Research

0 pages, 1360 KiB

Open AccessArticle

Modeling the Relation between Building Information Modeling and the Success of Construction Projects: A Structural-Equation-Modeling Approach

by Ahsan Waqar, Idris Othman, Dorin Radu, Zulfiqar Ali, Hamad Almujibah, Marijana Hadzima-Nyarko and Muhammad Basit Khan

Appl. Sci. 2023, 13(15), 9018; https://0-doi-org.brum.beds.ac.uk/10.3390/app13159018 - 07 Aug 2023

Cited by 6 | Viewed by 1277

Abstract

Over the course of the last twenty years, building information modeling (BIM) has emerged as a firmly established construction methodology integrating fundamental principles. The implementation of BIM methodologies possesses the capability to augment the attainment of quality, cost, and schedule objectives in construction [...] Read more.

Over the course of the last twenty years, building information modeling (BIM) has emerged as a firmly established construction methodology integrating fundamental principles. The implementation of BIM methodologies possesses the capability to augment the attainment of quality, cost, and schedule objectives in construction endeavors. Notwithstanding the widespread adoption of BIM in the construction sector, the execution of BIM-related tasks frequently suffers from the absence of established methodologies. The objective of this study was to create a BIM application model through an examination of the correlation between BIM integration and the achievement of overall project success (OPS) in construction endeavors. In order to develop the BIM application model, feedback was solicited from a cohort of fourteen industry experts who assessed a range of BIM activities in light of prior research. The data that were gathered underwent exploratory factor analysis (EFA) in order to authenticate the results acquired from the expert interviews. Furthermore, construction professionals participated in structured surveys in order to evaluate the importance of said BIM practices. This study utilized partial least squares–structural equation modeling (PLS-SEM) to ascertain and authenticate the underlying framework and correlations between BIM implementation and OPS. The findings indicate a moderate correlation between the implementation of BIM and the success of a project wherein BIM is responsible for approximately 52% of the project’s overall success. To optimize project outcomes, it is recommended that construction companies prioritize the implementation of BIM practices. This study highlights the correlation between the utilization of BIM and favorable project results, emphasizing the necessity for the construction sector to adopt BIM as a revolutionary instrument to attain enhanced project achievements. Full article

(This article belongs to the Special Issue Advances in BIM-Based Architectural Design and System)

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14 pages, 2125 KiB

Open AccessArticle

Construction Cost Prediction Using Deep Learning with BIM Properties in the Schematic Design Phase

by DoYoon Park and SeokHeon Yun

Appl. Sci. 2023, 13(12), 7207; https://0-doi-org.brum.beds.ac.uk/10.3390/app13127207 - 16 Jun 2023

Viewed by 2413

Abstract

In the planning and design stage, it is difficult to accurately predict construction costs only by estimating approximate cost. It is also very difficult to predict the change in construction costs whenever the design changes. However, using the BIM model’s attribute information and [...] Read more.

In the planning and design stage, it is difficult to accurately predict construction costs only by estimating approximate cost. It is also very difficult to predict the change in construction costs whenever the design changes. However, using the BIM model’s attribute information and machine learning techniques, accurate construction costs can be predicted faster than when using the existing approximate cost estimate. In this study, building information such as ‘total area’, ‘floor water’, ‘usage’, and BIM attribute information such as ‘wall area’, ‘wall water’, and ‘floor circumference’ were used together to predict construction costs in the schema design stage. As a result of applying the machine learning technique using both the building design information and the BIM model attribute information, it was found that the construction cost was improved compared to the result of individual predictions of the building information or BIM attribute information. While accurately predicting construction costs using BIM’s attribute information has its limits, it is expected to provide more accuracy compared to predicting costs solely based on construction cost influencing factors. Full article

(This article belongs to the Special Issue Advances in BIM-Based Architectural Design and System)

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21 pages, 13116 KiB

Open AccessArticle

Enhancing the Stability and Placement Accuracy of BIM Model Projections for Augmented Reality-Based Site Management of Infrastructure Projects

by Youngsu Yu, Haein Jeon and Bonsang Koo

Appl. Sci. 2022, 12(21), 10798; https://0-doi-org.brum.beds.ac.uk/10.3390/app122110798 - 25 Oct 2022

Cited by 4 | Viewed by 1860

Abstract

The utilization of Building Information Modeling (BIM) on-site has been limited due to the lack of an appropriate medium for visualizing and accessing models and project data intuitively in the field. AROS, an Augmented Reality-based site management system, was developed to allow the [...] Read more.

