Bridge Information Modeling and Life Cycle Assessment: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Farnaz Jalaei

Construction 4.0 is a platform that combines digital and physical technologies to enhance the design and construction of the built environment. Bridge Information Modeling (BrIM), a component of Construction 4.0′s digital technologies, streamlines construction processes and promotes collaboration among project stakeholders. A comprehensive literature review and bibliometric and content analysis are conducted on building information modeling (BIM), life cycle assessment (LCA), life cycle cost (LCC), BrIM, and Bridge LCA. It investigates the potential integration of BrIM, LCA, and LCC as inputs for bridges’ LCA to enhance decision making by providing designers with detailed and interactive cost and environmental information throughout an asset’s lifecycle and explores the functionalities of Construction 4.0 and its potential influence on the economy and sustainability of bridge projects. The reviewed literature showed that the tools currently used to apply LCA and LCC methods for infrastructure assets lack the ability to identify possible integration with BrIM and hold limitations in their key functions for identifying the utmost features that need to be adopted in the creation of any tool to increase the general resilience of bridges and infrastructure.

  • construction 4.0
  • bridge information modeling (BrIM)
  • life cycle assessment (LCA)
  • life cycle cost (LCC)
  • building information modeling (BIM)

1. Building Information Modelling (BIM) and Life Cycle Assessment (LCA)

This section provides a systematic review of the interaction between BIM and LCA tools in a BIM-LCA integration environment. Furthermore, it explores the status of integrating BIM and LCA to detect the unaddressed issues and evaluate how this integration can be applied to BrIM cases that have identical issues. The framework of ISO 14040 [1], which provides a standard procedure for implementing LCA, incorporates four essential steps that must be considered when conducting LCA for any project: (1) goal and scope definition; (2) life cycle inventory analysis (LCI); (3) life cycle impact assessment (LCIA); and (4) life cycle interpretation (ISO, 14044 [2]). Succar [3] considered BIM to be a method that assists the construction industry in achieving its sustainability goals. Many scholars have highlighted the effect of improving and simplifying the process of applying LCA methods to buildings; their findings were directed toward the integration of BIM and LCA as efficient ways of optimizing the performance of LCA. Several studies acknowledged and summarized the importance of BIM tools in helping decision-makers in applying LCA for buildings and choose building materials, construction methods, and building service systems [4][5][6][7].
Several studies concerning the integration of BIM and LCA have been published within the last four years; therefore, it is obvious that this area of research has received considerable attention from scholars. Content analysis, as a research tool, helps in extracting textual information from published studies about the combination of BIM with LCA (see Supplementary Information at https://www.mdpi.com/article/10.3390/su152015049/s1). For instance, Soust-Verdaguer et al. [8] analyzed studies that involve BIM and LCA with a focus on how BIM tools can facilitate the input of data and optimize the data output of LCA tools. The result of that review introduced possible methods for integrating BIM and LCA tools via developing templates and plugins into BIM tools. Nevertheless, this study was published before 2018; therefore, many new studies were not considered.

1.1. BIM Model’s Input

Defining the physical model is crucial throughout the application of LCA. However, few studies used the level of detail (LOD) of the developed models for building projects, the majority of studies utilized the third level of detail (3rd LOD) to achieve acceptable quantities, shape, size, location, and orientation for energy and emission analysis [9][10]. Soust-Verdaguer et al. [8] believed that the reason behind using the 3rd LOD is that, at this level, most of the relevant materials and components are well defined and therefore appropriate for assessing the environmental impacts of proposed buildings.

1.2. LCA Model’s Input

Numerous studies have chosen an entire asset as the functional unit to apply the LCA method, while few articles considered partial sections of buildings for this purpose [11][12][13]. Most of the reviewed studies developed life cycle inventories (LCIs) by creating different databases. The most common types of databases used in these previous studies included the Inventory of Carbon and Energy, ICE, [13][14]; the GaBi database [15]; and the Ecoinvent database [9][10]. These databases were widely employed in carbon modeling and energy simulation software, such as EnergyPlus 9.2 [16] and DesignBuilder [17], while other studies used national databases, like the Swiss Buildings Database [18], the KBOB database [19][20], and the CLCD Chinese database [21][22].

