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1. Generative AI in Lean Construction: A Scoping Review

   GanjiRad, M; Ilbeigi, M (June 2025, IGLC)

   Seppänen, O; Koskela, L; Murata, K (Ed.)

   Generative Artificial Intelligence (AI), specifically Large Language Models (LLMs), has the potential to reshape construction management practices. These novel solutions can transform lean construction by enabling real-time data analysis, streamlined communication, and automated decision-making across project teams. They can facilitate enhanced collaboration by generating insights from vast construction data sets, improving workflow efficiency, and reducing waste. Additionally, LLMs can support predictive modeling, proactive risk management, and knowledge sharing, aligning with lean principles of maximizing value and minimizing inefficiencies. Given the recent advancements in generative AI, it is critical to systematically shape future research directions by building on the existing body of knowledge and addressing key knowledge gaps. The first step toward identifying knowledge gaps and uncovering critical areas that remain underexplored is to systematically analyze the existing body of knowledge. Therefore, this study conducts a scoping review to synthesize the extent, range, and nature of existing studies that have proposed novel solutions using generative AI and LLMs for various aspects of construction management. The outcomes of this systematic scoping review will help identify potential research directions for future studies in this domain. 

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2. Analyzing the impact of the aspect ratio of a building on concrete use in its structure

   https://doi.org/10.14311/APP.2022.33.0133  

   Dixit, Manish K.; Pradeep Kumar, Pranav (March 2022, Acta Polytechnica CTU Proceedings)

   Buildings consume over half of annual energy supply as embodied and operating energy in their construction and operation releasing harmful emissions to the atmosphere. Over 90 % of the embodied energy is attributed to construction materials used in building structure, envelope, and interiors that must be reduced to minimize material use. Concrete is one of the major materials that contributes significantly to the energy and carbon footprint of buildings, as it is responsible for 5-9 % of global carbon emission. Because most of the concrete use in the building sector occurs in building structures, assessing how building design parameters influence its environmental sustainability is important. One of the design parameters that impact the sustainability of buildings is the aspect ratio, which is defined as the ratio of horizontal to vertical surface area of a building. A building with the same floor area can be designed horizontally or vertically with different aspect ratios, which will influence its structural design and eventually the amount of concrete used in the building. In this paper, we examine how aspect ratio may affect the environmental sustainability of a buildings foundation, structural framing, and slab. We model the structure of a generic building with different aspect ratio to analyze if aspect ratio can help reduce the energy and carbon embodied in reinforced concrete structures. 

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3. Exploring the challenges of implementing parametric modeling to support robotic construction

   McClymonds, A. (May 2023, Annual meeting Canadian Society of Civil Engineers)

   Desjardin, S. and (Ed.)

   Building Information Modeling (BIM) is a critical data source for constructing new structures depicting the inner workings of the systems and components in detail. However, current modeling practices are based on traditional construction methods, resulting in insufficient details within the BIM model to support robotic construction for many building systems. The model’s level of development (LOD) needs to be increased to facilitate the changes in data requirements. One method that allows for increased LOD is computational modeling; however, many factors can influence the process. Therefore, this study investigates challenges for implementation to increase the LOD for building to enable robotic construction. Dynamo is used as the computational modeling software in conjunction with Autodesk Revit to accomplish this. A process was created to place various components, such as concrete masonry units (CMUs), in their final design location and extract information utilizing these platforms for masonry construction. However, challenges were met during this process, including material naming conventions, tolerance/specification inputs, wall openings/lintels, and component/material libraries. The challenges presented during the implementation of the Dynamo mirror what the literature shows for supporting technological infrastructure BIM and mobile robot construction. To accomplish this research, an extensive literature review was completed, along with documentation of challenges during the development and implementation of the script. 

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4. Analyzing Embodied Energy and Embodied Water of Construction Materials for an Environmentally Sustainable Built Environment

   https://doi.org/10.1088/1755-1315/1122/1/012045  

   Dixit, M K; Pradeep Kumar, P (December 2022, IOP Conference Series: Earth and Environmental Science)

   Abstract Buildings consume over 40% of global energy in their construction and operations contributing to over 39% of global carbon emission each year. This huge environmental footprint presents an excellent opportunity to reduce energy use and help deliver an environmentally sustainable built environment. Most of the energy is consumed by buildings as embodied energy (EE) and operational energy (OE). EE is used directly and indirectly during buildings’ initial construction, maintenance and replacement, and demolition phases through construction products and services. OE is used in the processes of heating, cooling, water heating, lighting, and operating building equipment. Most environmental optimization research has been centered on energy and carbon emission overlooking another critical sustainability aspect, water use. Each building also consumes a significant amount of freshwater as embodied water (EW) or virtual water in its initial construction, maintenance and replacement, and demolition phases. Since each primary and secondary energy source depletes water in its extraction, refinement or production, there is also a water expense associated with EE and OE use that must also be included in total EW use. The total EW, therefore, includes both non-energy and energy related water use. Research suggests that there are tradeoffs between EE and EW that may complicate design decisions such as material selection for environmental sustainability. In other words, a material selected for its lower EE may have higher EW and selecting such a material may not help reach environmental sustainability goals since water scarcity is becoming a grave problem. In this paper, we created an input-output-based hybrid (IOH) model for calculating and comparing EE and EW of building materials frequently used in building construction. The main goal is to examine and highlight any tradeoffs that may exist when selecting one material over another. The results reveal that there is a weak correlation between EE and total EW that is the sum of energy and non-energy water use, which means that a design decision made solely based on EE may conflict with EW. The share of energy related water use in total EW of construction materials also varies significantly (2.5%-31.2%), indicating that reducing energy use alone may not be sufficient to reduce freshwater use; additional efforts may be needed to decrease the use of materials and processes that are water intensive. The results of this study are significant to achieving the goal of creating a truly sustainable built environment. 

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5. A Non-Parametric Approach for Setting Safety Stock Levels

   https://doi.org/10.2139/ssrn.3624998  

   Saldanha, John; Price, Brad; Thomas, Douglas J. (June 2020, SSRN Electronic Journal)

   In practice, lead time demand (LTD) can be non-standard: skewed, multi-modal or highly variable; factors that compromise the validity of the classic approaches for setting safety stock levels. Motivated by encountering this problem at our industry partner, we develop an approach for setting safety stock levels using the bootstrap, a widely-used statistical procedure. We extend prior research that has used the bootstrap for quantile estimation to address the multi-parameter estimation of safety stocks. We develop a multivariate central limit theorem for the bootstrap mean and bootstrap quantile -- components of the safety stock calculation -- highlighting why the generalization of these bootstrap methods is critical for inventory management. These results provide a theoretical underpinning for the bootstrap estimator of safety stock and permit the construction of confidence intervals for safety stock estimates, allowing decision makers to understand the reliability with which the desired service level will be achieved. Building on our theoretical results, and supported by numerical experiments, we provide insights on the behavior of the bootstrap for various LTD distributions, which our results demonstrate are critical when employing the bootstrap method. Implementation results with our industry partner indicate our approach is quite effective in setting safety stock levels. 

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