Wang, Y (2015) Structured versus direct-mapping approaches to empirical modeling of civil engineering problems. PhD thesis, University of Florida, USA.
Abstract
Empirical modeling and its associated methodologies are widely adopted in the development process of prediction systems for solving construction engineering problems. Most current empirical models employ a direct mapping architecture with only one mapping step between input and output variables. This property limits the model extendibility by preventing the model from solving some extended versions of the civil engineering problem it is originally developed to solve. In addition, the data size used to train and test the model grow exponentially when new independent variables are introduced. This study aims to develop a new approach to circumvent the above mentioned issues in the application of empirical models to civil and construction problems. The study investigated the performances of several direct mapping models in solving truck type classification based on weigh-in-motion data and resolve this problem using a newly developed empirical modeling schema called structured empirical modelling approach (SEMA). The innovation is inspired by the adoption of intermediate features in the deep machine learning field. Proposed benefits of SEMA modeling includes, but is not limited to, improved model prediction accuracy, improved model flexibility in training and testing, reduced training data requirements and potential extendibility to solve similar construction engineering problems.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Flood, I |
| Uncontrolled Keywords: | accuracy; flexibility; civil engineering; construction engineering; innovation; learning; training; civil engineer; machine learning |
| Index terms: | independent variable, construction engineering, modelling, testing, accuracy, civil engineer, mapping, methodology, machine learning |
| Subjects: | research methods, spatial and geospatial analysis, analytical methods, professional practice, statistical analysis, engineering methods, professional development, artificial intelligence, profession |
| Topics: | Digital Applications, Information Management, Research Practice, Roles and Professions, Engineering Principles |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here