• Geomechanics and Engineering
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• v.27 no.5
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• pp.433-445
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• 2021

Artificial ground freezing (AGF) is a commonly used geotechnical support technique that can be applied in any soil type and has low environmental impact. Experimental and numerical investigations have been conducted to optimize AGF for application in diverse scenarios. Precise simulation of groundwater flow is crucial to improving the reliability these investigations' results. Previous experimental research has mostly considered horizontal seepage flow, which does not allow accurate calculation of the groundwater flow velocity due to spatial variation of the piezometric head. This study adopted vertical seepage flow-which can maintain a constant cross-sectional area-to eliminate the limitations of using horizontal seepage flow. The closure time is a measure of the time taken for an impermeable layer to begin to form, this being the time for a frozen soil-ice wall to start forming adjacent to the freeze pipes; this is of great importance to applied AGF. This study reports verification of the reliability of our experimental apparatus and measurement system using only water, because temperature data could be measured while freezing was observed visually. Subsequent experimental AFG tests with saturated sandy soil were also performed. From the experimental results, a method of estimating closure time is proposed using the inflection point in the thermal conductivity difference between pore water and pore ice. It is expected that this estimation method will be highly applicable in the field. A further parametric study assessed factors influencing the closure time using a two-dimensional coupled thermo-hydraulic numerical analysis model that can simulate the AGF of saturated sandy soil considering groundwater flow. It shows that the closure time is affected by factors such as hydraulic gradient, unfrozen permeability, particle thermal conductivity, and freezing temperature. Among these factors, changes in the unfrozen permeability and particle thermal conductivity have less effect on the formation of frozen soil-ice walls when the freezing temperature is sufficiently low.

• Geomechanics and Engineering
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• v.33 no.1
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.707-716
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• 2009

The role of cost modeller is to facilitate the design process by systematic application of cost factors so as to maintain a sensible and economic relationship between cost, quantity, utility and appearance which thus helps in achieving the client's requirements within an agreed budget. There are a number of research on cost estimates in the early design stage based on the improvement of accuracy or impact factors. It is common knowledge that cost estimates are undertaken progressively throughout the design stage and make use of the information that is available at each phase, through the related research up to now. In addition, Cost estimates in the early design stage shall analyze the information under the various kinds of precondition before reaching the more developed design because a design can be modified and changed in all process depending on clients' requirements. Parametric cost estimating models have been adopted to support decision making in a changeable environment, in the early design stage. These models are using a similar instance or a pattern of historical case to be constituted in project information, geographic design features, relevant data to quantity or cost, etc. OLAP technique analyzes a subject data by multi-dimensional points of view; it supports query, analysis, comparison of required information by diverse queries. OLAP's data structure matches well with multiview-analysis framework. Accordingly, this study implements multi-dimensional information system for case based quantity data related to design information that is utilizing OLAP's technology, and then analyzes impact factors of quantity by the design criteria or parameter of the same meaning. On the basis of given factors examined above, this study will generate the rules on quantity measure and produce resemblance class using clustering of data mining. These sorts of knowledge-base consist of a set of classified data as group patterns, of which will be appropriate stand on the parametric cost estimating method.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• Environmental and Resource Economics Review
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• v.32 no.3
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• pp.149-166
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• 2023

The emissions trading system, introduced to reduce greenhouse gas emissions, experienced a sharp increase in emission allowance prices during the second plan period (2018-2020), which led to an increase in the demand for smooth supply and demand of emission allowances, while suppliers anticipating a shortage of emission allowances in the future did not participate in trading. Therefore, the authority temporarily revised the guidelines to ensure that the amount of allowances carried forward is proportional to the trading volume as a market stabilization measure. Through an optimization process using a dynamic nonlinear mathematical model, this paper analyzes the impact of the government's intervention on the carryover policy on GHG emission reductions and emission allowance market prices. According to the simulation analysis results, banking regulations could cause a decline in prices during the regulation period, even though the initial policy was predicted to be adopted.

• Informatization Policy
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• v.30 no.1
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• pp.67-88
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• 2023

The expansion of the scope of product liability is necessary because the industrial environment has changed following the enactment of the Product Liability Act. Unlike human-coded algorithms, artificial intelligence is black-boxed according to machine learning, and even developers cannot explain the results. In particular, since the cause of the problem by artificial intelligence is unknown, the responsibility is unclear, and compensation for victims is not easy. This is because software or artificial intelligence is a non-object, and its productivity is not recognized under the Product Liability Act, which is limited to movable property. As a desperate measure, productivity may be recognized if it is stored or embedded in the medium. However, it is not reasonable to apply differently depending on the medium. The EU revise the product liability guidelines that recognize product liability when artificial intelligence is included. Although compensation for victims is the value pursued by the Product Liability Act, the essence has been overlooked by focusing on productivity. Even if an accident occurs using an artificial intelligence-adopted service, however, it is desirable to present standards according to practical risks instead of unconditionally holding product responsibility.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6D
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• pp.935-943
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• 2006

