Improving occupational safety in office spaces in the post-pandemic era

The rise of COVID-19 and its consequent socio-economic losses raised concerns regarding the resilience of workplaces against widespread infectious diseases. During the COVID-19 pandemic, several outbreaks occurred in workplaces. As a result, local authorities implemented restrictive interventions (e.g., lockdown and social distancing) to control the spread of this disease in different contexts. Despite the short-term positive impacts of these interventions, they are not sustainable in the long run due to their associated economic costs to industries. Hence, in the post-pandemic era, novel and non-restrictive interventions are needed to limit the spread of similar diseases inside workplaces during epidemics. Herein, several non-restrictive interventions have been introduced to limit the spread of COVID-19 in office spaces. The effectiveness of these interventions is tested in generic office space by a disease spread simulator (CoDiSS), which is based on stochastic agent-based modeling. As a result, this research identifies the most impactful interventions based on the simulation outcomes and offers practical strategies to improve occupational safety within office environments. Our findings help enhance safety in the ever-transforming occupational environment by limiting the spread of infectious diseases in workplaces using non-restrictive interventions

A framework to model the spread of infectious diseases on construction sites using hybrid agent-based modelling and Monte Carlo simulation

The construction industry has been severely affected by COVID-19 restrictionsresulting in several challenges in form of supply chain disruptions, performance loss, and limited workforce interactions. The construction industry initially needs to understand and quantify the impacts of COVID-19 on each aspect of the industry. Simulation techniques are powerful tools for this purpose, which enable modellers to run construction projects virtually and assess their performance under different circumstances, scenarios or settings. In this paper, a hybrid simulation framework is developed using agent-based modelling (ABM) and Monte Carlo simulation techniques (MCS) to evaluate the impact of the spread of COVID-19 on the performance of construction projects. The proposed simulation framework enables the construction modellers to capture the interactions between construction workers effectively and to determine the impact of restrictions on the overall project performance. It also helps practitioners in the post-COVID-19 era by testing multiple virus spread scenarios on construction sites and minimize the adverse impacts of restrictions on the project performance

Framework for Risk Identification of Renewable Energy Projects Using Fuzzy Case-Based Reasoning

Construction projects are highly risk-prone due to both internal factors (e.g., organizational, contractual, project, etc.) and external factors (e.g., environmental, economic, political, etc.). Construction risks can thus have a direct or indirect impact on project objectives, such as cost, time, safety, and quality. Identification of these risks is crucial in order to fulfill project objectives. Many tools and techniques have been proposed for risk identification, including literature review, questionnaire surveys, and expert interviews. However, the majority of these approaches are highly reliant on expert knowledge or prior knowledge of the project. Therefore, the application of such tools and techniques in risk identification for renewable energy projects (e.g., wind farm and solar power plant projects) is challenging due to their novelty and the limited availability of historical data or literature. This paper addresses these challenges by introducing a new risk identification framework for renewable energy projects, which combines case-based reasoning (CBR) with fuzzy logic. CBR helps to solve problems related to novel projects (e.g., renewable energy projects) based on their similarities to existing, well-studied projects (e.g., conventional energy projects). CBR addresses the issue of data scarcity by comparing novel types of construction projects to other well-studied project types and using the similarities between these two sets of projects to solve the different problems associated with novel types of construction projects, such as risk identification of renewable energy projects. Moreover, the integration of fuzzy logic with CBR, to develop fuzzy case-based reasoning (FCBR), increases the applicability of CBR in construction by capturing the subjective uncertainty that exists in construction-related problems. The applicability of the proposed framework was tested on a case study of an onshore wind farm project. The objectives of this paper are to introduce a novel framework for risk identification of renewable energy projects and to identify the risks associated with the construction of onshore wind farm projects at the work package level. The results of this paper will help to improve the risk management of renewable energy projects during the construction phase

Using Artificial Neural Networks to Model Bricklaying Productivity

The pre-planning phase prior to construction is crucial for ensuring an effective and efficient project delivery. Realistic productivity rates forecasted during pre-planning are essential for accurate schedules, cost calculation, and resource allocation. To obtain such productivity rates, the relationships between various factors and productivity need to be understood. Artificial neural networks (ANNs) are suitable for modelling these complex interactions typical of construction activities, and can be used to assist project managers to produce suitable solutions for estimating productivity. This paper presents the steps of determining the network configurations of an ANN model for bricklaying productivity

