Computational methods in service life modeling of concrete ... · maintenance or repair. ... Safety of reinforced concrete structures of expected longer service life are threatened

49The Indian Concrete Journal March 2014

POINT OF VIEW

Reinforced concrete (RC) Structures constructed over six decades ago are now approaching the end of their service life and showing serious level of deterioration and therefore, prediction of service life of structural materials and components has gained significance. With the increased efforts of researchers, development in computer knowledge and advancement in building materials science, it is possible to predict the service life of structures. Recently, Computational methods such as finite element analysis method (FEM or FEA), finite difference method (FDM) and artificial neural network (ANN) are becoming popular among researchers for modeling the service life of reinforced concrete structures. This article presents review of service life models developed through these computational tools. This article also evaluates the effect of age and concrete cover over the chloride content at rebar level of structures through ANN.

1. IntroductIon

With the advancements in material science, construction technology and forms of structures, more durable structures are expected to be constructed, but unfortunately it is not so. During recent past, durability and service life aspects of structures are brought into focus. Service life is the actual life during which structures fulfil its performance requirements with no unacceptable expenditure on maintenance or repair. Several methods and tools are available for modeling and simulating service life of RC structures such as FEM, finite difference method (FDM),

artificial neural network (ANN), probability based methods and other mathematical modeling tools. This paper reviews several service life models developed by applying these computational methods. Results of a field survey conducted in the Bhopal, India have been utilised to study the effect of age and concrete cover over the chloride content. One hidden layer multilayer perceptron (MLP) network has been used for conducting this study.

Safety of reinforced concrete structures of expected longer service life are threatened by various factors and repairs are required after few years of exposure, few deteriorated structures are shown in Figures 1 and 2. In addition to the

Computational methods in service life modeling of concrete structures

Sanjeev K. Verma, Sudhir S. Bhadauria and Saleem Akhtar

50 The Indian Concrete Journal March 2014

POINT OF VIEW POINT OF VIEW

flaws in building standards, design and unsatisfactory construction, the direct causes are

(a) Corrosion induced cracking

(b) Carbonation

(c) Chloride attack

(d) Sulphate attack

(e) Freeze thaw attack

(f) Alkali Silica Reactivity (ASR)

To predict service life of reinforced concrete (RC) structures with the help of various deterministic empirical models or experimental methods are being used. Service life modeling of RC structures involves analyses and prediction of the performance of structure before the deterioration. Service life models may be defined as “procedure which is able to evaluate the desired attributes of a structure to satisfy the need of the user and means required for achieving the goal”. Various stages of development of service life models are presented by flow chart in Figure 3.

Models for predicting service life of RC structures are classified by many authors, Folic and Zenunovic classified service life models in three main categories [1] as shown in Figure 4.


POINT OF VIEW

Empirical models are based on previously observed relationships of service life, concrete composition and environmental conditions without understanding scientific reasons, for this relationship such as neural network models. Mechanistic models provide prediction of service life based on mathematical descriptions of the phenomenon involved in concrete degradation, such as understanding microstructure of concrete before and during degradation. Semi empirical models tend to use more simple mathematical expressions then mechanistic, predictions are made by fitting parameters based on data from field and laboratory tests and analysis.

Khatri and Sirivivatnanon & Abu-Tair et al. described five approaches to predict the service life of construction materials [2,3]. These approaches are (1) to estimate life of a structure based on experience (2) deductions from

the performance of similar materials (3) based on results of accelerated condition test (4) mathematical modeling based on chemistry and physics of degradation process and (5) application of probability concepts.

Usually researchers considered corrosion initiation and propagation as two significant phases of service life. However, it has been observed that service life of a structure has three major phases – (1) time after construction and before corrosion initiation (2) time between corrosion initiation and crack formation (3) time period after crack formation before failure of structure as shown in Figure 5.

Figure 5 presents the life cycle of a reinforced concrete structure. Initially after the construction even in corrosion free environment, concrete structures deteriorate with increase in age at a constant rate in the first phase of



the service life. When chloride ion more than threshold value penetrates into concrete or when pH of pore solution reduces below 9 due to carbonation corrosion initiates and the first phase of service life ends. During the second phase of service life after the corrosion initiation, deterioration rate of the structure increases and performance degradation in RC structures is faster than first phase. During the second phase of service life increase in volume of corrosion products results in the formation of cracks on the concrete surface. Formation of cracks and increase in crack width with time speed up ingress of harmful ions and, thus, further increase the deterioration rate in third phase of service life. This increased deterioration rate leads to the early failure of RC structures with the end of third phase of the service life. Figure 5 also indicates loss in service life due to corrosion initiation and crack formation.

