• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.309-315
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• 2021

Risks and uncertainty are unavoidable problems in management of projects. Therefore, project managers should not only prevent risks, but also have to respond and manage them. Risk management has become a critical interest subject in the construction industry for both practitioners and researchers. This paper presents critical risk factors of office building projects in the construction phase in Ho Chi Minh City, Vietnam. Data was collected through a questionnaire survey based on the likelihood and consequence level of risk factors. These factors fell into five groups: (i) financial risk factors; (ii) management risk factors; (iii) schedule risk factors; (iv) construction risk factors; and (v) environment risk factors. The research results showed that critical factors affecting office building projects are natural (i.e., prolonged rain, storms, climate effects) and human-made issues (i.e., soil instability, safety behaviors, owner's design change) and the schedule-related risk factors contributed to the most significant risks for office buildings projects in the construction phase in Ho Chi Minh City. They give construction management and project management practitioners a new perspective on risks and risk management of office buildings projects in Ho Chi Minh City and are proactive in the awareness, response, and management of risk factors comprehensively.

• Structural Engineering and Mechanics
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• v.78 no.3
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• pp.351-368
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• 2021

This paper, by applying a reliability-based framework, develops seismic vulnerability macrozonation maps for Tehran, the capital and one of the most earthquake-vulnerable city of Iran. Seismic performance assessment of 3-, 4- and 5-story steel moment resisting frames (SMRFs), designed according to ASCE/SEI 41-17 and Iranian Code of Practice for Seismic Resistant Design of Buildings (2800 Standard), is investigated in terms of overall maximum inter-story drift ratio (MIDR) and unit repair cost ratio which is hereafter known as "damage ratio". To this end, Tehran city is first meshed into a network of 66 points to numerically locate low- to mid-rise SMRFs. Active faults around Tehran are next modeled explicitly. Two different combination of faults, based on available seismological data, are then developed to explore the impact of choosing a proper seismic scenario. In addition, soil effect is exclusively addressed. After building analytical models, reliability methods in combination with structure-specific probabilistic models are applied to predict demand and damage ratio of structures in a cost-effective paradigm. Due to capability of proposed methodology incorporating both aleatory and epistemic uncertainties explicitly, this framework which is centered on the regional demand and damage ratio estimation via structure-specific characteristics can efficiently pave the way for decision makers to find the most vulnerable area in a regional scale. This technical basis can also be adapted to any other structures which the demand and/or damage ratio prediction models are developed.

• Journal of the Korean Geotechnical Society
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• v.23 no.8
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• pp.117-127
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• 2007

Slope stability analysis is a geotechnical engineering problem characterized by many sources of uncertainty. Some of them are connected to the uncertainties of soil properties involved in the analysis. In this paper, a numerical procedure for integrating commercial finite difference method into probabilistic analysis of slope stability is presented. Since the limit-state function cannot be expressed in an explicit form, the ANN-based response surface method is adopted to approximate the limit-state function and the first-, second-order reliability method and the Monte Carlo simulation technique are used to calculate the probability of failure. Probabilistic stability assessments for a hypothetical two-layer slope and the Sugar Creek embankment were performed to verify the application potential to the slope stability problems. The examples show the successful implementation and the possibility of the extension of the proposed procedure to the variety of geotechnical engineering problems.

• Journal of the Korean Geotechnical Society
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• v.24 no.11
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• pp.133-141
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• 2008

In this study, a new method for estimating the undrained shear strength $s_u$ of saturated clays using piezocone penetration test (CPTu) result is proposed. This is to develop more effective CPTu-based $s_u$ estimation method at lower cost with less uncertainty. For this purpose, a marine clay deposit is selected and tested through extensive experimental testing program including both in-situ and fundamental laboratory tests. The proposed method is based on a correlation between the undrained shear strength $s_u$ and the cone resistance $q_t$, without introduction of the total overburden stress into the $s_u-q_t$ correlation. As a result, no additional testing procedure for collecting undisturbed soils samples is required, which can reduce overall testing cost. To verify the proposed method, 4 test sites, which consist of a variety of soil conditions, are selected and used for comparison between measured and predicted undrained shear strength. From comparison, it is seen that predicted values of $s_u$ using the proposed method match well those from measured results.

