of civil infrastructures. It is attracting prodigious research interests and the active development interests of scientists and engineers because a great number of civil infrastructures are planned and built every year in mainland China. In a SHM system the sheer number of accompanying wires, fiber optic cables, and other physical transmission medium is usually prohibitive, particularly for such structures as offshore platforms and long-span structures. Fortunately, with recent advances in technologies in sensing, wireless communication, and micro electro mechanical systems (MEMS), wireless sensor technique has been developing rapidly and is being used gradually in the SHM of civil engineering structures. In this paper, some recent advances in the research, development, and implementation of wireless sensors for the SHM of civil infrastructures in mainland China, especially in Dalian University of Technology (DUT) and Harbin Institute of Technology (HIT), are introduced. Firstly, a kind of wireless digital acceleration sensors for structural global monitoring is designed and validated in an offshore structure model. Secondly, wireless inclination sensor systems based on Frequency-hopping techniques are developed and applied successfully to swing monitoring of large-scale hook structures. Thirdly, wireless acquisition systems integrating with different sensing materials, such as Polyvinylidene Fluoride(PVDF), strain gauge, piezoresistive stress/strain sensors fabricated by using the nickel powder-filled cement-based composite, are proposed for structural local monitoring, and validating the characteristics of the above materials. Finally, solutions to the key problem of finite energy for wireless sensors networks are discussed, with future works also being introduced, for example, the wireless sensor networks powered by corrosion signal for corrosion monitoring and rapid diagnosis for large structures.

• Earthquakes and Structures
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• 제25권2호
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• pp.79-87
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• 2023

Special moment resisting steel frame structures are now being used commonly in highly seismic regions as seismically reliable structures. However, a very important parameter describing the dynamics of steel structures during earthquake loading, Soil Structure Interaction (SSI), is generally neglected. In this study, the significance of consideration of flexibility of soil in being able to obtain a result closer to reality is asserted. The current paper focuses on calculation of seismic fragility curves special moment resisting steel frame structures under different earthquake loadings for fixed-base and SSI models. The observation of obtained fragility curves lead to the conclusion that the SSI has a considerable effect on component fragility for the steel structures, with its effects decreasing for higher peak ground acceleration. The results show that the structures when considered SSI have a higher probability of exceeding a damage limit state. This observation attests the role of SSI in the accurate study of structural performance.

• Smart Structures and Systems
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• 제16권6호
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• pp.981-1002
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• 2015

For vibration control of civil structures, especially large civil structures, one of the important issues is how to place a minimal number of actuators and sensors at their respective optimal locations to achieve the predetermined control performance. In this paper, a methodology is presented for the determination of the minimal number and optimal location of actuators and sensors for vibration control of building structures under earthquake excitation. In the proposed methodology, the number and location of the actuators are first determined in terms of the sequence of performance index increments and the predetermined control performance. A multi-scale response reconstruction method is then extended to the controlled building structure for the determination of the minimal number and optimal placement of sensors with the objective that the reconstructed structural responses can be used as feedbacks for the vibration control while the predetermined control performance can be maintained. The feasibility and accuracy of the proposed methodology are finally investigated numerically through a 20-story shear building structure under the El-Centro ground excitation and the Kobe ground excitation. The numerical results show that with the limited number of sensors and actuators at their optimal locations, the predetermined control performance of the building structure can be achieved.

• Wind and Structures
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• 제36권2호
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• pp.133-147
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• 2023

The random vibration of saddle membrane structures under wind load is studied theoretically and experimentally. First, the nonlinear random vibration differential equations of saddle membrane structures under wind loads are established based on von Karman's large deflection theory, thin shell theory and potential flow theory. The probabilistic density function (PDF) and its corresponding statistical parameters of the displacement response of membrane structure are obtained by using the diffusion process theory and the Fokker Planck Kolmogorov equation method (FPK) to solve the equation. Furthermore, a wind tunnel test is carried out to obtain the displacement time history data of the test model under wind load, and the statistical characteristics of the displacement time history of the prototype model are obtained by similarity theory and probability statistics method. Finally, the rationality of the theoretical model is verified by comparing the experimental model with the theoretical model. The results show that the theoretical model agrees with the experimental model, and the random vibration response can be effectively reduced by increasing the initial pretension force and the rise-span ratio within a certain range. The research methods can provide a theoretical reference for the random vibration of the membrane structure, and also be the foundation of structural reliability of membrane structure based on wind-induced response.

• 국제초고층학회논문집
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• 제12권3호
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• pp.203-208
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• 2023

The implementation of artificial intelligence (AI) design for tall building structures is an essential solution for addressing critical challenges in the current structural design industry. Generative AI technology is a crucial technical aid because it can acquire knowledge of design principles from multiple sources, such as architectural and structural design data, empirical knowledge, and mechanical principles. This paper presents a set of AI design techniques for building structures based on two types of generative AI: generative adversarial networks and graph neural networks. Specifically, these techniques effectively master the design of vertical and horizontal component layouts as well as the cross-sectional size of components in reinforced concrete shear walls and frame structures of tall buildings. Consequently, these approaches enable the development of high-quality and high-efficiency AI designs for building structures.