2. SerVIce lIfe modelS deVeloped uSIng computAtIonAl methodS

Recently, there has been a growing interest in application of computational methods such as FEM, FDM and ANN in field of civil engineering. FEM is applied by several researchers to simulate diffusivity of harmful agents in concrete and solving theoretical equations describing the deterioration of concrete.

Pan and Wang Presented a FE based model developed to evaluate the service life of RC Structures in three Key steps - chemical ingress, steel corrosion and concrete cracking[4]. Chemical ingress has been modeled using principle of mass conservation. Steel corrosion and increased diametric expansion are formulated using Faraday’s law and concrete cracking is simulated using a cohesive fracture approach. Developed FE model is validated using laboratory results. In this study, three phases of service life for concrete structure has been considered instead of two phases which is usually adopted by researchers. Developed model can be considered as reliable as it is validated through laboratory results.

A 2-D FE coupled model has been developed by Cheung et al., to evaluate the chloride penetration process in varying environment to predict the corrosion initiation time [5]. Corrosion initiation time is controlled by the speed of chloride transfer and depassivation process within

the structure. Variation of environmental conditions on the surface of RC had very significant impact on the corrosion process. Therefore, microclimatic variation on the concrete surface has been investigated in this research. The corrosion performance model is formulated considering change in environmental conditions and simulates the coupled diffusion process and corrosion performance. A set of realistic environmental conditions is proposed based on exposure conditions and material properties. Surface chloride concentration shows a quasi-linear increase with the nth root of time and this increase is relatively fast and reaches a quasi-constant content in about five years time, as in eqn. (1)

Cs (t) = Cs_ref. tn t < 5 years

Cs_ref. 5n t > 5 years ...(1)

where Cs_ref = chloride content (% wt. of concrete), n = empirical coefficient and in most cases is usually proposed as 0.5. The parametric analysis results suggest that the corrosion initiation time in tropical/ subtropical regions depends mainly on the annual mean relative humidity (h), the source chloride Concentration (C), concrete cover depth (d) and w/c ratio. The proposed empirical equation is presented in eqn. (2)

6

phases which is usually adopted by researchers. Developed model can be considered as reliable

as it is validated through laboratory results.

A 2D FE coupled model has been developed by Cheung et al., to evaluate the chloride

penetration process in varying environment to predict the corrosion initiation time5. Corrosion

initiation time is controlled by the speed of chloride transfer and depassivation process within the

structure. Variation of environmental conditions on the surface of RC had very significant impact

on the corrosion process. Therefore, microclimatic variation on the concrete surface has been

investigated in this research. The corrosion performance model is formulated considering change

in environmental conditions and simulates the coupled diffusion process and corrosion

performance. A set of realistic environmental conditions is proposed based on exposure

conditions and material properties. Surface chloride concentration shows a quasilinear increase

with the nth root of time and this increase is relatively fast and reaches a quasiconstant content

in about five years time, as in eqn. (1)

Cs (t) = Cs_ref. tn t < 5 years

Cs_ref. 5n t > 5 years eqn. (1)

where Cs_ref = chloride content (% wt. of concrete), n=empirical coefficient and in most cases is

usually proposed as 0.5. The parametric analysis results suggest that the corrosion initiation time

in tropical/ subtropical regions depends mainly on the annual mean relative humidity (h), the

source chloride Concentration (C), concrete cover depth (d) and w/c ratio. The proposed

empirical equation is presented in eqn. (2)

= ℎ

.C. eqn. (2)

where k, a, b, c= adjustment factor for the w/c ratio and weighing factor respectively. It is an

interesting and useful study, as it considered variation in environmental conditions. This study

coupled diffusion process and corrosion to evaluate chloride penetration for providing more

realistic results.

Dao et al. presented an algorithm to develop different types of models for steel

corrosion6. An adaptive FEM which effectively overcomes most of the major limitations of

...(2)

where k, a, b, c = adjustment factor for the w/c ratio and weighing factor respectively. It is an interesting and useful study, as it considered variation in environmental conditions. This study coupled diffusion process and corrosion to evaluate chloride penetration for providing more realistic results.