1.3. Software Integration and Data Exchange

Integrating BIM with LCA is an efficient way of obtaining necessary information about the construction materials used in BIM models and transferring it to an existing LCA database for LCI outcomes. Rezaei et al. [10] used Autodesk Revit©, Ecoinvent database, and openLCA to perform a comprehensive carbon assessment of a four-story multi-residential building in Quebec, Canada. Based on professional and subjective judgments, the Canadian building norms and standards, and the quality of the materials involved, the Ecoinvent database was initially compared to a list of materials generated in Autodesk Revit© as a BIM tool before it was transferred and processed using the openLCA system. It is obvious that there is a considerable difference between the formats of the data recognized by both the LCA and BIM tools. Therefore, to map the data extracted from a BIM model and import them into the LCA tool, a consistent data format and similar naming conventions for both kinds of data should be established when a link between BIM and LCA tools is to be set [23]. During a data transition, information from BIM models is transferred into LCA tools to determine the LCI results; hence, several studies, such as the ones presented in [24][25], identified six different methods that can be used to bilaterally transfer information and data between BIM models and LCA tools. These methods are listed as follows:
(i)
Export the bill of quantities (BOQ) into Excel: it is well known that Microsoft Excel is a third-party tool that is commonly employed in many studies; thus, as part of the integration process, the quantities of materials extracted from BIM tools, like Autodesk Revit© [26] and ArchiCAD [27], are multiplied by the related emission factors provided by LCA tools, such as the Ecoinvent [9][28] and ICE [13][29] databases.
(ii)
Export BOQ into dedicated LCA tools: Abbasi and Noorzai [12] presented a BIM-based multi-optimization framework for determining the trade-off between embodied and operational energy. Furthermore, they provided an approach to choosing optimal solutions during the early stage of design. The geometric data and materials’ bill of quantity were extracted from Revit, a BIM tool, and subsequently inputted into Athena Impact Estimator, a LCA tool, to calculate the embodied energy of an eight-story residential building.
(iii)
The use of LCA plugins in BIM tools: This method has been used to a great extent since Tally, one of the LCA tools, was realized due to its user-friendly interface. Tushar et al. [11] integrated a BIM tool (Autodesk Revit©) with an energy rating tool (FirstRate5) and a Tally plugin into a BIM tool to quantify and compare different design options for a residential building. They compared various design scenarios with different options, including insulation and accessory materials. The results of their study showed that plywood walls had a lower impact on the environment compared to the other types of walls. Furthermore, some studies used sensitivity analysis to determine the factors that have a greater impact on the overall performance.
(iv)
The use of visual programming languages (VPL) to evaluate environmental impacts: to assess the emissions of building elements, Marzouk et al. [30] developed an interface in which building data extracted from a BIM model could be transferred to Microsoft Access through a DB link in Autodesk Revit©, a BIM tool, whereas the emission factors could be retrieved from Athena Impact Estimator, as LCA tool.
(v)
The use of industry foundation classes (IFC) to transfer the data exported from BIM models to an LCA tool: It is commonly known that the IFC scheme for storing LCA-related information within BIM environments is a workable method for coping with a large volume of data. However, the most recent version of IFC, IFC4, is the only version that makes use of IFC features for simple LCA but not a full LCA [31]. Therefore, more IFC properties should be created if a comprehensive LCA needs to be performed.
(vi)
The incorporation of LCA data into BIM objects: In this method, each building’s material data stored in the native library of the BIM tool, Autodesk Revit©, are connected to an emission factor obtained from the KBOB database using a unique ID similar to the case reported in the study conducted by Hollberg et al. [19]. The quantities of the materials are obtained using Dynamo through unique KBOB IDs. However, Dynamo is only able to retrieve volumetric data for technical routing components like pipes or ducts. However, several studies considered the creation of new and unique APIs in BIM tools (i.e., Autodesk Revit) for this purpose.
Impact categories are generally grouped into four main areas: (1) the use of natural resources (resource depletion); (2) the effect on human health (human health and safety effects); (3) the effect on the ecosystem (ecological effects); and (4) greenhouse effects (climate change). Each of these effects interacts with the environment at different geographical scales, which can be considered for additional classification of the impact categories, such as global (the depletion of the ozone layer and greenhouse effects); regional (acidification and eutrophication); and local (land use and the formation of photochemical smog).

2. Bridge LCA Stages and Frameworks

The life cycle assessment (LCA) approach for a bridge examines all its related activities and processes over its expected life to quantify its energy consumption and potential impacts on the environment, resources, and public health. The reviewed literature in this area revealed that those activities and processes are characterized by diverse scientific scopes, functional units, and system boundaries. However, the typical scopes of these studies encompassed the entire life cycle of bridges, including the material-manufacturing, construction, maintenance and operation, and end-of-life stages [32][33][34][35][36][37]. Moreover, the research analyzed shows that certain studies did not consider all the scopes pertaining to bridge life cycle assessment, such as the end-of-life stage [38][39]; the maintenance stage [40]; the operation phase [41]; and the use phase [42][43]. Furthermore, the focus of other studies was on a particular scope and specific life cycle boundaries, such as maintenance activities [44] and a material’s end-of-life stage [45]. For each of these LCA phases, the specifications of the utilized materials and equipment were given alongside life cycle inventory (LCI) data, which generally consist of background data on the upstream processing models and the related environmental emissions [46]. In any LCA study, the functional unit (FU), which is an essential element, must be clearly defined. The evaluation of case studies related to Bridges’ LCA shows the use of a wide range of several functional units, although the two, most commonly single, units used are (i) 1 linear meter of length of a bridge deck and (ii) 1 m2 of effective bridge surface area, especially when performing comparative LCA studies. However, Guest et al. [32] highlighted that the selection of an appropriate functional unit for infrastructure systems like a bridge is still a challenge. They therefore proposed an LCA result based on several FUs, including (i) FUs that consider only area or distance (i.e., 1 m2 year; 1 lane m year); (ii) FUs that consider both distance and utilization (i.e., 1 ESAL-m; 1 person m, where ESAL means Equivalent Single Axle Load).