The utilization of Building Information Modeling (BIM) on-site has been limited due to the lack of an appropriate medium for visualizing and accessing models and project data intuitively in the field. AROS, an Augmented Reality-based site management system, was developed to allow the visual projection of BIM models and relevant data directly to field personnel. Detailed field experiments with inspection experts revealed specific issues with the stability and accurate placement of model projections in AROS. Projection stability was improved by reducing the number of triangle meshes of the model to relieve the need for processing power. Investigations revealed that simplification rates of 40% and 20% were optimal for rectilinear and curvilinear components, respectively. Projection placement was improved by implementing a hybrid of target anchoring methods. Specifically, ARWorldMap was used to add additional anchor points, which were identified from the topologies of the structure and surrounding planes. Post-evaluations demonstrated increased stability and reductions in displacement errors. The formalizations provide measures for using AR and BIM models when applying these technologies to large-scale civil infrastructure projects. Full article

(This article belongs to the Special Issue Advances in BIM-Based Architectural Design and System)

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11 pages, 1437 KiB

Open AccessArticle

A Stacking Heterogeneous Ensemble Learning Method for the Prediction of Building Construction Project Costs

by Uyeol Park, Yunho Kang, Haneul Lee and Seokheon Yun

Appl. Sci. 2022, 12(19), 9729; https://0-doi-org.brum.beds.ac.uk/10.3390/app12199729 - 27 Sep 2022

Cited by 9 | Viewed by 2273

Abstract

The accurate cost estimation of a construction project in the early stage plays a very important role in successfully completing the project. In the initial stage of construction, when the information necessary to predict construction cost is insufficient, a machine learning model using [...] Read more.

The accurate cost estimation of a construction project in the early stage plays a very important role in successfully completing the project. In the initial stage of construction, when the information necessary to predict construction cost is insufficient, a machine learning model using past data can be an alternative. We suggest a two-level stacking heterogeneous ensemble algorithm combining RF, SVM and CatBoosting. In the step of training the base learner, the optimal hyperparameter values of the base learners were determined using Bayesian optimization with cross-validation. Cost information data disclosed by the Public Procurement Service in South Korea are used to evaluate ML algorithms and the proposed stacking-based ensemble model. According to the analysis results, the two-level stacking ensemble model showed better performance than the individual ensemble models. Full article

(This article belongs to the Special Issue Advances in BIM-Based Architectural Design and System)

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17 pages, 3853 KiB

Open AccessArticle

Performance Analysis of Construction Cost Prediction Using Neural Network for Multioutput Regression

by Seokheon Yun

Appl. Sci. 2022, 12(19), 9592; https://0-doi-org.brum.beds.ac.uk/10.3390/app12199592 - 24 Sep 2022

Cited by 5 | Viewed by 2742

Abstract

In a construction project, construction cost estimation is very important, but construction costs are affected by various factors, so they are difficult to predict accurately. However, with the recent development of ANN technology, it has become possible to predict construction costs with consideration [...] Read more.

In a construction project, construction cost estimation is very important, but construction costs are affected by various factors, so they are difficult to predict accurately. However, with the recent development of ANN technology, it has become possible to predict construction costs with consideration of various influencing factors. Unlike previous research cases, this study aimed to predict the total construction cost by predicting seven sub-construction costs using a multioutput regression model, not by predicting a single total construction cost. In addition, analysis of the change in construction cost prediction performance was conducted by scaling and regularization. We estimated the error rate of predicting construction costs through sub-construction cost prediction to be 16.80%, a level similar to that of the total construction cost prediction error rate of 17.67%. This study shows that the construction cost can be calculated by predicting detailed cost factors at once, and it is expected that various types of construction costs or partial construction costs can be predicted using the predicted detailed cost elements. As a result of predicting several sub-construction costs using multioutput-based ANN, it was found that the prediction error rate varies depending on the type of construction. To improve accuracy, it is necessary to supplement influencing factors suitable for the construction features. Full article

(This article belongs to the Special Issue Advances in BIM-Based Architectural Design and System)

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22 pages, 4232 KiB

Open AccessArticle

Parametric Method and Building Information Modeling-Based Cost Estimation Model for Construction Cost Prediction in Architectural Planning

by Seung-Won Yang, Seong-Wan Moon, Hangyeol Jang, Seungyeon Choo and Sung-Ah Kim

Appl. Sci. 2022, 12(19), 9553; https://0-doi-org.brum.beds.ac.uk/10.3390/app12199553 - 23 Sep 2022

Cited by 5 | Viewed by 3616

Abstract

Economic feasibility and cost analysis in the preliminary planning stage of early-phase construction projects have a significant impact on project management and implementation. However, while estimating the construction cost per unit area, the existing approaches do not account for factors other than the [...] Read more.