Dao et al. presented an algorithm to develop different types of models for steel corrosion [6]. An adaptive FEM which effectively overcomes most of the major limitations of available models has been incorporated in the proposed model. Modeling steel corrosion in concrete structures includes solving governing equations in Laplace form. This satisfies the boundary conditions of potential and current density at the steel concrete interface.

7

available models has been incorporated in the proposed model. Modeling steel corrosion in

concrete structures includes solving governing equations in Laplace form. This satisfies the

boundary conditions of potential and current density at the steel concrete interface.

∇∅ = ∅ +

∅=0 eqn. (3),

Where Ø= electrical potential and∇ = Laplacien operator. This study presents a unified and

simple approach for modeling the corrosion of steel bars in reinforced concrete structures and it

this overcomes limitations of previous models.

Governing equations of heat, moisture and chloride transport into non saturated concrete

are described and solved numerically through FEA by Lin et al.7. Systematic and robust model is

also developed for predicting service life of RC structures considering environmental humidity,

temperature fluctuations, chloride binding & diffusion and decay of structural performance.

These numerical models are demonstrated through predicting service life of chloride exposed

slabs. It has been observed that 55mm of concrete cover is required for assuring the initiation

time of reinforcement corrosion beyond 50 years. This model considered environmental and

temperature effects with chemical ingress, therefore, more reliable results are expected.

Okasha and Frangopol presented a computational methodology for predicting life cycle

and estimating the service life of bridges through latest modeling tools8. This methodology

considered techniques such as incremental nonlinear FEA, quadratic response surface, modeling

using design of experiments concept and Latin hypercube sampling. Recently, use of incremental

nonlinear FEA (INLFEA) in computation has been emerged due to the advances in the FEA field

and rapid increase in the speed and power of computers. In INLFEA, the specified loading has

been applied incrementally until a failure mode occurs and the load at which the failure mode

occurred has been defined as the resistance of the structural system. This study presents several

latest modeling tools with more emphasis over FEA technique. This study can be utilized by

researchers to estimate service life of structures through computational methods.

DuraPGulf a service life design model has been presented by Shekarchi et al., to predict

corrosion initiation for RC structures in the south of Iran9. It is a semiempirical model, based on

Fick’s’ law, for determining chloride diffusion process. This Model is developed using the FE

technique and user friendly software is developed for practical engineering applications. Service

= 0 ... (3)


POINT OF VIEW

where Ø = electrical potential and

7

available models has been incorporated in the proposed model. Modeling steel corrosion in

concrete structures includes solving governing equations in Laplace form. This satisfies the

boundary conditions of potential and current density at the steel concrete interface.

∇∅ = ∅ +

∅=0 eqn. (3),

Where Ø= electrical potential and∇ = Laplacien operator. This study presents a unified and

simple approach for modeling the corrosion of steel bars in reinforced concrete structures and it

this overcomes limitations of previous models.

Governing equations of heat, moisture and chloride transport into non saturated concrete

are described and solved numerically through FEA by Lin et al.7. Systematic and robust model is

also developed for predicting service life of RC structures considering environmental humidity,

temperature fluctuations, chloride binding & diffusion and decay of structural performance.

These numerical models are demonstrated through predicting service life of chloride exposed

slabs. It has been observed that 55mm of concrete cover is required for assuring the initiation

time of reinforcement corrosion beyond 50 years. This model considered environmental and

temperature effects with chemical ingress, therefore, more reliable results are expected.

Okasha and Frangopol presented a computational methodology for predicting life cycle

and estimating the service life of bridges through latest modeling tools8. This methodology

considered techniques such as incremental nonlinear FEA, quadratic response surface, modeling

using design of experiments concept and Latin hypercube sampling. Recently, use of incremental

nonlinear FEA (INLFEA) in computation has been emerged due to the advances in the FEA field

and rapid increase in the speed and power of computers. In INLFEA, the specified loading has

been applied incrementally until a failure mode occurs and the load at which the failure mode

occurred has been defined as the resistance of the structural system. This study presents several

latest modeling tools with more emphasis over FEA technique. This study can be utilized by

researchers to estimate service life of structures through computational methods.