• Journal of the Korean Geotechnical Society
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• v.26 no.9
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• pp.25-35
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• 2010

Settlement at reclamation area caused by secondary compression should be considered using spatial evaluating method because the thickness of consolidation layer varies at every location. Probabilistic method can be implemented to evaluate uncertainty of spatial distribution of secondary compression. This study spatially evaluated mean and standard deviation of secondary compression in the overall analyzing region using spatial distribution of consolidation thickness estimated by ordinary kriging method and statistical values of soil properties. And then, the area where secondary compression exceeds a design criterion at the specific time was evaluated using probabilistic method. It was observed that the area exceeding the design criterion increased as the variability of $C_{\alpha}/(1+e_o)$ increased or the probabilistic design criterion 0: decreased. It is considered that the probabilistic method can be used for the geotechnical design of soft ground when a probabilistic design criterion is established in the specification.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6C
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• pp.349-358
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• 2008

In performing the reliability analysis for predicting the settlement with time of alluvial clay layer at Kobe airport, the uncertainties of geotechnical properties were examined based on the stochastic and probabilistic theory. By using Terzaghi's consolidation theory as the objective function, the failure probability was normalized based on AFOSM method. As the result of reliability analysis, the occurrence probabilities for the cases of the target settlement of ${\pm}10%,\;{\pm}25%$ of the total settlement from the deterministic analysis were 30~50%, 60%~90%, respectively. Considering that the variation coefficients of input variable are almost similar as those of past researches, the acceptable error range of the total settlement would be expected in the range of 10% of the predicted total settlement. As the result of sensitivity analysis, the factors which affect significantly on the settlement analysis were the uncertainties of the compression coefficient Cc, the pre-consolidation stress Pc, and the prediction model employed. Accordingly, it is very important for the reliable prediction with high reliability to obtain reliable soil properties such as Cc and Pc by performing laboratory tests in which the in-situ stress and strain conditions are properly simulated.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5C
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• pp.343-350
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• 2006

The resistance bias factors for driven steel pipe piles are evaluated as a part of study to develop the LRFD(Load and Resistance Factor Design) for foundation structures in Korea. The 43 data sets of static load tests and soil property tests performed in the whole domestic area were collected and analyzed to determine the representative bearing capacities of the piles using various methods. Based on the statistical analysis of the data, the Davisson's criterion is proved to be the most reasonable method for estimation of pile bearing capacity among the methods used. The static bearing capacity formulas and the Meyerhof method using N values are applied to calculate the design bearing capacity of the piles. The resistance bias factors of the driven steel pipe piles are evaluated respectively as 0.98 and 1.46 by comparison of the bearing capacities for both of the static bearing capacity formulas and the Meyerhof method. It is also shown that uncertainty of the static bearing capacity formulas is relatively less than that of the Meyerhof method.

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Journal of Climate Change Research
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• v.10 no.3
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• pp.237-242
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• 2019

Ammonia is main precursor gas of secondary particulate matter and contributes almost 78% of total ammonia emission from the agricultural sector in Korea. The current method of estimating ammonia emission from fertilizer application, which contributes 7% of the total emission, has high uncertainty and needs to be improved to better predict PM2.5 concentration. In this study, we suggest an improvement method for ammonia emission quantification from fertilizer application. The first improvement was in the emission factor of NPK fertilizer by conducting a field study to verify the currently used factor. The improved NPK emission factor of 52.2 kg NH ton-1N was confirmed by comparing with the value from the EEA (European Environment Agency) and adjusting the value for the Korean climate and soil conditions. We also improved the amount of fertilizer usage by including the sales amount to the fertilizer supply amount of the Korean Farmers Association, increasing total fertilizer usage by 39.8%. As the statistical data on fertilizer supply and sales are compiled yearly, we estimated monthly emission of ammonia by considering cultivated areas and timing of fertilization for each crop. In summary, we suggest a novel and practical method to improve estimation methodology of ammonia emission from the field of fertilizer application: 1) emission factor of NPK fertilizer was reconfirmed; 2) total amount of fertilizer use was revised considering fertilizer sales; and 3) monthly emission of ammonia was realized by considering different crop practices. A bottom-up approach to compile activity data is needed to increase the estimation accuracy of monthly emission of ammonia, which is very helpful for predicting PM2.5 concentration.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.5
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• pp.1443-1454
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• 2014

In this study, we assess impact of spatial resolution of radar rainfall and a distributed hydrologic model on parameter estimation and rainfall-runoff response. Radar data measured by S-band polarimetric radar located at Mt. Bisl in the year of 2012 are used for the comparative study. As different rainfall estimates such as R-KDP, R-Z, and R-ZDR show good agreement with ground rainfall, R-KDP are applied for rainfall-runoff modeling due to relatively high accuracy in terms of catchment averaged and gauging point rainfall. GRM (grid based rainfall-runoff model) is implemented for flood simulations at the Geumho River catchment with spatial resolutions of 200m, 500m, and 1000m. Automatic calibration is performed by PEST (model independent parameter estimation tool) to find suitable parameters for each spatial resolution. For 200m resolution, multipliers of overlandflow and soil hydraulic conductivity are estimated within stable ranges, while high variations are found from results for 500m and 1000m resolution. No tendency is found in the estimated initial soil moisture. When parameters estimated for different spatial resolution are applied for other resolutions, 200m resolution model shows higher sensitivity compared to 1000m resolution model.