Incorporating multi-physics deterioration analysis in building information modeling for life-cycle management of durability performance

Automation in Construction 110 (2020) 103004

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Incorporating multi-physics deterioration analysis in building information modeling for life-cycle management of durability performance

T

Jie Wu , Michael D. Lepech ⁎

Department of Civil and Environmental Engineering, Stanford University, United States of America

ARTICLE INFO

ABSTRACT

Keywords: Building information modeling Facility management Durability Life-cycle management Sustainability

Extending the application of Building Information Modeling (BIM) tools into the operation and management phases of a building's life cycle is a significant advance in recent years. However, few studies have investigated or developed tools for the life-cycle management of durability performance of built structures leveraging BIM. This gap is partly due to (i) the lack of an accessible interface that connects BIM software with durability models of built structures, and (ii) no available durability models that are fundamental in nature and thus can integrate with other tools. To begin addressing this gap, this paper presents a framework that uses the Application Programming Interface (API) within BIM to integrate physics-based durability performance models, which extends the application of BIM into the post-construction phases by enabling more accurate predictions of life cycle performance. An application of the framework is presented for analysis and management of a reinforced concrete structure.

1. Introduction Building information modeling (BIM) has transformed the design and construction of buildings over the past decades, while also facilitating a growing set of sustainability design requirements in the architecture, engineering and construction (AEC) industry [1]. This transformation includes the application of BIM throughout the operation and management phases of a facility [2–5]. Efforts to extend BIM beyond the design and construction phases of a project have been significant. These efforts have encompassed building survey and as-built BIM applications, use-phase modeling and energy management, enabling access to and integration of maintenance information and knowledge, as well as real-time information exchange and interoperability [2]. According to Aziz et al., the potential value proposition of BIM for facility management (FM) stems from reduced operational cost, shorter cycle times for decision-making, its function as a central resource for decision-makers, and its ability to provide better documentation, improved collaboration and work flexibility, and updated information thorough the building life cycle [6]. While the potential benefits of integrating BIM into facility management are significant, there remains a gap in fundamental or application-based research on life-cycle facility management that considers the ongoing degradation of buildings and building materials over time, even as BIM is widely applied in the structural design of new buildings

⁎

in both academic research and industry practice [7–9]. Most closely related to the consideration of long-term degradation or durability performance of built structures are efforts that consider the sustainability performance of built structures that rely on BIM. For example, Oti et al., demonstrated the use of Revit® to assess the sustainability score of alternative conceptual design solutions by considering life cycle cost and carbon and ecological footprint [10]. In such studies, however, sustainability metrics are calculated based on the environmental impacts of building material production and BIM is used to automate material quantity take-offs. The durability or service life performance of a built structure during the decades-long use phase is not considered. Thus, current tools (e.g., [10]), remain limited to the early design stages and cannot extend into the operation phase of a built structure as a facility management, decision-support, or maintenance optimization tool. Although the ongoing degradation of buildings and building materials over time has not yet been integrated with BIM capabilities, the management and maintenance of built facilities during their operation and use phase is essential to maintain an acceptable level of serviceability and to minimize life cycle costs. Built structures are continuously at risk from aging, fatigue, and deterioration processes resulting from aggressive chemical attacks and other physical damage mechanisms over a decades-long lifetime [11]. Currently, facility owners typically often defer maintenance until major defects or damage are observed

Corresponding author. E-mail address: [email protected] (J. Wu).

https://doi.org/10.1016/j.autcon.2019.103004 Received 14 May 2019; Received in revised form 16 September 2019; Accepted 1 November 2019 0926-5805/ © 2019 Elsevier B.V. All rights reserved.

