• Steel and Composite Structures
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• v.44 no.3
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• pp.309-324
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• 2022

The post-fire elastic stiffness and performance of concrete-filled steel tube (CFST) columns containing recycled aggregate concrete (RAC) has rarely been addressed, particularly in terms of material properties. This study was conducted with the aim of assessing the modulus of elasticity of recycled aggregate concrete-filled steel tube (RACFST) stub columns following thermal loading. The test data were employed to model and assess the elastic modulus of circular RACFST stub columns subjected to axial loading after exposure to elevated temperatures. The length/diameter ratio of the specimens was less than three to prevent the sensitivity of overall buckling for the stub columns. The gene expression programming (GEP) method was employed for the model development. The GEP model was derived based on a comprehensive experimental database of heated and non-heated RACFST stub columns that have been properly gathered from the open literature. In this study, by using specifications of 149 specimens, the variables were the steel section ratio, applied temperature, yielding strength of steel, compressive strength of plain concrete, and elastic modulus of steel tube and concrete core (RAC). Moreover, parametric and sensitivity analyses were also performed to determine the contribution of different effective parameters to the post-fire elastic modulus. Additionally, comparisons and verification of the effectiveness of the proposed model were made between the values obtained from the GEP model and the formulas proposed by different researchers. Through the analyses and comparisons of the developed model against formulas available in the literature, the acceptable accuracy of the model for predicting the post-fire modulus of elasticity of circular RACFST stub columns was seen.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.

• Geotechnical Engineering
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• v.12 no.4
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• pp.157-178
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• 1996

Current design methods for reinforced earth structures take no account of the magnitude of the strains induced in the tensile members as these are invariably manufactured from high modulus materials, such as steel, where straits are unlikely to be significant. With fabrics, however, large strains may frequently be induced and it is important to determine these to enable the stability of the structure to be assessed. In the present paper internal design method of analysis relating to the use of fabric reinforcements in reinforced earth structures for both stress and strain considerations is presented. For the internal stability analysis against rupture and pullout of the fabric reinforcements, a strain compatibility analysis procedure that considers the effects of reinforcement stiffness, relative movement between the soil and reinforcements, and compaction-induced stresses as studied by Ehrlich 8l Mitchell is used. I Bowever, the soil-reinforcement interaction is modeled by relating nonlinear elastic soil behavior to nonlinear response of the reinforcement. The soil constitutive model used is a modified vertsion of the hyperbolic soil model and compaction stress model proposed by Duncan et at., and iterative step-loading approach is used to take nonlinear soil behavior into consideration. The effects of seepage pressures are also dealt with in the proposed method of analy For purposes of assessing the strain behavior oi the fabric reinforcements, nonlinear model of hyperbolic form describing the load-extension relation of fabrics is employed. A procedure for specifying the strength characteristics of paraweb polyester fibre multicord, needle punched non-woven geotHxtile and knitted polyester geogrid is also described which may provide a more convenient procedure for incorporating the fablic properties into the prediction of fabric deformations. An attempt to define improvement in bond-linkage at the interconnecting nodes of the fabric reinforced earth stracture due to the confining stress is further made. The proposed method of analysis has been applied to estimate the maximum tensions, deformations and strains of the fabric reinforcements. The results are then compared with those of finite element analysis and experimental tests, and show in general good agreements indicating the effectiveness of the proposed method of analysis. Analytical parametric studies are also carried out to investigate the effects of relative soil-fabric reinforcement stiffness, locked-in stresses, compaction load and seepage pressures on the magnitude and variation of the fabric deformations.

• The Journal of Engineering Geology
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• v.15 no.3
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• pp.257-268
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• 2005

In situ rock mass is generally heterogeneous and discontinuous, with varying degrees of strength along the planes of weakness. The planes of weakness such as joints, faults, cracks and bedding planes, control the strength and deformation characteristics of the rock mass. Subsequently, the stability of underground opening depends upon the spatial distribution of discontinuities and their mechanical properties in relation with geometrical shape of openins as well as the mechanical properties of intact rock materials. Understanding the behaviour of a discontinuous rock mass remains a key issue for improving excavation design in hiかy stressed environments. Although recent advances in rock mechanics have provided guidelines for the design of underground opening in isotropic rock mass, prediction and control of deformation in discontinuous rock masses are still unclear. In this study, parametric study was performed to investigate the plastic zone size, stress distribution and deformation behavior around underground opening in a discontinuous rock mass using a continuum joint model. The solutions were obtained by an elasto-plastic finite difference analysis, employing the Mohr-Coulomb failure criteria. Non-associated flow rule and perfectly plastic material behavior are also assumed.

• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.61-74
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• 2010

ITS (Intelligent transportation systems) collects real-time traffic data, and accumulates vest historical data. But tremendous historical data has not been managed and employed efficiently. With the introduction of data management systems like ADMS (Archived Data Management System), the potentiality of huge historical data dramatically surfs up. However, traffic data in any data management system includes missing values in nature, and one of major obstacles in applying these data has been the missing data because it makes an entire dataset useless every so often. For these reasons, imputation techniques take a key role in data management systems. To address these limitations, this paper presents a promising imputation technique which could be mounted in data management systems and robustly generates the estimations for missing values included in historical data. The developed model, based on NPR (Non-Parametric Regression) approach, employs various traffic data patterns in historical data and is designated for practical requirements such as the minimization of parameters, computational speed, the imputation of various types of missing data, and multiple imputation. The model was tested under the conditions of various missing data types. The results showed that the model outperforms reported existing approaches in the side of prediction accuracy, and meets the computational speed required to be mounted in traffic data management systems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.1
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• pp.73-80
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• 2018

In motorway traffic flow control, the conventional way based on real-time response has been changed into advanced way based on proactive response. Future traffic conditions over multiple time intervals are crucial input data for advanced motorway traffic flow control. It is necessary to overcome the uncertainty of the future state in order for forecasting multiple-period traffic volumes, as the number of uncertainty concurrently increase when the forecasting horizon expands. In this vein, multi-interval forecasting of traffic volumes requires a viable approach to conquer future uncertainties successfully. In this paper, a forecasting model is proposed which effectively addresses the uncertainties of future state based on the behaviors of temporal evolution of traffic volume states that intrinsically exits in the big past data. The model selects the past states from the big past data based on the state evolution of current traffic volumes, and then the selected past states are employed for estimating future states. The model was also designed to be suitable for data management systems in practice. Test results demonstrated that the model can effectively overcome the uncertainties over multiple time periods and can generate very reliable predictions in term of prediction accuracy. Hence, it is indicated that the model can be mounted and utilized on advanced data management systems.

• Korean Journal of Environment and Ecology
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• v.29 no.3
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• pp.333-343
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• 2015

This research aims at identifying the goshawk's possible and replaceable breeding ground by using the MaxEnt prediction model which has so far been insufficiently used in Korea, and providing evidence to expand possible protection areas for the goshawk's breeding for the future. The field research identified 10 goshawk's nests, and 23 appearance points confirmed during the 3rd round of environmental research were used for analysis. 4 geomorphic, 3 environmental, 7 distance, and 9 weather factors were used as model variables. The final environmental variables were selected through non-parametric verification between appearance and non-appearance coordinates identified by random sampling. The final predictive model (MaxEnt) was structured using 10 factors related to breeding ground and 7 factors related to appearance area selected by statistics verification. According to the results of the study, the factor that affected breeding point structure model the most was temperature seasonality, followed by distance from mixforest, density-class on the forest map and relief energy. The factor that affected appearance point structure model the most was temperature seasonality, followed by distance from rivers and ponds, distance from agricultural land and gradient. The nature of the goshawk's breeding environment and habit to breed inside forests were reflected in this modeling that targets breeding points. The northern central area which is about $189.5 km^2$(2.55 %) is expected to be suitable breeding ground. Large cities such as Cheongju and Chungju are located in the southern part of Chungcheongbuk-do whereas the northern part of Chungcheongbuk-do has evenly distributed forests and farmlands, which helps goshawks have a scope of influence and food source to breed. Appearance point modeling predicted an area of $3,071 km^2$(41.38 %) showing a wider ranging habitat than that of the breeding point modeling due to some limitations such as limited moving observation and non-consideration of seasonal changes. When targeting the breeding points, a specific predictive area can be deduced but it is difficult to check the points of nests and it is impossible to reflect the goshawk's behavioral area. On the other hand, when targeting appearance points, a wider ranging area can be covered but it is less accurate compared to predictive breeding point since simple movements and constant use status are not reflected. However, with these results, the goshawk's habitat can be predicted with reasonable accuracy. In particular, it is necessary to apply precise predictive breeding area data based on habitat modeling results when enforcing an environmental evaluation or establishing a development plan.