This paper proposes a framework for simulating weather-sensitive construction projects that are executed under extreme weather conditions. It applies the framework steps to enable simulating and planning pipeline construction activities under severe cold weather conditions. The uncertainties caused by weather, such as extreme cold, heat, wind, or precipitation, can significantly affect a project’s schedule and produce significant deviations from the baseline schedule. The proposed framework structures a project in the way an engineer would approach it, setting out a breakdown of work activities to quantify weather effects and account for their impact on the project baseline. The proposed weather-sensitive construction simulation framework is employed to determine the effects of weather on the construction process of high-density polyethylene (HDPE) pipe installation. The relevant simulation findings are reported to clarify the impact of extreme weather events on construction projects and to assist in proj...

Today's Architecture, Engineering, Construction and Facility Management (AEC/FM) industry has to deal with complex obstacles (e.g., aging infrastructure, the protection of the natural environment, the need for resilient infrastructure). Among several technical fields that have been adopted and advanced, computer simulation has been widely researched and practiced for the effective delivery and maintenance of capital projects. Considering the unprecedented problems of today's infrastructure and the rapid advancements in simulation research, it is very timely to investigate what grand challenges exist in simulation that, if properly addressed, can help create a more sustainable and resilient engineering of infrastructure lifecycle. To this end, this paper aims to identify grand challenges in simulation by presenting major areas of interest that can benefit the AEC/FM domain and discussing three specific areas identified as challenges in simulation: 1) realistic simulation modeling; 2) applicability of simulation models to the industry; and 3) academic and educational obstacles. Specific foci are the knowledge gaps in these major areas, and the current efforts to mitigate these gaps based on extensive literature reviews. This paper also proposes the next steps for addressing these challenges based on the findings herein.

The generation of well-defined and moderately sized field installation work packages for the construction workforce, referred to as workface planning, has been recently employed to plan large-scale industrial construction projects under tight schedules. However, traditional CPM-based scheduling of several thousand work packages (e.g. 5000 activities multiply by 10 work packages per activity on average) is a tedious, error prone process. Defining proper logics and controlling congestion among work packages crossing several work areas, and also effective resource allocation over time are other major challenges in workface planning. This paper presents a novel simulation-based framework to implement workface planning for large-scale industrial construction projects. This framework proposes a time-stepped discrete event simulation-based modelling for dynamic resource allocation based on congestion and other constraints on the job site. The proposed method is demonstrated and tested against traditional CPM-based solutions based on an actual case study.

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.