Highway and bridge agencies use several systematic inspection approaches to ensure an acceptable standard for their assets in terms of safety, convenience, and economic value. The Bridge Condition Index (BCI), used by the Ontario Ministry of Transportation, is defined as the weighted condition of all bridge elements to determine the rehabilitation priority for the bridge. Therefore, accurate forecasting of BCI is essential for bridge rehabilitation budgeting and planning. The large amount of data available about bridge conditions for several years enables the use of different mathematical models to predict future BCI. This research focuses on investigating different classification models developed to predict the BCI in the province of Ontario, Canada, based on the publicly available historical data for 2,802 bridges over a period of more than 10 years. Predictive models used in this study include k-nearest neighbors (k-NN), decision trees (DTs), linear regression (LR), artificial neural networks (ANN), and deep learning neural networks (DLN). These models are compared and statistically validated via cross validation and paired $\mathrm{t}$ -test. The decision tree model showed acceptable predictive results (within 0.25% mean relative error) when predicting the future BCI and is the recommended option based on its performance and certainty in posterior maintenance decision making for the selected case study.

Modular construction has the advantage of producing structures quickly and efficiently, while not requiring the resources to build a structure to be co-located with the construction site. Large modules can be produced in quality controlled environments, and then shipped to the construction site and assembled with minimal labor requirements. An additional advantage is that once the modules are on-site, construction can proceed extremely quickly. This is ideal for situations where compressed schedules are required in order to meet client's time constraints. This paper examines using software simulation, specifically Simphony.NET, in the design and analysis of the construction process. This is done both before and after project execution to predict productivity and duration and also to allow for exploration of alternate construction scenarios.

In industrial modular construction, components fabricated in the shop are assembled into modules before being transported to construction sites. This approach offers improved construction efficiency, higher quality products, and the opportunity to work in a controlled environment that offers more predictable schedules. A distinct category of modularized construction is that of piping structures where prefabricated segments of pipes, called pipe spools, are arranged and attached to a steel structure to speed up on-site assembly. Determining when a particular pipe spool is ready for delivery to the site is affected by many factors, including material availability, workforce performance and availability, shop loading conditions, and shop capacity. These factors combine to produce a high degree of uncertainty when estimating the fabrication duration. This paper investigates the improvement of delivery time prediction of pipe spools that are fabricated in a manufacturing facility. The study’s main contribution is to propose a data-driven machine-learning model that accurately predicts the total fabrication duration in a pipe spool fabrication facility. The developed model is expected to assist production managers to plan for the appropriate workforce and material delivery requirements. The proposed approach utilizes a combination of product composition and real-time loading status of the shop to develop and examine several tree-based machine-learning models. The approach was evaluated using records of 6,989 pipe spools. The overall prediction performance was improved by a reduction of 35% in the mean absolute percentage error compared to original heuristic estimates. The results also show a strong positive correlation of 0.8 between the actual and predicted values.