• Advances in Computational Design
• /
• v.5 no.1
• /
• pp.35-54
• /
• 2020

One of the prevailing structural systems in high-rise buildings is the core-wall system. On the other hand, the existence of one or more underground stories causes the perimeter below-grade walls with the diaphragm of grade level to constitute of a very stiff box. In this case or a similar situation, during the lateral response of a tall building, underground perimeter walls and diaphragms that provide an increased lateral resistance relative to the core wall may introduce a prying action in the core that is called backstay effect. In this case, a rather great force is generated at the diaphragm of the grade-level, acting in a reverse direction to the lateral force on the core-wall system, and thus typically causes a reverse internal shear. In this research, in addition to review of the results of the preceding studies, an improved relationship is proposed for prediction of backstay force. The new proposed relationship takes into account the effect of foundation flexibility and is presented in a non-dimensional form. Furthermore, a specific range of the backstay force to lateral load ratio has been determined. And finally, it is shown that although all suggested formulas are valid in the elastic domain, yet with some changes in the initial considerations, they can be applied to some certain non-linear problems as well.

• Smart Structures and Systems
• /
• v.24 no.6
• /
• pp.733-744
• /
• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Wind and Structures
• /
• v.2 no.1
• /
• pp.1-23
• /
• 1999

Wind effects are critical considerations in the design of topside structures, overall structural systems, or both, depending on the water depth and type of offshore platform. The reliable design of these facilities for oil fields in regions of hostile environment can only be assured through better understanding of the environmental load effects and enhanced response prediction capabilities. This paper summarizes the analysis and performance of offshore platforms under extreme wind loads, including the quantification of wind load effects with focus on wind field characteristics, steady and unsteady loads, gust loading factors, application of wind tunnel tests, and the provisions of the American Petroleum Institute Recommended Practice 2A - Working Stress Design (API RP 2A-WSD) for the construction of offshore structures under the action of wind. A survey of the performance of platforms and satellite structures is provided, and failure mechanisms concerning different damage scenarios during Hurricane Andrew are examined. Guidelines and provisions for improving analysis and design of structures are addressed.

• Smart Structures and Systems
• /
• v.2 no.2
• /
• pp.127-140
• /
• 2006

In this study, a wavelet-based covariance-driven system identification technique is proposed for damage assessment of structures under ambient excitation. Assuming the ambient excitation to be a white-noise process, the covariance computation is shown to be able to separate the effect of random excitation from the response measurement. Wavelet transform (WT) is then used to convert the covariance response in the time domain to the WT magnitude plot in the time-scale plane. The wavelet coefficients along the curves where energy concentrated are extracted and used to estimate the modal properties of the structure. These modal property estimations lead to the calculation of the stiffness matrix when either the spectral density of the random loading or the mass matrix is given. The predicted stiffness matrix hence provides a direct assessment on the possible location and severity of damage which results in stiffness alteration. To demonstrate the proposed wavelet-based damage assessment technique, a numerical example on a 3 degree-of-freedom (DOF) system and an experimental study on a three-story building model, which are all under a broad-band excitation, are presented. Both numerical and experimental results illustrate that the proposed technique can provide an accurate assessment on the damage location. It is however noted that the assessment of damage severity is not as accurate, which might be due to the errors associated with the mode shape estimations as well as the assumption of proportional damping adopted in the formulation.

• Wind and Structures
• /
• v.16 no.2
• /
• pp.157-178
• /
• 2013

Output-only modal parameter identification is based on the assumption that external forces on a linear structure are white noise. However, harmonic excitations are also often present in real structural vibrations. In particular, it has been realized that the use of forced acceleration responses without knowledge of external forces can pose a problem in the modal parameter identification, because an external force is imparted to its impulse acceleration response function. This paper provides a three-stage identification procedure as a solution to the problem of harmonic and white noise excitations in the acceleration responses of a linear dynamic system. This procedure combines the uses of the mode indicator function, the complex mode indication function, the enhanced frequency response function, an iterative rational fraction polynomial method and mode shape inspection for the correlation-related functions of the force-embedded acceleration responses. The procedure is verified via numerical simulation of a five-floor shear building and a two-dimensional frame and also applied to ambient vibration data of a large-span roof structure. Results show that the modal parameters of these dynamic systems can be satisfactorily identified under the requirement of wide separation between vibration modes and harmonic excitations.