2.1. Material Manufacturing Stage

During this stage, raw materials are extracted and processed into a final, usable product for the construction and upkeep of a bridge. The most common manmade materials utilized in bridge structures were found to be concrete and steel [32][34][42][45][46][47][48]. The other reviewed auxiliary manufactured bridge materials include asphalt and membrane, epoxy paint, wood, etc. Most of the assessed studies opted to use the Ecoinvent database as their life cycle inventory (LCI) to examine how much energy is consumed during the material manufacturing processes and their impacts on the environment. The three main reasons why most of those studies used this database are as follows: (1) it is frequently updated; (2) it has environmental profile units; and (3) its effectiveness has been verified through research. Alternatively, others utilized LCI data at this phase, which include Gabi [37] and Environmental Product Declarations (EPD) [49]. The LCA results from various studies show that the material manufacturing phase causes the most significant environmental problems in the entire life cycle of bridges.

2.2. Construction Stage

During the construction phase of a bridge, the energy consumed and emissions generated from using construction equipment, transporting construction materials, and associated types of work; traveling distance; and the bridge’s construction method have a great influence on the environmental effects. In their study, Bizjak and Lenart [41] found that the Ecoinvent database lacks information related to specialized equipment. However, Du et al. [43] evaluated diesel and electricity consumption throughout a building’s construction phase using Ecoinvent inventory data. Alternatively, other scholars obtained LCA data for the consumption of diesel-operated machines from different sources, such as manufacturer’s and contractor’s data [43][50]; databases [34]; the literature [41][42]; and a combination of sources [48].

2.3. Operation and Maintenance Stage

The different tasks and activities considered during the design and planning stage of a bridge are included in the operation and maintenance stage. Therefore, Guest et al. [32] utilized data retrieved from a traffic survey conducted by the Ontario Ministry of Transportation in 2015 to forecast fleet composition based on the daily traffic average over the long term. Annual average daily traffic provides an average hourly breakdown of vehicles of a particular range in length to determine the fuel consumed by every vehicle traveled over a bridge per hour over its service life during the operation phase. Previous studies categorized the activities and processes engaged in during the operating and maintenance phase into the following three main categories: (1) maintenance activities; (2) traffic detours; and (3) CO2 fixed [35][48]. For instance, Penadés-Plà et al. [34] believed that a bridge requires a period of two days for the replacement of concrete mortar cover during the maintenance stage to meet the codes over its 120 years of service life. A review of published studies related to bridge LCA revealed that the type and design of bridges under investigation need specific procedures and types of materials during the maintenance stage. Noticeably, the frequency of maintenance activities, the materials used, and traffic delays have a large impact on every scope of the maintenance process [41]. The main sources of harm to the environment were identified to be the emissions precipitated by the distance of traffic detours and the running distance to avoid traffic detours during the maintenance stage [37][48]. Penadés-Plà et al. [35] added the systems that produce a fixed amount of CO2 and that are used during the maintenance stage to these sources.

2.4. End-of-Life and Demolition Stage

The end-of-life stage involves all the activities related to tearing down a bridge after it reaches the end of its service life. As a result, assessing the environmental impact of this phase must consider the fleet of equipment used throughout the bridge’s destruction, the transportation type and the running distance, and the treatment of the generated debris when reuse, recycle, or landfill disposal are utilized as techniques for managing the waste. In the literature, since the methods used were like the ones used during the construction phase, there were neither detailed discussions about the technology and transportation needed in this phase nor details about the environmental impacts. Alternatively, the emphasis was on how to handle the waste generated during the demolition process. Moreover, the literature lacks detailed guidelines and regulations for directing the effective methods to be considered for handling the wastes generated from demolishing bridges because managing these wastes necessitates various acts based on the processes and goals of the treatment [34]. Full recycling at the end-of-life stage was cited as a useful environmental method because it reduces the consumption of original materials and lowers the associated emissions [41]. Hammervold et al. [50] claimed that the requirements for managing the waste produced by construction projects can be met by simply recycling or reusing steel and concrete. At the end of a project’s useful life, excess concrete may be crushed and used as aggregate for constructing roads and bridges. Several studies stated that the carbonation of concrete, which is a higher fixer of CO2, can be achieved by placing crushed concrete in a landfill [25][35][43][46][48]. There was no specific published study in the literature that provided a fixed ratio for recycling steel reinforcement; however, various assumptions were made: for example, Hammervold et al. [50] believed that 100% of the steel they examined could be recycled or reused, whereas Pang et al. [42] reported that only 85% could be recycled. However, Penadés-Plà et al. [34] considered this value to be 71%, while [51] reported a value of 72%. Nevertheless, Penadés-Plà et al. [35] believed that the steel recycling ratio differs according to the location. Du et al. [46] declared that Ecoinvent considers both the energy and the raw material savings from recycling steel rebar during the early stage of a material’s production. In regard to this point, it is apparent that the results of LCA lead one to the conclusion that during the whole life cycle of a bridge project, its end-of-life phase imposes the least environmental burden.