DuraPGulf a service life design model has been presented by Shekarchi et al., to predict

corrosion initiation for RC structures in the south of Iran9. It is a semiempirical model, based on

Fick’s’ law, for determining chloride diffusion process. This Model is developed using the FE

technique and user friendly software is developed for practical engineering applications. Service

= Laplacien operator. This study presents a unified and simple approach for modeling the corrosion of steel bars in reinforced concrete structures and it overcomes limitations of previous models.

Governing equations of heat, moisture and chloride transport into non saturated concrete are described and solved numerically through FEA by Lin et al. [7]. Systematic and robust model is also developed for predicting service life of RC structures considering – environmental humidity, temperature fluctuations, chloride binding & diffusion and decay of structural performance. These numerical models are demonstrated through predicting service life of chloride exposed slabs. It has been observed that 55 mm of concrete cover is required for assuring the initiation time of reinforcement corrosion beyond 50 years. This model considered environmental and temperature effects with chemical ingress, therefore, more reliable results are expected.

Okasha and Frangopol presented a computational methodology for predicting life cycle and estimating the service life of bridges through latest modeling tools [8]. This methodology considered techniques such as incremental nonlinear FEA, quadratic response surface, modeling using design of experiments concept and Latin hypercube sampling. Recently, use of incremental nonlinear FEA (INL-FEA) in computation has been emerged due to the advances in the FEA field and rapid increase in the speed and power of computers. In INL-FEA, the specified loading has been applied incrementally until a failure mode occurs and the load at which the failure mode occurred has been defined as the resistance of the structural system. This study presents several latest modeling tools with more emphasis over FEA technique. This study can be utilised by researchers to estimate service life of structures through computational methods.

DuraPGulf a service life design model has been presented by Shekarchi et al., to predict corrosion initiation for RC structures in the south of Iran [9]. It is a semi-empirical model, based on Fick’s’ law, for determining chloride diffusion process. This Model is developed using the FE technique and user friendly software is developed for practical engineering applications. Service life of a

RC structure is mainly governed by local surrounding environmental conditions, therefore, it is a useful study to develop a model for south of Iran region.

Song et al. predicted service life of RC structures through micromechanics based corrosion model [10]. They divided service life in four parts corrosion initiation period (ti), corrosion propagation period (tp), corrosion acceleration period (ta) and deterioration period (td) as shown in Figure 6. This model consists of three models – chloride penetration, electric corrosion cell model and oxygen diffusion model, to evaluate the rate of corrosion and accumulation of corrosion products. In these models a corrosion cracking model has been combined to evaluate critical amount of corrosion product required for initiating cracking in concrete cover. All these models are implemented in FEA program for corrosion analysis and predicting the service life of RC structures and the results are compared with test results. It has been observed that increase in cover thickness reduces the corrosion initiation time. For analysis of chloride transport, a governing equation of mass transfer has been used. Effect of temperature, aggregate and humidity on the chloride diffusion coefficient can be considered using Nernst-Planck’s equation and Debye-Huckel’s theory. It has been found that the service life of RC structures decreases with increasing crack width, increasing w/c ratio and decreasing pH of pore water. This model comprises of several models in combination with corrosion cracking model to provide better results. It is a better technique to combine different approaches for obtaining more realistic results.



Song et al. developed an analytical technique for predicting carbonation in early aged concrete [11]. Considered diffusion of carbon dioxide through pore water in sound and cracked concrete. Characteristics of diffusivity on the carbonation in early aged concrete are studied through FEA. Numerical results obtained for cracked concrete made with three different W/C ratios (45%, 55% and 65%) with different crack widths are compared with experimental results. Results obtained through finite element analysis of carbonated concrete are comparable with experimental results.

Coronelli and Gambarova used non linear FEA and developed a suitable numerical procedure for studying structural behavior of RC beams subjected to corrosion [12]. Effects of corrosion are modeled and validated by comparing with available test data. They considered several aspects such as reduction in steel area, changes in the ductility of carbon-steel bars, concrete area reduction because of cover cracking and spalling, changes in the strength and ductility of concrete in compression and changes in tension stiffening because of cover cracking. This study considered almost all aspects of corrosion for modeling corrosion process and results are validated with available test results.

