Wai Kiong Chong

Preliminary cost estimates using probabilistic simulation for highway bridge replacement projects

To improve the accuracy and efficiency of highway budgeting estimation has been an important research focus in the industry and is the objective of this paper. Useful data were extracted from the Texas Department of Transportation (TxDOT) to develop an alternative to achieve the research objective. Heuristic simulation models pertaining to highway bridge replacement projects were developed to guide engineers to reduce estimation variability before the beginning of planning authorization. The proposed simulation models include independent, correlated, and Latin Hypercube sampling approaches that specifically consider major work items, roll-up work items, and projectlevel engineering contingency. The charts of cumulative density functions (CDFs) are derived as a handy tool for decision makers to consider project finance initiation, project risk, and uncertainty assessment . The systematic procedure can be expanded to other project types and to develop lane-km (lane-mile) cost distributions when ample historical project data are available.

The use of artificial intelligence combiners for modeling steel pitting risk and corrosion rate

Abstract Corrosion is a common deterioration that reduces the service life of concrete structures and steels. Particularly, corrosion behavior is a highly nonlinear problem influenced by complex characteristics. This study used advanced artificial intelligence (AI) techniques to predict pitting corrosion risk of steel reinforced concrete and marine corrosion rate of carbon steel. The AI-based models used for prediction included single and ensemble models constructed from four well-known machine learners including artificial neural networks (ANNs), support vector regression/machines (SVR/SVMs), classification and regression tree (CART), and linear regression (LR). Notably, a hybrid metaheuristic regression model was implemented by integrating a smart nature-inspired metaheuristic optimization algorithm ( i.e. , smart firefly algorithm) with a least squares SVR. Prediction accuracy was evaluated using two real-world datasets. According to the comparison results, the hybrid metaheuristic regression model was better than the single and ensemble models in predicting the pitting corrosion risk (mean absolute percentage error=5.6%) and the marine corrosion rate (mean absolute percentage error = 1.26%). The hybrid metaheuristic regression model is a promising and practical methodology for real-time tracking of corrosion in steel rebar. Civil engineers can use the hybrid model to schedule maintenance process that leads to risk reduction of structure failure and maintenance cost.

Predicting the production rates of foundation construction using factor and regression analysis

Foundations are often part of the critical path for bridge construction. However, foundation constructions are highly variable due to the fact that construction process faces difficult problems like poor soil conditions, congested traffic and unpredictable weather. Designers are often unable to obtain reliable production rates in order to improve the accuracy of project time estimation that often leads to more delays and disputes. Field construction data related to this research were collected from twenty-five Texas highway projects within a two-year period to develop three models that could predict production rates of drilled shafts and prestressed concrete piles more accurately. Analyses showed that these models are estimate production rates accurately and relatively simple to apply and develop.

A Web-based Framework of Project Performance and Control System

Making sure projects are completed on time, within budget and according to specified quality are the ultimate goals for all projects. The powers of modern computing system and World Wide Web (WWW) have made it easier for project managers to monitor information in the comfort of their office and home. Internet allows project managers access to information from any location of their choice and thus they could manage their projects without being present on location. Converting and delivering project-based information are necessary in order to make full use of the internet. Earned Value Management (EVM) is one of the many tools that project managers used to track cost growth and project delay. It provides an objective measure of the amount of work that has been accomplished. EVM is often utilized by project managers to track project progress and determine their achievements. Project information could be converted into manageable and easily understood pieces that would form information clusters. The purpose of this paper is to layout a visualized architecture of project performance measurement that integrates earned value analysis and control within a Web-based system which would allow construction personnel to track, modify and update cost and time-based data of project status online.

Estimating highway construction production rates during design: Elements of a useful estimation tool

Construction scheduling for highway projects is an important process during the design stage. Numerous research studies have attempted to apply new techniques to improve the accuracy of construction scheduling. Many of these studies, however, failed to address the practicality of the scheduling methods and the needs of highway designers. The authors conducted literature reviews, surveys, and interviews to study the challenges designers face in estimating production rates for highway construction. We found that estimation tools for production rates should be flexible, user friendly, and efficient yet comprehensive. Data should be collected from reliable sources and analyzed appropriately and efficiently before being applied to a production rate tool. This study suggested that combining designers’ experience and reliable tools is the most effective way to develop realistic production rates for highway construction scheduling.

IDENTIFYING FACTORS FOR STATISTICAL MODELS TO MEASURE HIGHWAY PROJECT DISRUPTIONS

Highway projects are thought to be influenced by many different types of delays. Data collected from twenty-eight Texas highway projects over a two-year period found that utilities conflicts, rain and shortage of materials are the only three significant disruptions. Research also found that most of the disruptions caused production to stop between one to five days while few had stoppages of more than ten days. This paper also found that productivity recovery from different types of delays was different and that learning effects had huge impact on the rate of recovery. The research identified several useful factors that could be used to develop statistical models to estimate delays on reinforced concrete pipes. First, rain related productivity delays could be estimated by differentiating clay content in soil. Second, utilities conflicts could be estimated by differentiating the types of utilities that were involved. Finally, the actions that were taken by the contractors could be used as factors to measure productivity recovery. The overall project delays could be estimated using these factors. These factors were also found to be extremely useful to resolute disputes on time claims due to disruptions.

Big data analytics and cloud computing for sustainable building energy efficiency

Currently, big data analytics and cloud computing are emerging practices for sustainable energy systems and efficient energy management. Utilizing building energy usage data is critical for the successful deployment of energy efficiency. This chapter presents the framework of a smart decision support system (SDSS) that integrates smart grid big data analytics and cloud computing for building energy efficiency. The framework is based on a layered architecture that includes smart grid and data collection, an analytics bench, and a web-based portal. A real-world smart metering infrastructure was installed in a residential building for the experiment. The SDSS is expected to accurately identify the building energy consumption patterns and forecasted future energy usage. Moreover, end users can reduce electricity costs by using the system to optimize operation schedules of appliances, lighting systems, and heating, ventilation, and air conditioning. The proposed framework serves as a start-up creation in an application of big data analytics and cloud computing technology for sustainable building energy efficiency.