Automation in Construction 110 (2020) 103004

J. Wu and M.D. Lepech

[12]. With appropriate information, modeling, and planning, preventive maintenance can be performed before deterioration propagates, allowing buildings to maintain serviceability and extend service life, while essential maintenance can restore reduced performance to a desired or safe service level [13]. Further, the application of optimization algorithms to plan maintenance activities can improve the life cycle performance of buildings, increase the structures' service life, reduce loss due to unexpected repair events, better allocate budget resources, and minimize the life-cycle cost and environmental impact of the structures [11,12,14–16]. While the potential value proposition of BIM for facility management, maintenance planning, and life cycle optimization is significant, accurate models of building performance and deterioration are needed. Recent advances in computational performance, material science, and structural engineering research have enabled interdisciplinary computational linking of such models in the form of “multi-scale multi-physics” models of built environments. These models are multi-scale in nature, computationally linking fundamental phenomena from the millimeter-scale (e.g., electrochemical anode and cathode formation on corroding rebar) to the meter-scale (e.g., formation of corrosion-induced surface cracks on reinforced concrete structural elements). These models are also multi-physics in nature, coupling, for instance, the physics of heat and matter transport, the physics and electrochemistry of reinforcement corrosion, and the mechanics of quasi-brittle concrete fracture [15]. As shown by Lepech et al., it is becoming possible to predict the deterioration of built environments over time based on fundamental material characteristics (i.e., concrete diffusion coefficient) and environmental loads (i.e., temperature profiles, chemical exposures) that result in deterioration, corrosion, and structural degradation of reinforced concrete structures [15]. Despite these advancements, such multi-physics models have not been applied in practice for facility management of built structures for two major reasons. First, there is a lack of multi-physics deterioration models that provide sufficient information regarding deterioration phenomena and processes such that it is useful for facility managers and decision-makers. As an example, researchers have shown that it is possible to successfully predict the deterioration of reinforced concrete structures using multi-physics models (e.g., [15,17]). However, the geometries of these models are often too simplified to represent realworld applications and thus lack the ability to provide actionable information for facility managers and decision-makers. One illustration of this over-simplification is the omission of shear reinforcement (i.e., stirrups) in multi-physics models of reinforced concrete elements undergoing corrosion of the reinforcing steel [15,17]. Located close to exterior faces and serving as an important electrical conductor between longitudinal reinforcing bars in the steel reinforcement cage, shear reinforcement is an important component of the electro-chemical reinforced concrete system. Ozbolt et al. [18] looked to address this oversimplification by including stirrups in a reinforced concrete model geometry. However, the Ozbolt et al. model is still rough in that it simplifies the stirrups' shapes to a closed loop, ignoring hook ends, and predefines an anode-cathode configuration that does not reflect the stochastic nature of corrosion initiation in reinforced concrete elements. A second, and more substantial, barrier to the practical application of multi-physics deterioration models is a lack of tools for rapid and efficient model construction and interpretation of results. As deterioration models become more complicated, a larger effort is required to build them. This is particularly the case with multi-physics deterioration models since the required model detail (e.g., shear reinforcement position within a reinforced concrete element) is in excess of BIM LOD 500. Many person-hours are required if the models are not parameterized and model construction is not automated, resulting in an analysis tool that is too costly or time-consuming to practically use. According to Azhar et al., such economic reasons are among the primary causes for not implementing sustainable design and construction

procedures [3]. Given these two barriers, there is a need for analysis tools that integrate complex deterioration models within a BIM environment and facilitate practical application. One successful application of science-based analysis models integrating with BIM, as discussed in literature [3,19,20] is building energy and performance analysis. BIM-based energy tools such as Autodesk® Insight™ are able to integrate energy analysis directly into BIM by automatically creating energy models using the geometrical and environmental information stored in BIM [21]. Such BIM-based energy analysis tools demonstrate value by reducing the cost of running energy analyses and making sustainability analysis more accessible to building design professionals. While only intended for high-level conceptual design, these tools such as Insight™ have been adopted in practice because they provide actionable information to building designers regarding the anticipated sustainability impacts and are simple to run within the native BIM environment [3,20], and thus overcoming the barriers described for greater adoption of multi-physics deterioration models. Additional examples exist of the extension of BIM application into facility management. For example, Wetzel et al., presented a BIM-based framework to support safe maintenance and repair practices during the facility management phase, through safety attribute identification/ classification, data processing and rule-based decision making, and a user interface [22]. Motawa et al., provided maintenance teams in public organisations with an integrated knowledge-based BIM system that helps capture/retrieve all relevant information/knowledge on maintenance operations [23]. Kang et al., proposed a software architecture for the effective integration of building information modeling into a geographic information system based facilities management system that could improve process reusability and extensibility [24]. Chen et al., proposed an facility maintenance management framework based on BIM and facility management systems, which can provide automatic scheduling of maintenance work orders to enhance good decision making [25]. Motamedi et al., presented a knowledge-assisted BIM-based visual analytics approach for failure root-cause detection in facility management by utilizing BIM visualization capabilities to provide facility management technicians with visualizations that allow them to utilize their cognitive and perceptual reasoning for problem solving [26]. 2. Motivation Motivated to overcome these barriers and recognizing the prior successful integration of energy modeling tools within BIM, this paper presents the development and demonstration of a framework that further extends BIM into the facility management phases of a project by enabling fundamental deterioration modeling and durability assessment within BIM. The framework utilizes the BIM Application Program Interface (API) [27,28] to integrate physics-based models in order to simulate long-term deterioration of built structures. Based on simulations, the framework also provides decision support and visualization through a Graphical User Interface (GUI) for facility managers tasked with life-cycle management of built structures. This paper demonstrates the framework by integrating a multi-physics model of chloride-induced corrosion of reinforced concrete structures, one of the most common deterioration processes of reinforced concrete worldwide [11]. Development of the integration architecture is described in detail in Section 3 of this paper. Section 4 introduces the multi-physics deterioration model developed for this research. Section 5 presents the modeling workflow and details the integration between the multiphysics model and BIM, and demonstrates the framework's potential application via a simple case study of a reinforced concrete building frame exposed to chlorides (i.e., deicing salts, ocean spray). Section 6 makes conclusions and suggests future work. 2

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Fig. 1. Integration architecture of building information model (BIM) with multi-physics deterioration model of reinforced concrete.