• Earthquakes and Structures
• /
• v.4 no.6
• /
• pp.607-627
• /
• 2013

The application of shape memory alloys (SMAs) to the seismic response reduction of civil engineering structures has attracted growing interest due to their self-centering feature and excellent fatigue performance. The loading rate dependence of SMAs raises a concern in the seismic analysis of SMA-based devices. However, the implementation of micromechanics-based strain-rate-dependent constitutive models in structural analysis software is rather complicated and computationally demanding. This paper investigates the feasibility of replacing complex rate-dependent models with rate-independent constitutive models for superelastic SMA elements in seismic time-history analysis. Three uniaxial constitutive models for superelastic SMAs, including one rate-dependent thermomechanical model and two rate-independent phenomenological models, are considered in this comparative study. The pros and cons of the three nonlinear constitutive models are also discussed. A parametric study of single-degree-of-freedom systems with different initial periods and strength reduction factors is conducted to examine the effect of the three constitutive models on seismic simulations. Additionally, nonlinear time-history analyses of a three-story prototype steel frame building with special SMA-based damping braces are performed. Two suites of seismic records that correspond to frequent and design basis earthquakes are used as base excitations in the seismic analyses of steel-braced frames. The results of this study show that the rate-independent constitutive models, with their parameters properly tuned to dynamic test data, are able to predict the seismic responses of structures with SMA-based seismic response modification devices.

• Structural Engineering and Mechanics
• /
• v.78 no.3
• /
• pp.319-332
• /
• 2021

The wood-concrete composite is an interesting solution in the field of Civil Engineering to create high performance bending elements for bridges, as well as in the building construction for the design of wood concrete floor systems. The authors of this paper has been working for the past few years on the development of the bonding process as applied to wood-concrete composite structures. Contrary to conventional joining connectors, this assembling technique does ensure an almost perfect connection between wood and concrete. This paper presents a careful theoretical investigation into interfacial stresses at the level of the two interfaces in composite wooden beam- reinforced concrete slab strengthened by external bonding of prestressed composite plate under a uniformly distributed load. The model is based on equilibrium and deformations compatibility requirements in all parts of the strengthened composite beam, i.e., the wooden beam, RC slab, the CFRP plate and the adhesive layer. The theoretical predictions are compared with other existing solutions. This research is helpful for the understanding on mechanical behaviour of the interface and design of the CFRP- wooden-concrete hybrid structures.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.44 no.4
• /
• pp.177-185
• /
• 2021

Over the past 40 years, Korea's defense industry has been deepening into a low-efficiency industrial structure as the government directly controls prices, quantities, and costs. By implementing the Defense Industry Building Act in 2021, the government is creating a healthy ecosystem for the defense industry and strengthening its global competitiveness. In this study, based on KPC's Productivity Management System (PMS), a diagnostic model of defense companies implemented since 2013, the on-site diagnosis was performed from 4 to 28 days depending on the size of the company data was collected based on the results. The causal relationship was analyzed through structural equation model path analysis for the effect of innovation capability on productivity performance. As a result, it suggests that defense materials suppliers should focus on which core processes to innovate and strengthen and improve their innovation capabilities.

• Smart Structures and Systems
• /
• v.28 no.6
• /
• pp.727-735
• /
• 2021

Recently, non-destructive health monitoring methods such as magnetic flux leakage (MFL) method, have become popular due to their advantages over destructive methods. Currently, numerical study on this field has been limited to simplified studies by only obtaining MFL instead of induced voltage inside coil sensor. In this study, it was proposed to perform a novel numerical simulation of MFL's coil sensor by considering vital parameters including specimen's motion with constant velocity and saturation status of specimen in time domain. A steel-rod specimen with two stepwise cross-sectional changes (i.e., 21% and 16%) was fabricated using low carbon steel. In order to evaluate the results of numerical simulation, an experimental test was also conducted using a magnetic probe, with same size specimen and test parameters, exclusively. According to comparative results of numerical simulation and experimental test, similar signal amplitude and signal pattern were observed. Thus, proposed numerical simulation method can be used as a reliable source to check efficiency of sensor probe when different size specimens with different defects should be inspected.

• Smart Structures and Systems
• /
• v.30 no.5
• /
• pp.501-511
• /
• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.