Benin et al. analysed cracking process of concrete induced by corrosion swelling of rebars numerically and experimentally [13]. Based on this analysis, a methodology for corrosion state monitoring, by considering crack opening width has been proposed. A 2-D FEA of concrete cracking under reinforcement corrosion had been conducted. In this study crack width produced due to swelling of concrete is considered for analyzing corrosion process. It is required to analyze this process experimentally and numerically for validation of observations.

Strauss et al. performed reliability assessment of RC structures and demonstrated it on real existing bridge structures [14]. Reliability index of structure decreases during its life cycle due to material degradation. This procedure is modeled by advanced life cycle computer simulation. Analytical deterioration models combined with in-situ monitoring are used to define the degradation process. Main feature of the presented approach is the nonlinear FE analysis of the structure. With the increase

in age of the structure, deterioration of performance and condition increases. This deterioration with age is analyzed in this study through nonlinear finite element analysis of structures.

Martin-Perez and Lounis presented an approach for predicting service life of RC structures exposed to chloride environment that combines a FE modeling of the chloride transport and a reliability based analysis for detecting damage and its accumulation [15]. Through Monte Carlo simulation, probabilistic distributions of the chloride penetration front and corrosion initiation time are generated and it combines FE model with reliability analysis for obtaining more reliable prediction of structures exposed to chloride environment.

Multispecies model has been used by Yuan et al. to describe the chloride transport in saturated concrete [16]. This model has been solved using FDM by using parameters such as porosity, density, chemical composition of pore solution, diffusion coefficient and chloride binding isotherm. Extended Nernst-Planck equation is used to describe the movement of multispecies as shown in eqn. (4). Diffusion coefficient used in this model was depth dependent instead of fixed.

10

Monte Carlo simulation, probabilistic distributions of the chloride penetration front and corrosion

initiation time are generated. Combines FE model with reliability analysis for obtaining more

reliable prediction of structures exposed to chloride environment.

Multispecies model has been used by Yuan et al. to describe the chloride transport in

saturated concrete16. This model has been solved using FDM by using parameters such as

porosity, density, chemical composition of pore solution, diffusion coefficient and chloride

binding isotherm. Extended NernstPlanck equation used to describe the movement of

multispecies as shown in eqn. (3). Diffusion coefficient used in this model was depth dependent

instead of fixed.

= −[ + +

, − ] eqn. (3)

Where D= effective diffusion coefficient; i=ionic concentration in pore solution; γ= chemical

activity coefficient; E= electric potential; F=Faraday constant; R= universal gas constant;

z=valence. This is a useful study as it considered all the parameters involved in chloride

diffusion process.

Alipour et al. studied the life cycle performance and cost of reinforced concrete highway

bridges subjected to earthquake ground motions while they are continuously exposed to the

attack of chloride ions17. Penetration of chloride ion into concrete is simulated through FDM that

takes into account all the parameters that can affect the corrosion process. From the results, the

corrosion initiation time is predicted and the extent of structural degradation is calculated over

the entire life of the bridge. It has been found that by increasing the time, during which bridge is

exposed to aggressive conditions, deterioration process of reinforcing bars can become relatively

fast. This study has been presented over the life cycle performance of reinforced concrete

structures. For simulating chloride ion penetration considered all the parameters influencing the

corrosion of steel bars, which is the best way to simulate realistically.

Puatatsananon and Saouma developed a 2D coupled model, based on the reduction of

concrete porosity due to calcium carbonate, using nonlinear Finite Difference program18. This

model analyzes the deterioration of RC caused by chloride and carbonation, considering the

effect of temperature and relative pore humidity. Carbonation results in formation of calcium

carbonate and release of water and heat, which affects diffusivity of concrete. In this study a

... (4)

where D = effective diffusion coefficient; i = ionic concentration in pore solution; γ = chemical activity coefficient; E = electric potential; F = Faraday constant; R = universal gas constant; z = valence. This is a useful study as it considered all the parameters involved in chloride diffusion process.

Alipour et al. studied the life cycle performance and cost of reinforced concrete highway bridges subjected to earthquake ground motions while they are continuously exposed to the attack of chloride ions [17]. Penetration of chloride ion into concrete is simulated through FDM that takes into account all the parameters that can affect the corrosion process. From the results, the corrosion initiation time is predicted and the extent of structural degradation is calculated over the entire life of the bridge. It has been found that by increasing the time, during which bridge is exposed to aggressive conditions, deterioration process of reinforcing bars can become relatively fast. This study has been presented over the


POINT OF VIEW

life cycle performance of reinforced concrete structures. It considered all parameters influencing corrosion of steel bars for simulating chloride ion penetration.