the multi-physics simulation, results from the deterioration analysis are then presented to users directly in Revit® through the Add-on (“Simulation Feedback” and “Results Visualization”, as shown in Fig. 1). The deterioration modeling suite is based on an interdisciplinary model for chloride-induced corrosion and deterioration of reinforced concrete elements that has been introduced by Michel et al. [29,30] and Lepech et al. [15], and extended in this paper. This multi-scale, multiphysics model combines physical, chemical, electrochemical, and fracture mechanics processes that collectively lead to reinforced concrete corrosion, deterioration, cracking, and failure. The model consists of four modules, (i) a MATLAB Interface for data interoperability (“Deterioration Model Interface”, as shown in Fig. 1), (ii) a Finite Element Model (FEM) in COMSOL Multiphysics® for modeling transport of heat and matter (i.e., moisture, chloride, oxygen) (“Coupled Transport Model”, as shown in Fig. 1), (iii) an FEM in COMSOL Multiphysics® for modeling electrochemical processes at the reinforcement surface (i.e., corrosion initiation, corrosion reactions) (“Corrosion Model”, as shown in Fig. 1), and (iv) an FEM-lattice model in MATLAB for modeling crack formation in the concrete domain (“Crack Model”, as shown in Fig. 1). Preprocessing done by the Customized Add-on enables the model to simulate practical concrete element and steel reinforcing geometries automatically through parameterization. The MATLAB interface ports data from the Customized Add-on and assembles the corresponding computational models in COMSOL Multiphysics® and MATLAB without user intervention. The MATLAB interface also serves as a communication bus to fully couple all three deterioration modules (transport, corrosion, and cracking). Results from these deterioration models, such as corrosion current density along the reinforcing steel surface and reinforcing bar cross section loss, are recorded within the MATLAB interface and passed to the Customized Add-on for post-processing and visualization.

3. Modeling and integration architecture The framework for incorporating the multi-physics deterioration model of reinforced concrete building elements into BIM is presented in Fig. 1. The framework is implemented within the Revit® modeling environment in this research, because of the maturity of the Revit® API and the broad-based industry adoption of Revit® software. The Revit® API provides specifications for programming language routines, data structures, classes and variables to enable non-Revit® software components to interact with each other and allows access to a specific database, hard drive, disc drive, video card, etc. [10,28]. For Revit®, the API enables data import and export of BIM tools, allows communication between BIM software and external applications, and allows for the addition of new functionalities through extensions. A number of research and commercial applications have demonstrated the successful use of the Revit® API [10]. For this work, an Add-on in Revit®, developed with the API in Visual C# (“Customized Add-on”, as shown in Fig. 1), serves as the central integration element of the framework. This Add-on extracts geometry and material properties from each element within the BIM model (“BIM-Info Extraction”, as shown in Fig. 1). To model chloride-induced deterioration of reinforced concrete using the multi-physics model, the necessary information extracted from the BIM model includes the 3D arrangement of each reinforced concrete element, the reinforcing steel size, the reinforcing steel location, the reinforcing steel geometry, the strengths of steel and concrete materials, and other related information (if available) such as material thermal conductivity, material density, and the specific heat capacity of concrete and steel materials. This information serves as a direct input into the multi-physics deterioration model detailed in Section 4. For information not related to the reinforced concrete element or its material constituents, users are asked to provide additional necessary information (“User Input”, as shown in Fig. 1) such as ambient temperature, ambient relative humidity, surface chloride ion concentrations, and other environmental exposure data needed for multi-physics deterioration simulation. In future cases, real time sensing networks may be used to provide environmental exposure data (“Real-time Info”, as shown in Fig. 1). Data extracted from the BIM model and input by users is processed within the Add-on and fed into the multi-physics deterioration model (“Data Preprocessing” feeding into “Deterioration Model Interface”, as shown in Fig. 1). After running

4. Multi-scale multi-physics deterioration model The deterioration model used in this research is based on an interdisciplinary model for chloride-induced corrosion and deterioration of reinforced concrete elements that has been introduced by Michel et al. [29,30] and Lepech et al. [15]. The model connects computational models that describe (i) transport of heat and matter and chemical processes resulting in changes in phase assemblage in hydrated 3

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Portland cement, (ii) electrochemical corrosion processes at the steel reinforcement-concrete interface, and (iii) corrosion-induced damage in concrete such as radial cracking and cover spalling. The model is fully coupled such that information including phase assemblage, moisture, chloride and temperature distributions, corrosion rates, and the damage state of the concrete are continuously exchanged among the three computational models [15]. For this study, significant improvements were implemented over the model developed by Michel et al. and Lepech et al. Most importantly, the model was extended from a two-dimensional formulation capable of analyzing simplified element geometries to a three-dimensional formulation capable of analyzing practical reinforced concrete building elements. As discussed previously, the deterioration model was also parameterized for this study to facilitate automated FEM model assembly in COMSOL Multiphysics®.

concrete element, including chloride concentration, temperature, and relative humidity profiles, for each surface, serve as the boundary conditions for the finite element model. Nonconstant material properties, such as concrete density, and environmental exposure profiles, are treated as input variables for the deterioration model (Section 4.4). Detailed information on the models that describe the coupled transport of heat and matter, the chemical processes that result in phase assemblage changes in hydrated Portland cement, along with experimental verification of the transport and chemical process models can be found in Michel et al. [29]. 4.2. Corrosion model Within the multi-physics model, corrosion is based on stringent physical laws describing the thermodynamics and kinetics of electrochemical processes, which include various reinforcement corrosion phenomena, such as anodic and cathodic polarizations. Such polarizations describe the relationship between electrical potential of the corrosion reduction-oxidation reactions and the spatial density of the electrical current being passed in the reactions. The impact of temperature, relative humidity, and oxygen concentration on these reactions is also considered. The coupled physiochemical process is simulated using a finite element model where Laplace's Equation (Eq. (4)) describes the electrical potential distribution in concrete, assuming electrical charge conservation and isotropic conductivity.

