A Multi-Contextual Approach to Modeling the Impact of Critical Highway Work Zones in Large Urban Corridors

Accurate Construction Work Zone (CWZ) impact assessments of unprecedented travel inconvenience to the general public are required for all federally-funded highway infrastructure improvement projects. These assessments are critical, but they are also very difficult to perform. Most existing prediction approaches are project-specific, shortterm, and univariate, thus incapable of benchmarking the potential traffic impact of CWZs for highway construction projects. This study fills these gaps by creating a big-data-based decision-support framework and testing if it can reliably predict the potential impact of a CWZ under arbitrary lane closure scenarios. This study proposes a big-data-based decision-support analytical framework, “Multi-contextual learning for the Impact of Critical Urban highway work Zones” (MICUZ). MICUZ is unique as it models the impact of CWZ operations through a multi-contextual quantitative method utilizing sensored big transportation data. MICUZ was developed through a three-phase modeling process. First, robustness of the collected sensored data was examined through a Wheeler’s repeatability and reproducibility analysis, for the purpose of verifying the homogeneity of the variability of traffic flow data. The analysis results led to a notable conclusion that the proposed framework is feasible due to the relative simplicity and periodicity of highway traffic profiles. Second, a machine-learning algorithm using a Feedforward Neural Networks (FNN) technique was applied to model the multi-contextual aspects of iii long-term traffic flow predictions. The validation study showed that the proposed multi-contextual FNN yields an accurate prediction rate of traffic flow rates and truck percentages. Third, employing these predicted traffic parameters, a curve-fitting modeling technique was implemented to quantify the impact of what-if lane closures on the overall traffic flow. The robustness of the proposed curve-fitting models was then scientifically verified and validated by measuring forecast accuracy. The results of this study convey the fact that MICUZ would recognize how stereotypical regional traffic patterns react to existing CWZs and lane closure tactics, and quantify the probable but reliable travel time delays at CWZs in heavily trafficked urban cores. The proposed framework provides a rigorous theoretical basis for comparatively analyzing what-if construction scenarios, enabling engineers and planners to choose the most efficient transportation management plans much more quickly and accurately

Analysis of plant construction accidents and loss estimation using insurance loss records

There are many risks and uncertainties in plant construction projects, because of their complexity, difficulty in loss prediction and size of construction being large. The risk management of plant construction projects should not be relied solely on experiences and intuition of the contractors or the construction managers as it has been in the past. Therefore, a new quantitative and empirical risk analysis is required, in order for the development of a risk assessment using risk indicators for the plant construction projects. This research used the insurance payout record from a global insurance company to reflect the actual quantitative loss in the risk assessment model for plant construction project. The researchers adopted the geographic information as well as construction information (construction phase and commissioning phase, schedule rate, total duration), as the independent variables, which found to be statistically significant in the analysis in this study. It was found that the relationship between damage ratio and the valid variables was statistically significant, and thus, the damage model is also statistically significant. This research suggests that the regression model containing such valid independent variables could be beneficial in terms of providing foundational guidelines for the plant construction project risk analysis

A quantitative risk assessment development using risk indicators for predicting economic damages in construction sites of South Korea

The purpose of this study is to suggest a quantitative risk assessment approach for construction sites using risk indicators to predict economic damages. The frequency of damage in building construction has recently increased, and the associated costs have been increased as well. Although a request for a damage estimation model has been extended, the industry still lacks quantitative and comprehensive research that reveals the physical relationship between damage and risk indicators. To address that issue, we use an insurance company’s payouts from construction site claims in South Korea to reflect the real financial damage. We adopted a multiple regression method to define the risk indicators: geographic vulnerability, natural hazards, capability, and general project information. The results and findings of this research will be accepted as an essential guideline for developing a construction risk estimation model

A study of deep learning algorithm usage in predicting building loss ratio due to typhoons: the case of southern part of the Korean Peninsula

The goal of this study is to suggest an approach to predict building loss due to typhoons using a deep learning algorithm. Due to the influence of climate change, the frequency and severity of typhoons gradually increase and cause exponential destruction of building. Therefore, related industries and the government are focusing their efforts on research and model development to quantify precisely the damage caused by typhoons. However, advancement in the accuracy of prediction is still needed, and the introduction of new technology, obtained due to the fourth revolution, is necessary. Therefore, this study proposed a framework for developing a model based on a deep neural network (DNN) algorithm for predicting losses to buildings caused by typhoons. The developed DNN model was tested and verified by calculating mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2). In addition, to further verify the robustness of the model, the applicability of the framework proposed in this study was verified through comparative verification with the conventional multi-regression model. The results and framework of this study will contribute to the present understanding by suggesting a deep learning method to predict the loss of buildings due to typhoons. It will also provide management strategies to related workers such as insurance companies and facility managers

Harnessing Machine Learning for Classifying Economic Damage Trends in Transportation Infrastructure Projects

Given the highly visible nature, transportation infrastructure construction projects are often exposed to numerous unexpected events, compared to other types of construction projects. Despite the importance of predicting financial losses caused by risk, it is still difficult to determine which risk factors are generally critical and when these risks tend to occur, without benchmarkable references. Most of existing methods are prediction-focused, project type-specific, while ignoring the timing aspect of risk. This study filled these knowledge gaps by developing a neural network-driven machine-learning classification model that can categorize causes of financial losses depending on insurance claim payout proportions and risk occurrence timing, drawing on 625 transportation infrastructure construction projects including bridges, roads, and tunnels. The developed network model showed acceptable classification accuracy of 74.1%, 69.4%, and 71.8% in training, cross-validation, and test sets, respectively. This study is the first of its kind by providing benchmarkable classification references of economic damage trends in transportation infrastructure projects. The proposed holistic approach will help construction practitioners consider the uncertainty of project management and the potential impact of natural hazards proactively, with the risk occurrence timing trends. This study will also assist insurance companies with developing sustainable financial management plans for transportation infrastructure projects