Puatatsananon and Saouma developed a 2-D coupled model, based on the reduction of concrete porosity due to calcium carbonate, using nonlinear Finite Difference program [18]. This model analyzes the deterioration of RC caused by chloride and carbonation, considering the effect of temperature and relative pore humidity. Carbonation results in formation of calcium carbonate and release of water and heat, which affects diffusivity of concrete. In this study a coupled equation governing moisture, heat and carbon dioxide flow through concrete has been proposed. Porosity and temperature considered in this study are important parameters governing the diffusion of harmful ions in the concrete.

Neural network analysis is an analytical technique that can be applied to complex problems described by a large amount of data. It does not require knowledge of processes involved, however, it identifies the relationships among a set of data. Hence, it may be applied where other mathematical tools are not applicable. Huang developed an ANN model to predict deterioration of RC bridges based on data from previous maintenance and inspection [19]. MATLAB programs has been used and developed to construct the ANN prediction model. Deterioration of bridges is a complex problem, as it includes several parameters. Therefore, use of ANN provides more reliable result, which has been applied in this study.

Song and Kwon proposed a numerical technique for chloride diffusion in High performance concrete with a Neural Network algorithm [20]. Electrically driven chloride penetration tests for diffusion coefficient are performed for the concretes with various parameters such as w/c ratio and various mineral admixtures. Chloride diffusion is a major cause of deterioration of RC structures even in high performance concrete.

Elhag and Wang presented an application of ANN in bridge risk assessment [21]. Back- propagation Neural Networks are developed to model bridge risks score and categories. Model is compared with multiple regression technique and it has been found that hybrid models

combining ANN and regression technique produces better accuracies than any of the individually developed models. Developed model is comparable with results of multiple regression techniques.

Above literature review reveals that most of researchers considered chloride ingress and carbonation of concrete as major factors influencing the service life of structures. Researchers considered these parameters for evaluating performance degradation and estimating the service life of existing RC structures. Application of computational tools in these models increases the accuracy and reliability of results.

Variations in parameter and mechanism influencing durability and service life of RC structures are complex and uncertain. Therefore, modeling of these mechanisms required high speed computational tools such as FEM or ANN.

3. eVAluAtIng effect of Age And concrete coVer on chlorIde content through Ann

In this study effect of age and concrete cover over the chloride content (in percentage wt. of concrete) at rebar level has been evaluated through ANN. Results of a field survey conducted in the Bhopal, India have been utilised for this study. Structures selected for this study are of age 10 to 62 years. In field survey, Cover-meter and Rapid chloride tests have been performed to evaluate concrete cover and chloride content of the structures.

One hidden layer multilayer perceptron (MLP) has been used for this evaluation. The purpose of the model is to capture the relationship between age, concrete cover and corresponding chloride content of the RC structures. The outputs produced by the model have been compared with the target outputs, which are the chloride content values evaluated during field survey. Generalisation ability of developed network is measured by the mean square error (MSE). This procedure is repeated for each training example in the training set; a cycle (an epoch) represents one pass over the whole training set; multiple epochs are required until a satisfactory data mapping is achieved.



Total data set is divided into training set (60%), validation set (20%), and test set (20%). The Training set is a part of input dataset used for neural network training, i.e. for adjustment of network weights. Validation set is a part of data used to tune network topology or network parameters other than weights. For example, it is used to define the number of hidden units to detect the moment when the neural network performance started to deteriorate. Here, validation set is used to calculate generalisation loss and retain the best network (the network with the lowest error

on Validation set). Test set is used only to test how well the neural network will perform on new data. Test set is used after the network is ready (trained), to test what errors will occur during future network application. This set is not used during training and thus can be considered as consisting of new data entered by the user for the neural network application.

In the present study, age (years) and concrete cover (mm) of columns are tagged as input columns and chloride content (% wt. of concrete) is tagged as target column. Training is conducted over training set with 1000 epoch in 03 runs. Network obtained is tested for all the three data sets i.e. training, testing and validation and a good correlation between outputs obtained and target values have been obtained.