4.1. Coupled transport model Within the multi-physics model, the fully coupled transport of heat, matter, and ions, as well as thermodynamic principles for phase changes in hydrated Portland cement, are simulated in a finite-elementbased transport and chemical process model. The Nernst-Planck Equation (Eq. (1)) is used to describe the transport of ions in porous media (i.e., concrete) taking into account three different transport phenomena; diffusion, migration, and convection.

ci = t

(Di ci + z i um, i Fci E

ci v )

(1)

2E

where, E is the electrical potential. Ohm's Law (Eq. (5)) is used to determine the corrosion current density based on the potential distribution and resistivity of the electrolyte (concrete pore water).

where, ci is the ionic concentration, Di the ionic diffusion coefficient, zi the charge number of the ionic species, um, i the ionic mobility, F Faraday's constant, E the electrostatic potential and v the velocity of the solvent [29]. Coupled heat and moisture transport in concrete is described using Richard's Equation, as shown in Eq. (2) and Eq. (3).

T C = t l

pc

pc pC

(kT , T T + kT , pC pC ) = CpC

pC = t

(kpC, pC pC + kpC, T T )

(4)

=0

icorr =

1 conc

E n

(5)

where, icorr is the corrosion current density, ρconc the concrete resistivity, and n the equipotential [29]. The kinetics of the electrochemical corrosion processes are described by anodic and cathodic polarization curves, as shown in Fig. 2. The electrochemical processes are coupled with transport mechanisms to account for the impact of temperature, relative humidity, and oxygen transport on corrosion. To link initiation and propagation of corrosion, when the critical chloride concentration along the reinforcement is reached, an anode forms while the remaining reinforcement surface stays cathodic. The corrosion current density along the reinforcement is then used to model the reinforcement corrosion and Faraday's Law (Eq.

(2) (3)

where ρ is the mass density of concrete, C the specific heat capacity of concrete, T the temperature, t the time, pC the logarithm of the capillary pressure, θl the moisture content, pc the capillary pressure, CpC the moisture capacity and kpC, T and kpC, pCtransport coefficients for the heat and moisture transfer respectively [29]. Time-dependent environmental exposure profiles of the reinforced

Fig. 2. Corrosion current density versus potential (polarization curves) on the steel surface for anodic and cathodic reactions of corroding reinforced concrete [29]. 4

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(6)) is used to sum metal mass loss over time.

X (t ) =

MFe zF Fe

reinforcing bars. Object-oriented programming in C# and MATLAB was applied, and each rebar object was defined with properties including rebar type (i.e., longitudinal rebar or shear rebar), size, shape, coordinates in 3D space, and material property object. Each concrete object was defined with properties including shape, coordinates in 3D space, and material property object. In addition, rebar objects contained within the concrete 3D space and environmental exposure condition objects on all surfaces were defined within each concrete object. Each material property object was defined with properties including material type (i.e., steel or concrete), tensile strength, Young's modulus, thermal conductivity, specific heat capacity, density and water/cement ratio for concrete. Each environmental exposure condition object was defined with a time-dependent chloride concentration profile, relative humidity profile, and temperature profile. These profiles are arrays with two vectors, a time vector and an exposure vector. The time vectors for these three profiles can be different, in which case linear interpolation is applied to achieve artificial consistency in temporal resolutions for the boundary conditions in the model. The structure of object classes used for preprocessing is shown in Fig. 4. The information regarding an entire reinforced concrete element object in Revit® and pertaining to geometry information of the concrete domain and geometry information of the steel reinforcement cage within the reinforced concrete element is extracted and stored in .txt files external to Revit® that can be accessed by the MATLAB Deterioration Model Interface. User defined, default modeling values, the deterioration analysis period, and the time step for the analysis are also stored within.txt files external to Revit® that can be accessed by the MATLAB Deterioration Model Interface. In addition to parameterization of design details, the deterioration model was also modified to accommodate complex environmental exposure conditions and interactions among different regions of the reinforcing steel cage. Complex environmental exposure conditions (i.e., different surface temperatures on each element face due to direct sunlight or shading) are parameterized within the environmental exposure object associated with each surface of each reinforced concrete element. Complex electrochemical interactions among different regions of the reinforcing steel cage are dependent upon the electrical potential distribution, in which the critical chloride threshold plays an essential role. Chloride-induced steel reinforcement corrosion in non‑carbonated, alkaline concrete begins once the chloride ion concentration at the steel reinforcement surface has reached a threshold value, often referred to as the critical chloride threshold [31]. The spatial variation of the critical chloride threshold on the steel rebar surfaces, the spatial and temporal distributions of the actual chloride ion concentration at the steel rebar surfaces, and the time-dependent effects of localized cathodic protection after corrosion initiation together determine the complex pattern of anode and cathode couples on the steel rebar surface that drives the reduction-oxidation corrosion reactions [32]. Within the deterioration model, the critical chloride threshold on the reinforcing steel surface is modeled using a Gaussian Random Field. The minimum critical chloride threshold along the steel reinforcing bar is determined by a beta-distribution with a lower boundary of 0.2% chloride by weight of cement and a mean value of 0.6% by weight of cement, as proposed by the fib Model Code for Service Life Design [33].