3. Bridge Information Modeling (BrIM)

BIM is increasingly being adopted as a valuable concept in the construction industry. Its tools can be employed to create shop drawings, detect clashes, estimate quantities, and manage documentation. Bridge Information Modeling (BrIM), on the other hand, is a concept like BIM, but it is specifically applied to bridges. The BrIM tool provides an efficient presentation of bridges that incorporates all the required information during their life cycle [52]. Applying BrIM concepts ensures consistency in acquiring necessary information at the different stages of a bridge’s life, starting from design and moving on to maintenance, and this is essential for a bridge’s stakeholders because it improves their three key areas of concern (quality, schedule, and cost), speeding up construction while lowering costs [53]. 4D BrIM benefits projects’ participants when used during the construction phase for planning materials’ delivery, controlling and monitoring a project’s progress, and improving construction coordination, schedules, and documentation [54].
At the planning and design stage of bridges, BrIM technology has mostly been used for creating and utilizing 3D bridge models to guide design decisions. To improve the accuracy of modeling designs in 3D, several scholars used parametric modeling for bridges and proposed design guidelines with associated information about 3D modeling to reduce collisions and other modeling issues. Shim et al. [55] modeled each component of a bridge, such as beams, piers, and abutments, by using fundamental parameters like geometric dimensions, which are connected to other elements by a layered architecture of geometry models that are not used by special-shaped components. Lee et al. [56] performed parametric modeling for a bridge’s reinforcing bars, while [53] claimed that 3D Bridge models that use digital copies of the Work Breakdown Structure (WBS) and Product Breakdown Structure (PBS) have design improvements and accelerate the learning of construction engineers. Markiz and Jrade [57] developed an integrated 3D model within a BrIM environment, a fuzzy-logic decision support system, and a cost-estimating module to assist in the conceptual design of concrete box-girder bridges. They performed a parametric analysis to measure the system’s level of accuracy by exporting BrIM input databases in the IFC file format to minimize the loss of information during the transition process. At the conceptual design stage of a bridge, their developed BrIM system incorporates several bridge maintenance and repair (MR) and replacement (R) solutions to monitor the deterioration of bridges using a multi-criteria decision-making approach (MCDM) to obtain competitive priority ratings. Designing bridges with a beautiful aesthetic can also be carried out using three-dimensional information modeling. Tanner et al. [58] indicated that an elegant solution that satisfies the highest aesthetic requirements could coexist with a contemporary and technologically advanced design. In structural engineering, the environmental, human, and built contexts of civil works are typically not considered during the design stage. Several scholars utilized 3D information models to assess the procedures used to put together a bridge and, afterward, to make suggestions for improvement. Thus, to minimize errors during erection due to some unanticipated types of damage/deterioration in the operation stage, engineers can access and update the data related to the bridge life cycle analysis by using the master digital model throughout a bridge’s life cycle [59]. The terminologies and standards of unified modeling are lacking throughout the design process. Employing visualization modeling at the early design stage would yield geometry data for structural analysis [60].
For the construction stage, Lee et al. [56] used a 3D BrIM model to reduce the construction duration by around 4.5 months; the productivity of the site’s operation was increased, and it was possible to decrease the workforce by around 6%. Utilizing BrIM could aid in scheduling complex projects, resulting in cost savings between the range of 5% to 9% by minimizing the need for change orders and rework. The initial deployment of 3D modeling would cost about 70% of the total cost; this front-end loading of 3D modeling costs might prevent the wide spreading of its use [61]. Vilventhan and Rajadurai [54] used 4D BrIM to settle piles at various locations within a site that had limited space during the construction stage. Kaewunruen et al. [62] presented a novel 6D BrIM method for the asset management of a bridge structure by integrating 3D model data with cost projection, time schedule, and carbon footprint analysis throughout the duration of the bridge life cycle.
