Projects


Sponsors

Active Projects

1. Improving Cost Estimation and Budget Planning with New Michigan Highway Construction Cost Index, PI

     funded by the Michigan Department of Transportation (MDOT)

Objective: This research will enhance the accuracy of cost estimating and budget planning by leveraging  HCCI.  In particular, new estimation methods, incorporating economic factors and project/pay item-level index, are needed to allow owners to estimate and budget construction projects with improved accuracy. 

2. Evaluation of MDOT's Methodology for Estimating Work Zone User Delay Times & Costs, Co-PI

     funded by the Michigan Department of Transportation (MDOT)

Objective: This research is to provide a solution for estimating user delay times and costs in a cost-effective way while meeting MDOT’s needs. The proposed tool will incorporate existing work zone analysis tools and a cost analysis approach based on the research team’s previous development experiences. 

3. Enhancing the Development of Ohio’s Statewide Highway Construction Cost Index, PI

     funded by the Ohio Department of Transportation (ODOT)

Objective: This research is to investigate and compare various approaches to calculating the highway construction cost index and further develop a new method for Ohio DOT. 

Completed Projects

1. Competitive Bidding in Construction Contracting, PI

     funded by the Michigan Department of Transportation (MDOT),  January 2021 - September 2022

Objective: This research is to investigate the impact of bidding procedures and behaviors on competitive bidding (e.g., encouraging competition) and develop new methodologies and tools for bid analysis and cost estimating, especially for traffic control.

2. Michigan Transportation Construction Price Index, PI

      funded by the Michigan Department of Transportation (MDOT),  January 2020 - April 2021

Objective: With existing bid data, this research is to investigate a method of developing and maintaining the Michigan state Highway Construction Cost Index  (HCCI). By doing such, MDOT can use HCCI to project future funding needs, develop better construction cost estimates, and identify reasons that cost estimating trends may be high or low.

3. Automated Manufacturing-centric BIM to Facilitate Building Panel Prefabrication

     funded by a Canadian Natural Sciences and Engineering Research Council (NSERC) - Industrial Research Chairs Grants Program

Objectives: The effectiveness of BIM models utilised to communicate information among project stakeholders plays an important role in determining the construction efficiency (Alwisy & Al-Hussein, 2010). To make a BIM model fit for use by contractors and sub-contractors, it needs to be designed with sufficient construction details (i.e., construction-specific information) for specific application needs. Such detailed BIM models are of vital importance in project coordination and decision making in relation to construction material takeoff and usage during the construction stage (Liu et al., 2015). Nevertheless, developing a construction-centric BIM model through manual modelling is time-consuming and error-prone. Furthermore, trades’ know-how in the construction industry remains mainly in the minds of experienced trades people and is generally missing from existing design software. As such, construction-centric designs from existing BIM design software usually fall short of practical constructability analysis, resulting in considerable material waste and re-work in the field. 

Methodology: This study thus explores a BIM-based automated approach to boarding design (i.e., optimizing and modelling sheathing and drywall layout design). This approach advances the current practice in the field by adapting BIM design models for construction practitioners in an automated fashion. As illustrated, domain knowledge, including practical trades’ know-how and construction best practice, are comprehensively interpreted as object-based computer-implementable generative rules (i.e., machine-readable codes), and these rules are encoded into the boarding design algorithm within the modelling system. The design algorithm takes pertinent rich building information (i.e., geometric and semantic information) from BIM models to formulate various boarding design scenarios under construction constraints. Each design alternative is analyzed to generate a thorough cutting list of building elements (i.e., quantities of sheathing/drywall sheets). Afterward, the cutting list and raw material (i.e., board sheets) of nominal sizes available in the market are fed into the material cutting-stock optimizer to generate the optimized cutting plan with minimal material waste. Finally, the design alternative with

                         Automated manufacturing-centric BIM

minimal material waste among all feasible design alternatives is identified as the optimized layout design and is modelled in the given BIM model. Along with optimized layout design, the material cutting plan and purchase plan are generated by the cutting-stock optimizer to assist construction practitioners in planning and managing field operation. 

4. Development of Cloud Based Collaborative BIM Modeling Software

     funded by a Canadian NSERC and Alberta Innovates – CRD Grants Program

Objectives: The objective of this research is to develop cloud-based building information modelling (BIM) programs that have significant implications for both business-to-business and business-to-consumer sales, and that lead to innovations in the building industry, especially in residential construction. 