t

icorr (t ) dt 0

(6)

where, X(t) is the cross-sectional reduction of the reinforcement as a function of time, t, MFe the molar mass of iron, z the charge number, F Faraday's constant and ρFe the density of iron. More information regarding the corrosion model can be found in Michel et al. [29]. 4.3. Crack model Within the multi-physics model, corrosion-induced damage in the concrete is described within a mechanical performance model using a thermal analogy to model the expansive nature of solid corrosion products (i.e., rust). The model accounts for non-uniform formation of corrosion products around the circumference of the reinforcement, as well as penetration of corrosion products into the available pore space of the surrounding cementitious matrix. To simulate crack formation, the mechanical performance model uses a coupled lattice and FEM model, where the reinforcement and corrosion product domains are discretized by FEM and a lattice approach is used for discretization of the concrete domain (Fig. 3). Crack initiation and propagation within the concrete domain are simulated by sequentially removing the lattice element with the highest stress to tensile strength ratio greater than unity. The process is repeated until failure. Crack widths can be estimated using the width of the gap between disconnected lattice elements. Additional information on the modeling approach for the mechanical performance model is provided in Michel et al. [30]. 4.4. Customized add-on preprocessing architecture For generally applicability to a range of practical reinforced concrete element geometries, the deterioration model developed by Michel et al. and Lepech et al. was modified within the MATLAB Interface to accommodate a wide variety of design details. Design details including geometry information and material properties were parameterized as input variables to handle a range of structural designs in which a range of longitudinal and shear reinforcing bar configurations are allowed. The range includes deformed steel rebar from 10 mm to 57 mm in diameter and includes rebar cage configurations that are rectangular or circular in shape with any number of longitudinal bars and shear

5. Deterioration modeling workflow As discussed previously, a barrier to the practical application of multi-physics deterioration models is a lack of accessible tools for easy integration with BIM models. Thus, the functionality to run deterioration analysis with minimum human intervention and interpretation, and to provide straightforward analysis feedback directly in BIM, is critical. This functionality is achieved by the Customized Add-on in BIM and the deterioration model interface within MATLAB by collecting information stored in BIM and from users, preprocessing the

Fig. 3. Cross-section geometry of a sample reinforced concrete element with four longitudinal reinforcing bars within the coupled lattice and FEM model (Note: the corrosion product domain is magnified by a factor of 100 for visibility). 5

Automation in Construction 110 (2020) 103004

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Fig. 4. Structure of object classes for parameterization of reinforced concrete element design details within the MATLAB Interface.

information, automatically assembling multi-physics models, analyzing results, and rendering feedback within BIM.

.txt files that can be accessed by the MATLAB Deterioration Model Interface.

5.1. Information collection from BIM and users

5.2. Information preprocessing and model assembly

Within the Customized Add-on, instructional windows prompt users to select specific reinforced concrete building elements for deterioration analysis within the BIM graphical user interface (i.e., Revit®). The related information within the BIM elements is then extracted by the Customized Add-on. Specifically for use in chloride-induced deterioration analyses, related information stored in a reinforced concrete element object in Revit® contains geometry information of the concrete domain (i.e., dimensions of the element and global coordinates of each vertex), and geometry information of the steel reinforcement cage within the reinforced concrete element (i.e., rebar sizes, number of rebars in the cage, shapes and coordinates of critical vertices depending on the specific rebar configuration). As detailed in Section 4, the extracted information is stored in .txt files externally that can be accessed by the MATLAB Deterioration Model Interface. With the exception of the geometry information discussed above, most deterioration-related information is usually missing from the BIM information ontology. Information pertaining to the material property object (described earlier) and the environmental exposure condition object (described earlier) are essential for deterioration analysis and may vary among reinforced concrete elements. Thus, users are prompted to provide this information, or use default modeling values, within external .txt files that can be accessed by the MATLAB Deterioration Model Interface. With the proliferation of available realtime data, wireless inspection networks, and structural health monitoring systems that can provide local weather data, element surface temperature and humidity, and other modeling information, continuous updating can also be used in the future to continually improve the accuracy of multi-physics deterioration simulations that are built upon fundamental inputs of temperature, relatively humidity, and surface ion concentrations. Such continuous updating of model inputs provides the decision-maker with model results that better represent real-world conditions of the structure. Finally, the Customized Add-on prompts users to provide a deterioration analysis period and a time step for the analysis. Once again, the information is collected and stored in