Domain-specific risk assessment using integrated simulation: A case study of an onshore wind project

Although many quantitative risk assessment models have been proposed in literature, their use in construction practice remain limited due to a lack of domain-specific models, tools, and application examples. This is especially true in wind farm construction, where the state-of-the-art integrated Monte Carlo simulation and critical path method (MCS-CPM) risk assessment approach has yet to be demonstrated. The present case study is the first reported application of the MCS-CPM method for risk assessment in wind farm construction and is the first case study to consider correlations between cost and schedule impacts of risk factors using copulas. MCS-CPM provided reasonable risk assessment results for a wind farm project, and its use in practice is recommended. Aimed at facilitating the practical application of quantitative risk assessment methods, this case study provides a much-needed analytical generalization of MCS-CPM, offering application examples, discussion of expected results, and recommendations to wind farm construction practitioners

Enhancing resilience in construction against infectious diseases using stochastic multi-agent approach

To recover from the adverse impacts of COVID-19 on construction and to avoid further losses to the industry in future pandemics, the resilience of construction industry needs to be enhanced against infectious diseases. Currently, there is a gap for modeling frameworks to simulate the spread of infectious diseases in construction projects at micro-level and to test interventions’ effectiveness for data-informed decision-making. Here, this gap is addressed by developing a simulation framework using stochastic agent-based modeling, which enables construction researchers and practitioners to simulate and limit the spread of infectious diseases in construction projects. This is specifically important, since the results of a building project case-study reveals that, in comparison to the general population, infectious diseases may spread faster among construction workers and fatalities can be significantly higher. The proposed framework motivates future research on micro-level modeling of infectious diseases and efforts for intervening the spread of diseases in construction projects

Application of fuzzy logic integrated with system dynamics in construction modeling

Construction projects are complex systems and their behaviors are extremely dynamic throughout their life cycles. This complexity and dynamism makes them perfect candidates for system dynamics modeling for management purposes. However, ill-known variables, a lack of historical data, uncertainties, subjectivity, and the use of linguistic terms in defining construction variables all complicate the application of system dynamics in construction. Fuzzy logic is an artificial intelligence technique that has the ability to model vague, incomplete, linguistically-expressed, and subjective data in a precise way. Since the quality of system dynamics modeling relies significantly on the accuracy of the data, integrating system dynamics with fuzzy logic makes for a powerful construction project simulation tool. Integrated fuzzy system dynamics models can effectively capture the dynamic characteristics of construction projects and simulate them more precisely by using fuzzy logic to capture subjective and linguistically-expressed information. In this paper, we illustrate how fuzzy logic and system dynamics can be integrated for use in construction project simulation. Moreover, we present a review of potential applications of integrated fuzzy system dynamics models in construction. Finally, we compare the performance of system dynamics with integrated fuzzy system dynamics for a construction-related problem adopted from the literature, and discuss how integrating fuzzy logic can enhance system dynamics capabilities for construction modeling.Non UBCUnreviewedFacultyOthe

Modeling Earthmoving Operations in Real-Time Using Hybrid Fuzzy Simulation

Predicting and optimizing performance in earthmoving operations is critical, because they are essential to many construction projects. The complexity of modeling earthmoving operations remains challenging, even with several modeling techniques available, including simulation. This paper advances the state-of-the-art of modeling earthmoving operations by introducing a hybrid fuzzy system dynamics–discrete event simulation framework with the capacity to: capture the dynamism of performance in earthmoving operations; capture subjective uncertainty of several factors affecting them; model their sequential nature and resource constraints; and determine actual travel time, in real time, using online navigation systems. Findings from this research confirm the proposed framework (1) extends the application of simulation techniques for modeling construction processes involving dynamic input variables and subjective uncertainty, through its ability to capture the non-probabilistic uncertainty of construction systems, and (2) when combined with the use of online navigation systems to assess trucks’ travel time, improves the accuracy of earthmoving operation models.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author