Improving the planning and design phases of construction projects by using a Case-Based Digital Building System

Soto, Borja García de; Streule, Thomas; Klippel, Michael; Bartlomé, Olin; Adey, Bryan T.

doi:10.1080/15623599.2018.1502929

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…Similarly, machine learning coupled with artificial intelligence (AI) can significantly benefit procurement management by automating data analysis, predicting material demands, optimizing supplier selection, and reducing procurement costs through intelligent decisionmaking [55]. Digital building systems play a vital role in enhancing the effective planning of construction projects through visualization and simulation [56]. Augmented reality technology (ART) further complements this by providing on-site, real-time information and interactive 3D models, enabling improved decision-making and collaboration among project stakeholders [4].…”

Section: Component 1-pre-contract Stagementioning

confidence: 99%

Application of Digital Technologies Tools for Social and Sustainable Construction in a Developing Economy

Oke,

Aliu,

Jamir Singh

et al. 2023

Sustainability

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This study aims to evaluate the diverse application areas of digital technologies (DTs) within the Nigerian construction industry, with the intention of gaining insights into their potential benefits, challenges, and opportunities for enhancing efficiency, productivity, and overall performance. This will help us to understand how innovative technologies can improve the various stages of construction projects. To achieve the objectives of this study, a convenience sampling approach was employed to distribute closed-ended questionnaires among construction professionals located in Lagos State. Several statistical tools were used to analyze the obtained data, including percentages, frequencies, mean item scores, and exploratory factor analyses were performed to gain a comprehensive understanding of the dataset. The major findings from the study indicated that architectural design, cost planning, building system analysis, structural analysis, and contract documentation are areas in which DT is mostly applied in the construction industry. Further analysis using factor analysis revealed four clusters of application areas as the pre-contract stage, construction stage, post-contract stage, and modeling stage. The findings of this study offer valuable insights into the effective utilization of DT in the construction industry, thereby contributing to informed decision-making and improved project management practices. The insights gained from this research can inform industry professionals, policymakers, and stakeholders in making informed decisions to drive positive changes and innovation within the sector.

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Section: Component 1-pre-contract Stagementioning

confidence: 99%

Application of Digital Technologies Tools for Social and Sustainable Construction in a Developing Economy

Oke,

Aliu,

Jamir Singh

et al. 2023

Sustainability

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“…Frequent design changes requested by the owner may lead to a higher probability of design defects [65]. Design changes will be measured on a scale from 1 to 10, with 1 indicating minor few design changes and 10 representing frequent major design changes.…”

Section: Design Changesmentioning

confidence: 99%

Development of a Prediction System for Design Defects of Buildings

Nouh,

El-Kasaby,

Aboulata

2023

Civ Eng J

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The research aimed to establish a quantitative model to predict the occurrence of five types of design defects: unsafe design, incompatible design, inefficient design, design ambiguity, and design delays. It also examined the contractor's liability for design to determine who is ultimately responsible for design defects disputes between the employer and contractor. The study employed a descriptive methodology to identify fifteen potential influencing factors on design defects. Data were collected from 42 construction projects and tested using Design of Experiments (DOE). Out of these fifteen factors, only five—design schedule, design firm experience, stakeholders’ involvement, project delivery system, and information accuracy—had a significant impact on design defects. These factors were used in the development of a new model to predict the occurrence of design defects. The model was validated using data that was held separate from the model development process for validation purposes. The calculated Mean Absolute Percentage of Error (MAPE) for the new model is 19%, which is considered a "good" prediction accuracy. The research achieved three significant milestones: 1) It identified the ultimate responsibility for the consequences of design defects in construction projects across different jurisdictions. This information can be used to resolve potential disputes between employers, designers, and contractors’ organizations. 2) It provided guidelines on how to minimize design defects in construction projects, thereby mitigating the consequences of such defects. 3) It developed a new model to predict the occurrence of design defects in construction projects. This model aids decision-makers in responsible organizations during the process of predicting the appropriate reserve to be added to the project schedule and budget. Consequently, it helps mitigate the risk of design defect-related delays and cost overruns. Doi: 10.28991/CEJ-SP2023-09-017 Full Text: PDF

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“…The traditional multiple regression analysis (RA) has been the tool most used by researchers (e.g., [3,4]). Artificial neural networks (ANN) have gained some expression for data modeling in various engineering problems, including cost estimation (e.g., [1,5,6]), and case-based reasoning (CBR) is also being used in various tasks related to construction management (e.g., resource estimation- [7]; duration estimation- [8]). A review on CBR use for construction management can be found in [9] and its use for cost estimation can be found in [10][11][12].…”

Section: Literature Reviewmentioning

confidence: 99%

Cost Modeling from the Contractor Perspective: Application to Residential and Office Buildings

et al. 2021

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For the majority of the contractual arrangements used in construction projects, the owner is not responsible for the cost deviations due to the variability of labor productivity or material price, amongst many other aspects. Consequently, the cost performance of a project may be entirely distinct for the owner and the contractor. Since the majority of quantitative research on cost estimation and deviation found in the literature adopts the owners’ perspective, this research provides a contribution towards modeling costs and cost deviation from a contractor’s perspective. From an initial sample of 13 residential buildings and 10 office building projects, it was possible to develop models for cost estimation at the early stage of development, including both endogenous and exogenous variables. Although the sample is relatively small, the authors were able to fully analyze all the cost data, using no secondary sources of data (which is very frequent in cost modeling studies). The statistically significant variables in the cost estimation models were the areas above and below ground and the years following the 2008 financial crisis, including the international bailout (2011–2014) period. For estimating the unit cost, a nonlinear model was obtained with the number of underground and total floor, the floor ratio, and the years following the 2008 financial crisis, including the international bailout (2011–2014) period as predictors. For the office buildings, a statistically significant correlation was also found between the cost deviation and number of underground floors.

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Improving the planning and design phases of construction projects by using a Case-Based Digital Building System

Cited by 5 publications

References 33 publications

Application of Digital Technologies Tools for Social and Sustainable Construction in a Developing Economy

Application of Digital Technologies Tools for Social and Sustainable Construction in a Developing Economy

Development of a Prediction System for Design Defects of Buildings

Cost Modeling from the Contractor Perspective: Application to Residential and Office Buildings

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