After all the necessary information is collected by the Customized Add-on and stored in .txt files, the MATLAB deterioration model interface preprocesses the information in order to assemble the deterioration model and run the analysis modules in COMSOL Multiphysics® and MATLAB. Data stored in .txt files is read into the MATLAB deterioration model interface and reformatted to construct concrete, reinforcing steel, material properties, and environmental exposure conditions objects according to the data structure shown in Fig. 4. The center of a random chosen surface of the concrete element is set to be at the original point of the coordinate system in COMSOL Multiphysics® and the rest of the coordinates of concrete element geometry and steel reinforcement geometries are then modified to comply with the new coordinate system through rotation and shift operations. A major component of preprocessing is upgrading the geometry information fidelity of the steel reinforcing cage. BIM software platforms are designed to provide architecture, engineering, and construction professionals with the insight and tools to more efficiently plan, design, construct, and manage buildings and infrastructure [34]. For some building and infrastructure projects, reinforcing steel elements have been modeled within BIM as constructed assemblies for building maintenance and operations in which the accurate size, shape, location, quantity, and orientation of individual steel rebar elements are required (BIM Level of Development (LOD) 500) [35]. However, such BIM LOD 500 models are uncommon in practice and still cannot provide the high resolution required for a computational physics-based model, particularly with regard to reinforcing steel cage detailing. For example, Fig. 5 shows a sample part of a reinforcing steel cage in a COMSOL Multiphysics® finite element model containing four longitudinal steel bars and a two-legged stirrup with 135° bend hooks, which is described as a T1 rebar shape in Revit®. To assemble a working multi-physics deterioration model with such resolution, detailed information beyond the general shape, size, and location of the stirrups and other components of the steel reinforcement 6

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Fig. 5. High fidelity geometry of steel reinforcement cage upgraded using MATLAB preprocessor with (a) side view, (b) top view, and (c) close up of stirrup corner.

cage is required. Rarely can this detailed information even be taken from specialized Revit® models built using the Revit® rebar detailing tool [36] or from conventional reinforcement bar detail drawings, thus requiring an automated data fidelity upgrade within preprocessing. For example, the exact positions of each leg of the reinforcement stirrup need to be defined within the COMSOL model. Specifically, the gap between each hook end of a stirrup (d1 in Fig. 5a), and the gap separating a stirrup and its adjacent longitudinal rebar (d2 in Fig. 5a) are rarely specified in design details using either Revit® models or traditional drawings. When modeling the electrochemical macro-cell formed by the entire steel reinforcement cage and the surrounding concrete domain, these geometric details must be defined. Thus, these missing values are treated within the MATLAB deterioration model interface as random variables considering they are stochastic in nature and largely affected by the manufacturing and construction processes that produced the reinforcement cage and reinforced concrete element, respectively. For the stirrup configuration shown in Fig. 5, the corner 90° bends of the stirrup are assumed to be a perfect quarter circle with radius of curvature centered at the center of the adjacent longitudinal rebar. The gap

between longitudinal rebar and stirrup, 2, remains constant throughout the 90° bend. The value of d2 is assumed to follow lognormal distribution with a mean of 12.7 mm and coefficient of variation of 0.1. The distribution is truncated, with a lower bound equal to the minimum element size used in COMSOL Multiphysics®. The values of mean and coefficient of variation are based on reinforcing steel fabrication requirements from the ACI Standard Specifications for Tolerance for Concrete Construction and Materials [37]. The use of a lognormal distribution, which is skewed to the right, recognizes that gaps between rebars are intended to be zero in practice. For similar reasons, the gap, d1, between two hook ends of the stirrup is assumed to follow the same distribution. The vertical location of the stirrup is assumed constant, except for the fourth leg (L1 in Fig. 5a), which slopes linearly downward until reaching the final 135° bend and flattening. As with 90° bends, d2 remains constant around each 135° bend. For other steel reinforcement cage configurations, similar stochastic approaches are used to upgrade the geometry information fidelity of the steel reinforcement cage for use in the deterioration model. These stochastic models contribute to the uncertainty of the deterioration model and can be modified and refined to improve accuracy in future work if related data is available. 7

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Fig. 6. Building three-dimensional finite element models in COMSOL Multiphysics® from (a) building information model for a reinforced concrete column with four longitudinal rebars and T1 shape stirrups, to (b) a 3D high-fidelity geometric representation, to (c) a multi-physics finite element model with finite element mesh.