To select the best locations for mobile cranes on the construction sites for a bridge, Marzouk and Hisham [63] proposed a hybrid model combining Genetic Algorithms (GAs) and BrIM that considers various limitations concerning the safety, clearance, existing site conditions, construction schedule, and duration of erecting structural members. Importing the crane model and modeling the erection process might aid in choosing the ideal position for a crane when utilizing a 3D model. Additional studies are required to confirm the applicability of hybrid models. Most of the current bridges were constructed during the 20th century, and accessing their 2D as-built drawings is difficult due to sparse information. It would be challenging and time consuming to create precise BrIM models for numerous bridges by using the information at hand. Furthermore, there are no standardized criteria for BrIM during the operation and maintenance stage, in contrast to the construction of a bridge, which entails specific rules about the levels of complexity (i.e., as-built BrIM can be used during the operation and maintenance stage). To mitigate these problems, a new framework was developed by Xu and Turkan [64] that used camera-based unmanned aerial systems (UASs) to gather and analyze inspection data and a BrIM tool to manage and store all associated inspection data. The findings of their study supported the idea of employing computer vision algorithms wherein high-resolution photographs taken from UASs can be used to visually detect cracks and identify other types of faults. Furthermore, the outcomes supported the use of BrIM for assigning defect information to specific model elements, which allows for the management of all bridge data in a single model throughout a bridge’s life cycle and provides a feature for decreasing site visits by negating the need for data re-entry through the aid of cloud computing. Almomani and Almutairi [65] proposed a balanced approach to making decisions related to the management of bridge maintenance under various limitations, such as cost optimization and expert advice [65].
Industry Foundation Classes (IFC) is a model file format used to export and import a BrIM model and its associated information to other tools used in the design, construction, and maintenance of bridges. Park et al. [66] discovered that a semantic-based query addressed to the created IFC-based bridge information model made it feasible to retrieve and extract information for the associated components. Wan et al. [67] proposed the development of a bridge management system (BMS) based on BIM technology and the extension of Industry Foundation Classes (IFC) and International Framework for Dictionaries (IFD) standards. Coding rules were presented for the Chinese bridge industry, and a standard structural modeling approach was presented to quickly build a bridge model in a BIM environment. The proposed system is a web-based BIM and includes a practical BIM-based BMS for a long-span cable-stayed bridge in China. The study demonstrated the potential for IFC-based BIM technology to improve bridge management systems and maintenance efficiency [67]. Dang et al. [59] addressed the information discontinuity between the various stages of bridge projects as well as the existing gap in collaboration between various stakeholders. More data about the performance of digital models must be added. Zhang et al. [68] proposed a new 4D-based model for the life-cycle integration, modeling, and visualization of infrastructure data. The developed method considers using 4D technology over the construction stage and shows its efficiency for the life-cyclic visualization and modeling of infrastructure data, including with respect to condition evaluations. The technique enhanced the accuracy of maintenance activities by 20–40% and decreased their duration by 30–50%. The lack of standardization in the format and storage of inspection reports makes it difficult for practitioners and researchers to use inspection information for knowledge generation purposes. To address this issue, Hüthwohl et al. [69] proposed an information model and a candidate bound to IFC to classify inspection information for R.C. bridges and standardize its storage in a format suitable for sharing and comparing between different users and requirements. They demonstrated that IFC, in their latest version, could provide adequate functionality for integrating relevant defect information and imagery and presenting a prototypical application as a proof of concept for automatic sharing and comparing of information needed in R.C. bridge inspections [69].