Figure 7 presents the Mean square error (MSE) for 1000 epochs in all the three runs during training. It has been observed that MSE decreased after each run.

Figures 8, 9 and 10 represent the comparison of target values and output obtained for training, testing and validation data.


POINT OF VIEW

Table 1 provides the results of all the three testing using training, testing and validation data considering mean square error (MSE), maximum and minimum absolute error and value of ‘r’ correlation coefficient. Value of ‘r’ indicates similarity between two data series, it value ranges from -1 to +1. A larger value of ‘r’ indicates better correlation. It has been observed from table 1, that value of ‘r’ between output from model and target value, for all the three sets of data is reaching ‘1’ which indicates a good correlation.

Through applying sensitivity testing of the training data, individual effect of age and concrete cover over the chloride content has been evaluated. Figure 11, presents the effect of both the parameters over the chloride content and it has been observed that, age is having more significant effect over chloride content then concrete cover. Figure 12 and 13, indicates that chloride content increases with the increase in age of structure and decreases with the increase in concrete cover.

Table 1. Results of model testing

Parameter Training set Testing set Validation set

MSE 0.00125309 0.000649391 0.001079533

Min. Abs. error 0.000530366 0.006760582 0.004525884

Max. Abs. error 0.111148179 0.063028686 0.054728944

r 0.918181158 0.987237727 0.965779663



4. dIScuSSIon And concluSIon

Modeling is a powerful tool to provide understanding of significant processes and their interactions that define service life of RC structures. In the last century predicting the service life of buildings materials and components was only a distant vision. However, today it is possible to incorporate the predictions of service lives into the design process. It is due to effort of researchers, development in computer knowledge and advancement in building materials science. Various models for predicting the service life of structures exposed to harsh environmental conditions have been developed.

Computational methods for modeling service life, performance and deterioration mechanisms of concrete structures have been developed and detected due to advancement in software technologies and rapid increase in speed and power of computers. Researchers have applied methods such as FEM, FDM and ANN for solving governing equations defining movement of various harmful ions casing deterioration of structures. However, expertise is needed for the development of computational model.

Neural Network tools for data processing in the field of durability are very efficient as compared with simple regression method obtained from experimental data. This is, however, particularly useful when values of variables are difficult to control, as is the case for many of the variables affecting concrete degradation.

Application of FEM for modeling service life, performance of RC structures and deterioration mechanisms is becoming popular among researchers as significantly faster simulation is possible from these computer aided models. Results are more accurate as compared to other conventional methods. FEM can handle complicated geometries and boundaries easily, but it requires high computation cost.

A network has been developed using one hidden layer multilayer perceptron for evaluating the effect of age and concrete cover over the chloride content at rebar level in RC structures. It has been observed from this study that age of structures influences chloride content more than the influence of concrete cover. Chloride content increases with the increase in age of structure and with the decrease in concrete cover.

ReferencesFolic, R. and Zenunovic, D., Durability design of concrete structures –part 2: modeling and structural assessment, Arch. And Civil Eng., 2010, 8(1), pp.45-66

Khatri, R.P. and Sirivivatnanon, V., Characteristics service life for concrete exposed to marine environment, Cem. Conc. Res., 2004, 34, pp.745-752.

Abu-Tair, A., McParland, C., Lyness, J.F. and Nadjai, A., Predictive models of deterioration rates of concrete bridges using the factor method based on historic inspection data, 9DBMC, 2002, pap. 262, pp.1-9

Pan, T. and Wang, L., Finite – element analysis of chemical transport and reinforcement corrosion-induced cracking in variably saturated heterogeneous concrete, J. Eng. Mech., 2011, 137(5), pp.334-345

Cheung, M.M.S., Zhao, J. and Chan, Y.B., Service life prediction of RC bridge structures exposed to chloride environments, J. Brid. Eng., 2009, 14(3), pp.164-178

Dao, L.T.N., Dao, V.T.N., Kim, S.K. and Ann, K.Y., Modeling steel corrosion in concrete structures - part 2: A unified adaptive finite element model for simulation of steel corrosion, Int. J. Elec. Sci., 2010, 5(3), pp.314-326, DOI :view/UQ:204299

1.

2.

3.

4.

5.

6.