Using the preprocessed information, the deterioration model interface in MATLAB assembles the concrete and reinforcement domains within the finite element deterioration model based on data stored in the concrete element, rebar, material property and environmental exposure condition objects as described in Fig. 4. A finite element mesh is generated for these domains as shown in Fig. 6(c). In addition to the model geometry and associated finite element mesh, the multi-physics model requires a number of material property and environmental exposure conditions. These are shown schematically in the object class structure in Fig. 4. Table 1 provides the values that were used for demonstration of the framework, along with a source for each of these values. The values of other multi-physics deterioration modeling parameters including diffusion coefficient of chloride ion in concrete at saturation that are assumed to be invariable across different building components and thus are not collected by the Customized Addon could be found in Michel et al. [30] and Flint et al. [38].

the critical chloride threshold, the corrosion current density becomes non-zero according to polarization curves for steel in concrete (Fig. 2). At this time-step, a two-dimensional damage model for the concrete domain surrounding the reinforcing steel is constructed in MATLAB (Fig. 3) for each cross section at which an electrochemical anode forms on the steel. Simulation results from all of the models, including maps of corrosion current density on reinforcement surface, concrete domain crack paths and widths, and 3-D distributions of corrosive matter in the concrete domain (i.e. chloride ions, moisture, and heat) are stored in external text, image, or output files for additional post-processing and visualization. For demonstration, Fig. 7 shows a sample corrosion current density map on the steel reinforcement surface and a sample corrosion crack map for one horizontal cross section of the column element modeled in Fig. 6.

5.3. Deterioration simulation

Following deterioration simulation, results are returned to the Revit® Customized Add-on for visualization. Result images, such as corrosion current density maps and crack maps, can be directly overlaid onto the BIM element visualization in the Revit® GUI. Key indicators like maximum corrosion current density, maximum rebar cross-section reduction, and maximum crack width can also be presented directly on the BIM element visualization in the Revit® GUI. Although such images and values are essential results of deterioration simulation, they do not always provide straightforward, easy-touse information for decision makers such as engineers, facility managers, and owners. Fig. 8 shows one potential form of direct information visualization for facility managers. A screenshot of deterioration analysis results for multiple reinforced concrete structural elements of a parking garage is shown. The results of the deterioration analysis are presented to decision-makers through color gradations in Revit®. Various colors indicate increasing deterioration risk, in this case the probability that each element will exhibit corrosion-induced surface cracking 25 years in the future (red shading indicates highest probability, pink shading indicates medium probability, yellow shading indicates minor probability, and green shading indicates low probability

5.4. Analysis and feedback

During the deterioration simulation, the transport model captures chloride ion penetration into the concrete domain, reaching the reinforcement surface as a function of time. For those reinforcing steel bar elements at which the local chloride ion concentration is higher than Table 1 Input values for the multi-physics deterioration modeling parameters. Parameter

Value

Unit

Source

Thermal conductivity of concrete Specific heat capacity of concrete Density of concrete Tensile strength of concrete Young's modulus of concrete Water/cement ratio of concrete Density of steel Chloride concentration profile Temperature profile Relative humidity profile

1.4 880 2400 3.94 42.25 0.5 7850 Time-dependent Time-dependent Time-dependent

W/m K J/kg K kg/m3 MPa GPa – kg/m3 mol/m3 K –

[38] [38] [38] [30] [30] [38] [38] [38] [38] [38]

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Fig. 7. For a reinforcement concrete column element, (a) sample corrosion current density map on the steel reinforcement surface, and (b) sample crack map for a horizontal cross section.

Fig. 8. Visualization of the probability that each reinforced concrete element will exhibit corrosion-induced surface cracking 25 years in the future for structural elements of a parking garage. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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of surface cracking). Such simple visualizations of future risk can be used by decision-makers to guide planning for repair and maintenance activities in order to achieve improved life cycle facility performance (i.e., more efficient budget allocations over time and reduced environmental impacts such as life cycle carbon dioxide emissions).

exposure, chemical sulfate attack, and alkali-silicate reaction. Due to the multi-physics nature of the model formulation, these can be considered in future work. The deterioration model is limited in that it does not account for spatial variability of materials, rather assuming homogeneity of material properties such as concrete diffusion coefficient. A number of opportunities for future research are currently being explored including (i) sensitivity analysis to verify assumptions and enable more accurate model upgrading (i.e., reinforcement stirrup shapes and locations, spatial distributions of critical chloride concentration thresholds on steel rebar surfaces, size of anodes and cathodes), (ii) software parallelization to enable Monte-Carlo simulation and stochastic life cycle management optimization, (iii) fundamental improvement of the multi-physics deterioration model to include reinforced concrete corrosion due to carbonation or other deterioration mechanisms, and (iv) demonstration of an iterative design of a reinforced concrete element for optimal durability performance over its service life.