This entry is adapted from the peer-reviewed paper 10.3390/su152015049

References

  1. ISO 14040:2006; Environmental Management–Life Cycle Assessment—Principles and Framework. ISO: Geneva, Switzerland, 2006. Available online: https://www.iso.org/standard/37456.html (accessed on 31 July 2023).
  2. ISO 14044:2006; The International Standards Organisation, ISO 14044: Environmental Management-Life Cycle Assessment-Requirements and Guidelines. ISO: Geneva, Switzerland, 2006. Available online: https://www.iso.org/standard/38498.html (accessed on 31 July 2023).
  3. Succar, B. Building Information Modelling Framework: A Research and Delivery Foundation for Industry Stakeholders. Autom. Constr. 2009, 18, 357–375.
  4. Basbagill, J.; Flager, F.; Lepech, M.; Fischer, M. Application of Life-Cycle Assessment to Early Stage Building Design for Reduced Embodied Environmental Impacts. Build. Environ. 2013, 60, 81–92.
  5. Eleftheriadis, S.; Duffour, P.; Mumovic, D. BIM-Embedded Life Cycle Carbon Assessment of RC Buildings Using Optimised Structural Design Alternatives. Energy Build. 2018, 173, 587–600.
  6. Mostafazadeh, F.; Eirdmousa, S.J.; Tavakolan, M. Energy, Economic and Comfort Optimization of Building Retrofits Considering Climate Change: A Simulation-Based NSGA-III Approach. Energy Build. 2023, 280, 112721.
  7. Tavakolan, M.; Mostafazadeh, F.; Jalilzadeh Eirdmousa, S.; Safari, A.; Mirzaei, K. A Parallel Computing Simulation-Based Multi-Objective Optimization Framework for Economic Analysis of Building Energy Retrofit: A Case Study in Iran. J. Build. Eng. 2022, 45, 103485.
  8. Soust-Verdaguer, B.; Llatas, C.; Garcia-Martinez, A. Critical Review of Bim-Based LCA Method to Buildings. Energy Build. 2017, 136, 110–120.
  9. Soust-Verdaguer, B.; Llatas, C.; Moya, L. Comparative BIM-Based Life Cycle Assessment of Uruguayan Timber and Concrete-Masonry Single-Family Houses in Design Stage. J. Clean. Prod. 2020, 277, 121958.
  10. Rezaei, F.; Bulle, C.; Lesage, P. Integrating Building Information Modeling and Life Cycle Assessment in the Early and Detailed Building Design Stages. Build. Environ. 2019, 153, 158–167.
  11. Tushar, Q.; Bhuiyan, M.; Zhang, G.; Maqsood, T. An Integrated Approach of BIM-Enabled LCA and Energy Simulation: The Optimized Solution towards Sustainable Development. J. Clean. Prod. 2021, 289, 125622.
  12. Abbasi, S.; Noorzai, E. The BIM-Based Multi-Optimization Approach in Order to Determine the Trade-off between Embodied and Operation Energy Focused on Renewable Energy Use. J. Clean. Prod. 2021, 281, 125359.
  13. Ansah, M.; Chen, X.; Yang, H.; Lu, L.; Lam, P. An Integrated Life Cycle Assessment of Different Facade Systems for a Typical Residential Building in Ghana. Sustain. Cities Soc. 2020, 53, 101974.
  14. Abanda, F.; Oti, A.; Tah, J. Integrating BIM and New Rules of Measurement for Embodied Energy and CO2 Assessment. J. Build. Eng. 2017, 12, 288–305.
  15. Carvalho, J.; Alecrim, I.; Braganca, L.; Mateus, R. Integrating BIM-Based LCA and Building Sustainability Assessment. Sustainability 2020, 12, 7468.
  16. Jalaei, F.; Guest, G.; Gaur, A.; Zhang, J. Exploring the Effects That a Non-Stationary Climate and Dynamic Electricity Grid Mix Has on Whole Building Life Cycle Assessment: A Multi-City Comparison. Sustain. Cities Soc. 2020, 61, 102294.
  17. Cheng, B.; Li, J.; Tam, V.; Yang, M.; Chen, D. A BIM-LCA Approach for Estimating the Greenhouse Gas Emissions of Large-Scale Public Buildings: A Case Study. Sustainability 2020, 12, 685.
  18. Cavalliere, C.; Habert, G.; Dell’Osso, G.; Hollberg, A. Continuous BIM-Based Assessment of Embodied Environmental Impacts throughout the Design Process. J. Clean. Prod. 2019, 211, 941–952.
  19. Hollberg, A.; Genova, G.; Habert, G. Evaluation of BIM-Based LCA Results for Building Design. Autom. Constr. 2020, 109, 102972.
  20. Kiamili, C.; Hollberg, A.; Habert, G. Detailed Assessment of Embodied Carbon of HVAC Systems for a New Office Building Based on BIM. Sustainability 2020, 12, 3372.
  21. Yang, X.; Hu, M.; Wu, J.; Zhao, B. Building-Information-Modeling Enabled Life Cycle Assessment, a Case Study on Carbon Footprint Accounting for a Residential Building in China. J. Clean. Prod. 2018, 183, 729–743.
  22. Su, S.; Wang, Q.; Han, L.; Hong, J.; Liu, Z. BIM-DLCA: An Integrated Dynamic Environmental Impact Assessment Model for Buildings. Build. Environ. 2020, 183, 107218.