Key factors affecting the mode share of Soul metro are investigated. The log-regression model, which can describe the elasticity of the factors with ease, is established rather than the conventional mode choice model is used. The log-regression model requires lower level of data availability for calibration and identifies the impact of the factors on mode share straightforwardly. As a result, it is found that the main reasons why the current mode share of railway is low are due to several problems such as winding lines, inconvenient transfers and unnecessary bypasses. The calibrated model is adopted to evaluate the network reshuffling alternatives. The network reshuffling is to rearrange the existing inefficient railway lines that have frequent transfers and many winding segments. The proposed network reshuffling, which includes straightening winding lines and changing grade separated transfers into cross-platform transfers, turned out to be a good measure to tackle the problems.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.23-29
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• 2020

Multilayer sub-contracting is a significant practice among the world, including Hong Kong. When a principal contractor secured a project from a developer, the specific jobs will usually be breaking down and sub-contractors with the lowest bid [1]. The adoption of multilayer sub-contracting has been a controversy issue which is considered as a two-side blade. While certain studies have been carried out to examine both the contributions, damages and improvements for multi-layer subcontracting, the construction industry and researchers are still waiting for a solid measure to enhance the system. Hence, this research attempts to study the advantages, disadvantages, conducts a comparison between single and multilayer sub-contracting and measures of current Hong Kong construction industry based on literature review, questionnaire and in-depth interviews. To achieve the objectives, Analytic Hierarchy Process (AHP) and total weighted score methods are adopted to examine and rank the criterion. The findings of this study provide a good basis for understanding the major reasons and problems caused by the adoption of multilayer sub-contracting. Besides, the identified safety perspective explores a new perspective regarding to issues of multi-layer subcontracting, which will serve as a solid foundation for further research to enhance safety performance. Finally, the findings of measurements towards improvement of multilayer sub-contracting will also provide a solidsolution for construction industry.

• Korean Journal of Radiology
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• v.22 no.5
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• pp.735-741
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• 2021

Objective: To evaluate circularity as a quantitative shape factor of small renal tumor on computed tomography (CT) in differentiating fat-poor angiomyolipoma (AML) from renal cell carcinoma (RCC). Materials and Methods: In 257 consecutive patients, 257 pathologically confirmed renal tumors (either AML or RCC less than 4 cm), which did not include visible fat on unenhanced CT, were retrospectively evaluated. A radiologist drew the tumor margin to measure the perimeter and area in all the contrast-enhanced axial CT images. In each image, a quantitative shape factor, circularity, was calculated using the following equation: 4 x π x (area ÷ perimeter²). The median circularity (circularity index) was adopted as a representative value in each tumor. The circularity index was compared between fat-poor AML and RCC, and the receiver operating characteristic (ROC) curve analysis was performed. Univariable and multivariable binary logistic regression analysis was performed to determine the independent predictor of fat-poor AML. Results: Of the 257 tumors, 26 were AMLs and 231 were RCCs (184 clear cell RCCs, 25 papillary RCCs, and 22 chromophobe RCCs). The mean circularity index of AML was significantly lower than that of RCC (0.86 ± 0.04 vs. 0.93 ± 0.02, p < 0.001). The mean circularity index was not different between the subtypes of RCCs (0.93 ± 0.02, 0.92 ± 0.02, and 0.92 ± 0.02 for clear cell, papillary, and chromophobe RCCs, respectively, p = 0.210). The area under the ROC curve of circularity index was 0.924 for differentiating fat-poor AML from RCC. The sensitivity and specificity were 88.5% and 90.9%, respectively (cut-off, 0.90). Lower circularity index (≤ 0.9) was an independent predictor (odds ratio, 41.0; p < 0.001) for predicting fat-poor AML on multivariable logistic regression analysis. Conclusion: Circularity is a useful quantitative shape factor of small renal tumor for differentiating fat-poor AML from RCC.

• Asia pacific journal of information systems
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• v.33 no.2
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• pp.261-281
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• 2023

The proliferation of online customer reviews has completely changed how consumers purchase. Consumers now heavily depend on authentic experiences shared by previous customers. However, deceptive reviews that aim to manipulate customer decision-making to promote or defame a product or service pose a risk to businesses and buyers. The studies investigating consumer perception of deceptive reviews found that one of the important cues is based on review content. This study aims to investigate the impact of the information amount of review on the review truthfulness. This study adopted the Information Manipulation Theory (IMT) as an overarching theory, which asserts that the violations of one or more of the Gricean maxim are deceptive behaviors. It is regarded as a quantity violation if the required information amount is not delivered or more information is delivered; that is an attempt at deception. A topic modeling algorithm is implemented to reveal the distribution of each topic embedded in a text. This study measures information amount as topic diversity based on the results of topic modeling, and topic diversity shows how heterogeneous a text review is. Two datasets of restaurant reviews on Yelp.com, which have Filtered (deceptive) and Unfiltered (genuine) reviews, were used to test the hypotheses. Reviews that contain more diverse topics tend to be truthful. However, excessive topic diversity produces an inverted U-shaped relationship with truthfulness. Moreover, we find an interaction effect between topic diversity and reviews' ratings. This result suggests that the impact of topic diversity is strengthened when deceptive reviews have lower ratings. This study contributes to the existing literature on IMT by building the connection between topic diversity in a review and its truthfulness. In addition, the empirical results show that topic diversity is a reliable measure for gauging information amount of reviews.