6. Conclusion and future work There exist no fundamental tools for the life-cycle management of durability performance of built structures leveraging multi-physics models and building information models (BIM). This gap is due to (i) the lack of an accessible interface that connects BIM software with durability models of the built environment, and (ii) no available durability performance evaluation tools that are fundamental in nature and thus can integrate with other tools. This paper presents a novel framework to enable high-fidelity durability performance assessment and visualization of life cycle performance of facilities. The framework integrates multi-physics durability performance models within BIM and extends the use of BIM into the post-construction phases of a facility. The framework is able to extract and preprocess information from existing BIM models and automatically upgrade the fidelity to meet the requirement of computational physics-based models. It can also solicit additional user-defined information in a Revit® Customized Add-on with a graphical user interface. The framework queries the necessary modeling inputs, assembles detailed finite element models, and runs computational physics-based deterioration analysis in a deterioration modeling suite. Simulation results are displayed directly in BIM for the user or decision-maker to visualize. In this research, the framework was demonstrated for chloride-induced corrosion analysis for reinforced concrete elements. Specifically, this work developed a Customized Addon that utilizes the Revit® API and a corresponding deterioration model interface in MATLAB to build coupled chloride-induced deterioration models in COMSOL Multiphysics® and MATLAB. The deterioration modeling suite was extended from a two-dimensional formulation to a three-dimensional formulation capable of analyzing practical reinforced concrete building elements. In addition, the deterioration model was also parameterized for this study to facilitate automated model assembly. This framework provides users like engineers, facility managers, and owners a user-friendly interface that connects BIM software with durability models of the built environment. By leveraging BIM for storage of geometry and material property information, this study contributes to the growing range and types of fundamental analysis tools for life cycle performance that designers can easily and efficiently use during the design process, and facility managers can apply to better understand the future performance of their structures. The durability models developed are fundamental in nature, thus allowing for integrating with other tools that rely on similar data inputs. The main contribution of this work is the foundation of a datadriven decision support tool that is based on a science-based deterioration simulation, which enables optimization of life cycle performance and improved maintenance of built structures. Such detailed deterioration analysis could trigger increased frequency of inspections, pre-emptive surface treatments or painting, or installation of cathodic protection to prevent further propagation of corrosion of reinforced concrete structures. One potential form of this decision support was demonstrated for reinforced concrete element deterioration risk visualization. Such visualizations can be used to guide the planning of future repair and maintenance activities in order to achieve improved life cycle facility performance and reduced facility downtime for maintenance. Limitations of this work include a narrow demonstration of the multi-physics model by only capturing chloride-induced steel reinforced corrosion. Many other deterioration mechanisms may deteriorate and ultimately lead to failure of reinforced concrete structures including carbonation-induced corrosion, cyclic fatigue, freeze-thaw

Declaration of competing interest None. Acknowledgements The authors would like to thank Professor Mette Geiker at the Norwegian University of Science and Technology, Professors Henrik Stang and Alexander Michel at the Technical University of Denmark, and Dr. Bo Shen and Dr. Glenn Katz at Stanford University for their valuable suggestions and collaboration on this research. The authors would like to acknowledge the John A. Blume and James Gere graduate fellowships and Thomas V. Jones engineering faculty scholarship at Stanford University for their generous support. This research is also funded by the United States National Science Foundation (Award #1453881). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the United States National Science Foundation. References [1] X. Zhao, A scientometric review of global BIM research: analysis and visualization, Autom. Constr. 80 (2017) 37–47, https://doi.org/10.1016/j.autcon.2017.04.002. [2] D. Ilter, E. Ergen, BIM for Building Refurbishment and Maintenance: Current Status and Research Directions, (2015), https://doi.org/10.1108/SS-02-2015-0008. [3] S. Azhar, J. Brown, R. Farooqui, BIM-based sustainability analysis: an evaluation of building performance analysis software, 45th ASC Annual Conference, 2009, pp. 1–4, , https://doi.org/10.1002/prsb.720090405. [4] I. Motawa, A. Almarshad, A knowledge-based BIM system for building maintenance, Autom. Constr. 29 (2013) 173–182, https://doi.org/10.1016/j.autcon. 2012.09.008. [5] R. Volk, J. Stengel, F. Schultmann, Building Information Modeling (BIM) for existing buildings - literature review and future needs, Autom. Constr. 38 (2014) 109–127, https://doi.org/10.1016/j.autcon.2013.10.023. [6] N.D. Aziz, A.H. Nawawi, N.R.M. Ariff, Building Information Modelling (BIM) in facilities management: opportunities to be considered by facility managers, Procedia - Social and Behavioral Sciences 234 (2016) 353–362, https://doi.org/10. 1016/j.sbspro.2016.10.252. [7] H.-L. Chi, X. Wang, Y. Jiao, BIM-enabled structural design: impacts and future developments in structural modelling, analysis and optimisation processes, Archives of Computational Methods in Engineering 22 (2015) 135–151, https://doi. org/10.1007/s11831-014-9127-7. [8] L. Barazzetti, F. Banfi, R. Brumana, G. Gusmeroli, M. Previtali, G. Schiantarelli, Cloud-to-BIM-to-FEM: structural simulation with accurate historic BIM from laser scans, Simul. Model. Pract. Theory 57 (2015) 71–87, https://doi.org/10.1016/j. simpat.2015.06.004. [9] I. Kaner, R. Sacks, W. Kassian, T. Quitt, Case studies of BIM adoption for precast concrete design by mid-sized structural engineering firms, Electronic Journal of Information Technology in Construction 13 (2008) 303–323, https://doi.org/10. 1143/APEX.2.011003. [10] A.H. Oti, W. Tizani, F.H. Abanda, A. Jaly-Zada, J.H.M. Tah, Structural sustainability appraisal in BIM, Autom. Constr. 69 (2016) 44–58, https://doi.org/10.1016/j. autcon.2016.05.019.

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