  23. Obrecht, T.; Rock, M.; Hoxha, E.; Passer, A. BIM and LCA Integration: A Systematic Literature Review. Sustainability 2020, 12, 5534.
  24. Teng, Y.; Xu, J.; Pan, W.; Zhang, Y. A Systematic Review of the Integration of Building Information Modeling into Life Cycle Assessment. Build. Environ. 2022, 221, 109260.
  25. Tam, V.; Zhou, Y.; Illankoon, C.; Le, K. A Critical Review on BIM and LCA Integration Using the ISO 14040 Framework. Build. Environ. 2022, 213, 108865.
  26. Santos, R.; Costa, A.A.; Silvestre, J.D.; Pyl, L. A Validation Study of a Semi-Automatic BIM-LCA Tool: 2o Congresso Português de Building Information Modelling. In 2° Congresso Português de Building Information Modelling; Instituto Superior Técnico Universidade de Lisboa: Lisbon, Portugal, 2018.
  27. Shin, Y.; Cho, K. BIM Application to Select Appropriate Design Alternative with Consideration of LCA and LCC. Math. Probl. Eng. 2015, 2015, 281640.
  28. Georges, L.; Haase, M.; Houlihan Wiberg, A.; Kristjansdottir, T.; Risholt, B. Life Cycle Emissions Analysis of Two NZEB Concepts. Build. Res. Inf. 2015, 43, 82–93.
  29. Iddon, C.; Firth, S. Embodied and Operational Energy for New-Build Housing: A Case Study of Construction Methods in the UK. Energy Build. 2013, 67, 479–488.
  30. Marzouk, M.; Abdelkader, E.M.; Al-Gahtani, K. Building Information Modeling-Based Model for Calculating Direct and Indirect Emissions in Construction Projects. J. Clean. Prod. 2017, 152, 351–363.
  31. Santos, R.; Costa, A.; Silvestre, J.; Pyl, L. Integration of LCA and LCC Analysis within a BIM-Based Environment. Autom. Constr. 2019, 103, 127–149.
  32. Guest, G.; Zhang, J.; Kadhom, B.; Singh, J. Incorporating the Impacts of Climate Change into a Life Cycle Assessment of a Slab-on-Girder Highway Bridge. In Pavement, Roadway, and Bridge Life Cycle Assessment 2020; Harvey, J., Al-Qadi, I.L., Ozer, H., Flintsch, G., Eds.; Taylor & Francis Group: London, UK, 2020; pp. 40–50. ISBN 9780367551667.
  33. Ma, M.; Li, Z.; Xue, K.; Liu, M. Exergy-Based Life Cycle Assessment Model for Evaluating the Environmental Impact of Bridge: Principle and Case Study. Sustainability 2021, 13, 11804.
  34. Penadés-Plà, V.; García-Segura, T.; Martí, J.V.; Yepes, V. An Optimization-LCA of a Prestressed Concrete Precast Bridge. Sustainability 2018, 10, 685.
  35. Penadés-Plà, V.; Martí, J.V.; García-Segura, T.; Yepes, V. Life-Cycle Assessment: A Comparison between Two Optimal Post-Tensioned Concrete Box-Girder Road Bridges. Sustainability 2017, 9, 1864.
  36. Peñaloza, D.; Erlandsson, M.; Pousette, A. Climate Impacts from Road Bridges: Effects of Introducing Concrete Carbonation and Biogenic Carbon Storage in Wood. Struct. Infrastruct. Eng. 2018, 14, 56–67.
  37. Shi, C.; Wang, Y.; Chen, B.; Liu, Y.; Li, K.; Luo, W. Life-Cycle Environmental and Economic Benefits of Jointless Bridges Considering Climate Change. Struct. Infrastruct. Eng. 2023, 19, 745–759.
  38. Kripka, M.; Yepes, V.; Milani, C.J. Selection of Sustainable Short-Span Bridge Design in Brazil. Sustainability 2019, 11, 1307.
  39. Lee, H.R.; Yu, J.; Yun, W.G.; Nam, J.; Kim, K.J. Estimating Environmental Load of PSC Beam Bridge Using Standard Quantities and Resource DB Model. KSCE J. Civ. Eng. 2019, 23, 958–972.
  40. Zhang, Y.R.; Wu, W.J.; Wang, Y.F. Bridge Life Cycle Assessment with Data Uncertainty. Int. J. Life Cycle Assess. 2016, 21, 569–576.
  41. Fifer Bizjak, K.; Lenart, S. Life Cycle Assessment of a Geosynthetic-Reinforced Soil Bridge System—A Case Study. Geotext. Geomembr. 2018, 46, 543–558.
  42. Pang, B.; Yang, P.; Wang, Y.; Kendall, A.; Xie, H.; Zhang, Y. Life Cycle Environmental Impact Assessment of a Bridge with Different Strengthening Schemes. Int. J. Life Cycle Assess. 2015, 20, 1300–1311.
  43. Du, G.; Pettersson, L.; Karoumi, R. Soil-Steel Composite Bridge: An Alternative Design Solution for Short Spans Considering LCA. J. Clean. Prod. 2018, 189, 647–661.
  44. Kim, H.; Tae, S.; Ahn, Y.; Yang, J. Scenarios for Life Cycle Studies of Bridge Concrete Structure Maintenance. Sustainability 2020, 12, 29557.
  45. Navarro, I.J.; Yepes, V.; Martí, J.V.; González-Vidosa, F. Life Cycle Impact Assessment of Corrosion Preventive Designs Applied to Prestressed Concrete Bridge Decks. J. Clean. Prod. 2018, 196, 698–713.
