by Jeffrey C Kadlowec, Registered Architect

Construction projects are filled with uncertainty from conception to completion and beyond. These variables fluctuate within probable ranges, though an increase in any risk factor can affect project performance, typically results in greater cost and longer duration (Feng 2022). Decision making in early stages have the greatest impact on outcome when uncertainty is high, while changes made at later stages will come at highest costs. Proper planning is therefore crucial to project success and must incorporate financing, design, building and operations by knowledgeable and experienced personnel from various industries.

Given the high levels of uncertainty, several approaches are commonly used. The probability based methods of sensitivity analysis and Monte Carlo experiments utilize numeric distributions to calculate possible outcomes (Feng 2022). Though these methods still rely heavily on prior knowledge, analysis of historic data can provide evidence in support of critical decisions. Mega projects which are uniquely complex and deep in uncertainty require newer approaches like discrete-event simulation to study performance of construction schemes. Building information modeling was developed in part to model these types of projects.

Life cycle assessment and energy efficiency calculations can be derived from these building models. This data will guide design decisions, explore alternatives and recommend changes that lead to further optimization of the building as a whole (Harter 2020). As design moves through phases from schematic through development to documentation, the representation of volumes and areas becomes more refined. Code requirements and dimensional data are incorporated along with building components, systems and equipment. In similar ways that material and quantity takeoff are made, performance studies will provide valuable information related to embedded and operational energy.

Long-term effect on the environment are often overlooked. Sustainable development must consider the impact on future generations regarding climate protection and disaster control. Multiple criteria decision analysis is another approach to managing the complexity of numerous influences. Global warming, construction cost, sound transmission, heat transfer, installation time, and recycled content are all factors to consider in design and construction (Scherz 2019). Actual building data often deviates significantly from design models. This can result from inaccurate assumptions, poor design practices, improper construction execution, and due to operation control strategies (Boxer 2017). Peer comparison and self-benchmarking can reveal those faults. Facilities managers should collect and record data from monitoring building energy to aid in determining appropriate corrections.

Uncertainty of future revenue influences the timing of investments in construction. Since the payoffs of a project only accrue after completion, the decision to start is irreversible (Thijssen 2015). A common practice is to evaluate projects on a benefit-to-cost ratio (BCR). This is the ratio of the estimated value of future revenue to the current estimate of construction. The value for money (VFM) ranges from very poor to very high for a BCR of less than zero to greater than four. The majority of large-scale projects are completed over budget and behind schedule. Project managers need better tools for deciding whether to 1) begin construction, 2) abandon the project during construction, and 3) start operations after completion (Thijssen 2022). This measure should account for 1) dynamic explicit, 2) separate costs and benefits, 3) value of the option to abandon, and 4) an independent project threshold.

References
Boxer, Eric; Henze, Gregor & Hirsch, Adam. (2017). A Model-Based Decision Support Tool for Building Portfolios Under Uncertainty. Automation in Construction. 78: 34-50. dx.doi.org/10.1016/j.autcon.2017.01.0150926-5805/.
Feng, Kailunl; Wang, Shuo; Lu, Weizhuo; Liu, Changyong & Wang, Yaowu. (2022). Planning Construction Projects in Deep Uncertainty: A Data-Driven Uncertainty Analysis Approach. American Society of Civil Engineers. DOI: 10.1061/(ASCE)CO.1943-7862.0002315.
Harter, Hannes; Singh, Manav Mahan; Schneider-Marin, Patricia & Lang, Werner. (2020). Uncertainty Analysis of Life Cycle Energy Assessment in Early Stages of Design. Energy & Buildings. 208. doi.org/10.1016/j.enbuild.2019.109635.
Scherz, A & Vafadarnikjoo, A. (2019). Multiple Criteria Decision Analysis Under Uncertainty in Sustainable Construction: A Neutrosophic Modified Best-Worst Method. Sustainable Built Environment Conference. doi:10.1088/1755-1315/323/1/012098.
Thijssen, Jacco. (2015). A Model for Irreversible Investment with Construction and Revenue Uncertainty. Journal of Economic Dynamics & Controls. 57: 250-266. dx.doi.org/10.1016/j.jedc.2015.06.001.
Thijssen, Jacco. (2022). Optimal Investment and Abandonment Decisions for Projects with Construction Uncertainty. European Journal of Operational Research. 298: 368-379. doi.org/10.1016/j.ejor.2021.07.003.