5. Ontology-based Semantic Approach for Construction-oriented Quantity Take-off from BIM Models

     funded by a Canadian NSERC – CRD Grants Program

Objectives: In building information modelling (BIM), rich information is generally embedded into the BIM model as properties for parametric building objects, and is exchangeable among project stakeholders and BIM design programs—a key feature of BIM for enhancing communication and work efficiency. However, BIM itself is a purpose-built, product-centric information database and lacks domain semantics such that extracting construction-oriented quantity take-off information for the purpose of construction workface planning still remains a challenge. Moreover, some information crucial to construction practitioners, such as the topological relationships among building objects, remains implicit in the BIM design model. This restricts information extraction from the BIM model for downstream analyses in construction. To address identified limitations, this study proposes an ontology-based semantic approach to extracting construction-oriented quantity take-off information from a BIM design model. This approach allows users to semantically query the BIM design model using a domain vocabulary, capitalizing on building product ontology formalized from construction perspectives. As such, quantity take-off information relevant to construction practitioners can be readily extracted and visualized in 3D in order to serve application needs in the construction field. 

Approach: As depicted in the figure, a construction-oriented product ontology is developed by formalizing domain terms, their interrelationships, and properties in the light-frame building industry. With the exception of building terms in the existing BIM model, such as IfcWall, and IfcSlab, some other terms including stud, plate, L-connection, T-connection and so forth are added into the product ontology. It is noteworthy that construction-oriented product ontology not only contains formalized terms from domain knowledge, but also includes specific BIM data. In order to populate this product ontology (i.e., ontology terms) with specific building information (i.e., ontology individuals), the terms in the formalized product ontology are first mapped with the BIM modelling elements within a building modelling tool (e.g., Revit, Tekla) or vendor-independent platform (e.g., IFC). Then, the BIM design model is analyzed against ontology terms using “BIM data parser” in order to extract specific BIM data, whereas “ontology individual generator” transforms extracted BIM data into 

Semantic Quantity Take-off

the product ontology. Ontology reasoning enabled by “ontology reasoner” can be further applied in order to infer new information or facts on the basis of explicit BIM data. Finally, an ontology-enhanced BIM model is generated for applications in construction planning. Semantic query can be formulated against “semantic query processor” in order to semantically query the ontology-augmented BIM model, thereby obtaining the required construction-oriented QTO information. 

6. BIM-simulation In-depth Integration for Panelized Construction Scheduling under Resource Constraints

     funded by a Canadian NSERC – Collaborative Research and Development (CRD) Grants Program

Objectives: Building information modelling (BIM) has been recognized as an information technology with the potential to profoundly change the AEC industry, and has drawn attention from numerous scholars within the construction domain. Despite the reported advancements pertaining to BIM in previous studies, the use of BIM in planning construction has not yet reached its full potential. For example, rich building information embedded in BIM models is not being fully utilized in order to facilitate the automatic generation of project schedules, entailing substantial manual work, especially in automated construction sequencing and information exchanges between BIM modelling tools and scheduling tools. This research explores a BIM-based integrated scheduling approach that automatically generates optimal component-centric activity-level schedules for panelized construction projects by performing simulation-based scheduling from the BIM model. 

Approach: This approach achieves in-depth integration among BIM product models, process simulation, and optimization models, thereby facilitating automatic generation of optimized component-centric activity-level schedules for construction projects. Within the integrated system, a BIM product model is supplemented with work breakdown structure (WBS) information, while the process simulation model can gain rich product information (including quantity take-offs) from BIM and work package information (e.g., operation productivity) from WBS in order to generate component-centric activity-level construction schedules. Moreover, this research requires the planner to build part of the activity network manually as per constraints that need to be observed and remain constant during construction, instead of a complete activity network as in previous research, in order to address the difficulty of manually building a complete activity network and overcome the limitation of defining a fixed activity network in project planning. The dynamic precedence constraints on activities will be derived at run time of discrete event simulation through BIM-simulation integration, whereas resource-induced precedence constraints are addressed by using priority dispatching rules to allocate limited resources in the simulation model. In addition, an evolutionary algorithm, namely, particle swarm optimization, is incorporated into the methodology to optimize the construction sequences with the objective of minimizing project duration under resource constraints. 

BIM-based integrated scheduling approach