  46. Du, G.; Safi, M.; Pettersson, L.; Karoumi, R. Life Cycle Assessment as a Decision Support Tool for Bridge Procurement: Environmental Impact Comparison among Five Bridge Designs. Int. J. Life Cycle Assess. 2014, 19, 1948–1964.
  47. Habert, G.; Arribe, D.; Dehove, T.; Espinasse, L.; Roy, R. Le Reducing Environmental Impact by Increasing the Strength of Concrete: Quantification of the Improvement to Concrete Bridges. J. Clean. Prod. 2012, 35, 250–262.
  48. Martínez-Muñoz, D.; Martí, J.V.; Yepes, V. Comparative Life Cycle Analysis of Concrete and Composite Bridges’ Varying Steel Recycling Ratio. Materials 2021, 14, 4218.
  49. O’Born, R. Life Cycle Assessment of Large Scale Timber Bridges: A Case Study from the World’s Longest Timber Bridge Design in Norway. Transp. Res. Part D Transp. Environ. 2018, 59, 301–312.
  50. Hammervold, J.; Reenaas, M.; Brattebø, H. Environmental Life Cycle Assessment of Bridges. J. Bridge Eng. 2013, 18, 153–161.
  51. Kim, K.J.; Yun, W.G.; Cho, N.; Ha, J. Life Cycle Assessment Based Environmental Impact Estimation Model for Pre-Stressed Concrete Beam Bridge in the Early Design Phase. Environ. Impact Assess. Rev. 2017, 64, 47–56.
  52. Marzouk, M.; Hisham, M. Implementing Earned Value Management Using Bridge Information Modeling. KSCE J. Civ. Eng. 2014, 18, 1302–1313.
  53. Shim, C.-S.; Lee, K.-M.; Kang, L.S.; Hwang, J.; Kim, Y. Three-Dimensional Information Model-Based Bridge Engineering in Korea. Struct. Eng. Int. 2012, 22, 8–13.
  54. Vilventhan, A.; Rajadurai, R. 4D Bridge Information Modelling for Management of Bridge Projects: A Case Study from India. Built Environ. Proj. Asset Manag. 2020, 10, 423–435.
  55. Shim, C.S.; Yun, N.R.; Song, H.H. Application of 3D Bridge Information Modeling to Design and Construction of Bridges. Procedia Eng. 2011, 14, 95–99.
  56. Lee, K.M.; Lee, Y.B.; Shim, C.S.; Park, K.L. Bridge Information Models for Construction of a Concrete Box-Girder Bridge. Struct. Infrastruct. Eng. 2012, 8, 687–703.
  57. Markiz, N.; Jrade, A. Integrating fuzzy-logic decision support with a bridge information management system (BrIMS) at the conceptual stage of bridge design. J. Inf. Technol. Constr. 2018, 23, 92–121.
  58. Tanner, P.; Bellod, J.L.; Sanz, D. Paper and Pencil in the Age of BIM. Struct. Eng. Int. 2018, 28, 396–407.
  59. Dang, N.-S.; Rho, G.-T.; Shim, C.-S. A Master Digital Model for Suspension Bridges. Appl. Sci. 2020, 10, 7666.
  60. Hautala, K.; Järvenpää, M.-E.; Pulkkinen, P. Digitalization Transforms the Construction Sector throughout Asset’s Life-Cycle from Design to Operation and Maintenance. Stahlbau 2017, 86, 340–345.
  61. Fanning, B.; Clevenger, C.M.; Ozbek, M.E.; Mahmoud, H. Implementing BIM on Infrastructure: Comparison of Two Bridge Construction Projects. Pract. Period. Struct. Des. Constr. 2015, 20, 04014044.
  62. Kaewunruen, S.; Sresakoolchai, J.; Zhou, Z. Sustainability-Based Lifecycle Management for Bridge Infrastructure Using 6D BIM. Sustainability 2020, 12, 2436.
  63. Marzouk, M.; Hisham, M. A Hybrid Model for Selecting Location of Mobile Cranes in Bridge Construction Projects. Balt. J. Road Bridge Eng. 2013, 8, 184–189.
  64. Xu, Y.; Turkan, Y. BrIM and UAS for Bridge Inspections and Management. Eng. Constr. Archit. Manag. 2020, 27, 785–807.
  65. Almomani, H.; Almutairi, O.N. Life-Cycle Maintenance Management Strategies for Bridges in Kuwait. J. Environ. Treat. Tech. 2020, 8, 1556–1562.
  66. Park, S.I.; Park, J.; Kim, B.-G.; Lee, S.-H. Improving Applicability for Information Model of an IFC-Based Steel Bridge in the Design Phase Using Functional Meanings of Bridge Components. Appl. Sci. 2018, 8, 2531.
  67. Wan, C.; Zhou, Z.; Li, S.; Ding, Y.; Xu, Z.; Yang, Z.; Xia, Y.; Yin, F. Development of a Bridge Management System Based on the Building Information Modeling Technology. Sustainability 2019, 11, 4583.
  68. Zhang, Z.; Hamledari, H.; Billington, S.; Fischer, M. 4D beyond Construction: Spatio-Temporal and Life-Cyclic Modeling and Visualization of Infrastructure Data. Electron. J. Inf. Technol. Constr. 2018, 23, 285–304.
  69. Hüthwohl, P.; Brilakis, I.; Borrmann, A.; Sacks, R. Integrating RC Bridge Defect Information into BIM Models. J. Comput. Civ. Eng. 2018, 32, 04018013.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
Video Production Service
Feedback