POINT OF VIEW

Lin, G., Liu, Y. and Xiang, Z., Numerical modeling for predicting service life of reinforced concrete structures exposed to chloride environments, Cem. Conc. Comp., 2010, 32, pp.571-579

Okasha, N.M. and Frangopol, D.M. (2010), Advanced modeling for efficient computation of life cycle performance prediction and service life estimation of bridges, J. Comp. Civil Eng., 2010, 24(6), pp.548-556

Shekarchi, M., Ghods, P., Alizadeh, R., Chini, M. and Hoseini, M., Durapgulf a local service model for the durability of concrete structures in the south of Iran, The Arabian Journal for Science and Engineering, 2008, 33(1B), pp.77-88

Song, H., Kim, H., saraswathy, V. and Kim, T., A micro-mechanics based corrosion model for predicting the service life of reinforced concrete structures, Int. J. Electrochem. Sci., 2007, 2, pp.341-354

Song, H., Kwon, S., Byun, K. and Park, C., Predicting carbonation in early-aged cracked concrete, Cem. Con. Res., 2006, 36, pp.379-389

Coronelli, D. and Gambarova, P., Structural assessment of corroded reinforced concrete beams: Modeling guidelines, J. Struc. Eng., 2004, 130(8), 1214-1224

13. Benin, A.V., Semenov, A.S. and Semenov, S.G., Modelling of fracture process in concrete reinforced structures under steel corrosion, J. Ach. Mat. Man. Eng., 2010, 39(2), pp.168-175

7.

8.

9.

10.

11.

12.

13.

Strauss, A., Bergmeister, K., Hoffman, S., Pukl, R., and Novak, D., Advanced life-cycle analysis of existing concrete bridges, J. Mat. Civil Eng., 2008, 20(1), pp. 9-19

Martin-Perez,B., and Lounis, Z., Numerical modeling of service life of reinforced concrete structures, Pro. Of 2nd Int. Rilem w/s on Life Pred. Ag. Mang., 2003, 5-6 May, Paris, France, pp. 71-79

Yuan, Q., Shi, C., Schutter, G.D., Deng, D., and Fuqiang, H., Numerical models for chloride penetration into saturated concrete. J. Mat. Civil Eng., 2011, 23(3), pp. 305-311

Alipour, A., Shafei, B., and Shinozuka, M., Performance evaluation of deteriorating highway bridges located in high seismic areas. J. Brid, Eng., 2011, 16(5), pp. 597-611

Puatatsananon, W., and Saouma, V.E., Nonlinear coupling of carbonation and chloride diffusion in concrete, J. Infra. Sys., 2005, 17(3), pp. 264-275

Huang, Y., Artificial neural network model of bridge deterioration. J. Per. Constr, Fac., 2010, 24(6), pp. 597-602

Song, H., and Kwon, S., Evaluation of chloride penetration in high performance concrete using neural network algorithm and micro pore structure, Cem. Con. Res., 2009, 39, pp. 814-824

Elhag, T.M.S., and Wang, Y., Risk management for bridge maintenance projects: neural network versus regression techniques. J. Comp. Civil Eng., 2007, 21(6), pp. 402-409

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16.

17.

18.

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Sanjeev K. Verma holds an M.Tech. in civil engineering and pursuing his PhD at the department of civil engineering, University Institute of Technology, Rajeev Gandhi Technological University Bhopal (M.P.). His areas of research interest are non destructive testing of concrete structures, durability studies, in-situ performance assessment, residual service life evaluation.

Dr. Sudhir S. Bhadauria holds a PhD in Civil engineering and is the Director of Shri Govindram Sekseria Institute of Technology and Science, Indore (M.P). He has been a Professor of civil engineering at Rajeev Gandhi Technological University, Bhopal (M.P.). His areas of research interest are durability studies, in-situ performance assessment, residual service life evaluation and durability design of concrete structures, life cycle costing of infrastructure projects and related fields.

Dr. Saleem Akhtar holds a PhD in civil engineering and is the Head of the Department of Civil engineering at University Institute of Technology, Rajeev Gandhi Technological University Bhopal (M.P.). His areas of research interest are non destructive testing of concrete structures, durability studies, in-situ performance assessment, residual service life evaluation, pre-stressed concrete and related fields.

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Computational methods in service life modeling of concrete ... · maintenance or repair. ... Safety of reinforced concrete structures of expected longer service life are threatened