Economic feasibility and cost analysis in the preliminary planning stage of early-phase construction projects have a significant impact on project management and implementation. However, while estimating the construction cost per unit area, the existing approaches do not account for factors other than the area-related information, causing estimation error. Therefore, a construction cost estimation model that can be utilized in the early phase of a construction project is developed in this study based on BIM in the architectural planning stage. Moreover, goodness of fit and accuracy of the model were verified through a validation method considering the BIM design process. The proposed model showed higher accuracy than the conventional models in terms of the floor area. Furthermore, it was possible to confirm the model performance based on the cost estimation accuracy range presented by the AACE. In addition, the developed model can generate estimation results corresponding to Class 1–3, a subsequent construction project stage. The findings of this study emphasize the importance of jointly considering the processes related to construction cost estimation and indicate that parameters other than the floor area need to be considered for construction cost estimation in the early phase of a construction project. Full article

(This article belongs to the Special Issue Advances in BIM-Based Architectural Design and System)

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17 pages, 6660 KiB

Open AccessArticle

Method for Constructing a Façade Dataset through Deep Learning-Based Automatic Image Labeling

by Hyeongmo Gu and Seungyeon Choo

Appl. Sci. 2022, 12(15), 7570; https://0-doi-org.brum.beds.ac.uk/10.3390/app12157570 - 27 Jul 2022

Cited by 2 | Viewed by 1773

Abstract

The construction industry has made great strides in recent decades by utilizing computer programs, including computer aided design programs. However, compared to the manufacturing sector, labor productivity is low because of the high proportion of knowledge-based tasks and simple repetitive tasks. Therefore, knowledge-based [...] Read more.

The construction industry has made great strides in recent decades by utilizing computer programs, including computer aided design programs. However, compared to the manufacturing sector, labor productivity is low because of the high proportion of knowledge-based tasks and simple repetitive tasks. Therefore, knowledge-based task efficiency should be improved through the visual recognition of information by computers. A computer requires a large amount of training data, such as the ImageNet project, to recognize visual information. This paper proposes façade datasets that are efficiently constructed by quickly collecting façade data through road-view images generated from web portals and automatically labeled using deep learning as part of the construction of image datasets for visual recognition construction by a computer. Therefore, we attempted to automatically label façade images to quickly generate large-scale façade datasets with much less effort than the existing research methods. Simultaneously, we constructed datasets for a part of Dongseong-ro, Daegu Metropolitan City, and analyzed their utility and reliability. It was confirmed that the computer could extract significant façade information from the road-view images by recognizing the visual information of the façade image. In addition, we verified the characteristics of the building construction image datasets. This study suggests the possibility of securing quantitative and qualitative façade design knowledge by extracting façade design information from façades anywhere in the world. Previous studies mainly collected façade images through camera photography to construct databases, but in this study, a significant part of the database construction process was shortened through automation. In the case of façade automatic image labeling studies, it is the façade-based automatic 3D modeling which has been primarily studied, but it is difficult to find a study to extract data for façade design research. Full article

(This article belongs to the Special Issue Advances in BIM-Based Architectural Design and System)

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28 pages, 17485 KiB

Open AccessArticle

Building Information Modeling-Embedded Building Energy Efficiency Protocol for a Sustainable Built Environment and Society

by Chen Wang, Benben Cui, Meng Wu, Yutong Tang, Jeffrey Boon Hui Yap, Huibo Zhang and Heng Li

Appl. Sci. 2022, 12(12), 6051; https://0-doi-org.brum.beds.ac.uk/10.3390/app12126051 - 14 Jun 2022

Cited by 4 | Viewed by 2247

Abstract

In order to accurately analyze the building energy consumption and identify the problem of building energy consumption in advance, this study carries out the energy consumption analysis based on BIM (Building Information Modeling). The research object is a four-story college student dormitory in [...] Read more.

In order to accurately analyze the building energy consumption and identify the problem of building energy consumption in advance, this study carries out the energy consumption analysis based on BIM (Building Information Modeling). The research object is a four-story college student dormitory in Beijing, and this set of BIM-based energy consumption simulation data was obtained using standard operating procedures (SOP). This operating procedure can start energy consumption analysis in the conceptual design stage, and developers can participate in real-time through the use of a three-dimensional information model, without additional design required. Then, comparing this study with the traditional energy consumption analysis, we see that the SOP of this research result has the following advantages: SOP function analysis is more professional, and the visual display method is more popular and intuitive; due to the flexible file format of the SOP, when data exchange is required between different software, the SOP can realize more convenient operation, and users can identify problems in the early stage of design through the SOP, correcting the scheme according to the simulation results, which is conducive to the development of the construction process. Finally, this study puts forward the analysis and estimation of energy consumption in different stages of the building life cycle, so as to provide researchers with ideas for improvement. Full article

(This article belongs to the Special Issue Advances in BIM-Based Architectural Design and System)

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