Research article Special Issues

A fuzzy DRBFNN-based information security risk assessment method in improving the efficiency of urban development

  • The rapid development of urban informatization is an important way for cities to achieve a higher pattern, but the accompanying information security problem become a major challenge restricting the efficiency of urban development. Therefore, effective identification and assessment of information security risks has become a key factor to improve the efficiency of urban development. In this paper, an information security risk assessment method based on fuzzy theory and neural network technology is proposed to help identify and solve the information security problem in the development of urban informatization. Combined with the theory of information ecology, this method establishes an improved fuzzy neural network model from four aspects by using fuzzy theory, neural network model and DEMATEL method, and then constructs the information security risk assessment system of smart city. According to this method, this paper analyzed 25 smart cities in China, and provided suggestions and guidance for information security control in the process of urban informatization construction.

    Citation: Li Yang, Kai Zou, Kai Gao, Zhiyi Jiang. A fuzzy DRBFNN-based information security risk assessment method in improving the efficiency of urban development[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 14232-14250. doi: 10.3934/mbe.2022662

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  • The rapid development of urban informatization is an important way for cities to achieve a higher pattern, but the accompanying information security problem become a major challenge restricting the efficiency of urban development. Therefore, effective identification and assessment of information security risks has become a key factor to improve the efficiency of urban development. In this paper, an information security risk assessment method based on fuzzy theory and neural network technology is proposed to help identify and solve the information security problem in the development of urban informatization. Combined with the theory of information ecology, this method establishes an improved fuzzy neural network model from four aspects by using fuzzy theory, neural network model and DEMATEL method, and then constructs the information security risk assessment system of smart city. According to this method, this paper analyzed 25 smart cities in China, and provided suggestions and guidance for information security control in the process of urban informatization construction.



    Structural members are categorized into two regions. The first one is Bernoulli region in which strain distribution for the entire section is linear. The second one is distributed region where the strain is nonlinear which appears to be in deep beams. Reinforced concrete deep beams have a wide variety of applications in structural engineering aspects like in the construction of wall footings, transfer girders, pier foundations, foundation pile caps. Also, the utilization of deep beams in high rise structures for various commercial purposes is increased rapidly due to their ease of convenience and economical efficiency [1]. In olden days, deep beams are designed in a traditional way of experience or empirical formulae [2]. Now a days, the strut and tie method is using for design of deep beams according to various codes. The shear strength estimated by using ACI Code and EURO Code were overestimated than the predicted actual strengths calculated from Zsutty and Russo models [3]. Compressive strength of concrete at the initial days was very low and increases with increase in the age up to 28 days [4,5]. Failure occurs in deep beams with formation of cracks from the stress zone and thereby spreading to pressure zone [6]. The width of deep beam is not important on shear conduct for a/d proportions and diagonal crack dominates the flexure cracks at the initial stage of loading [7].

    Use of Inclined shear reinforcement is not popular due to fabrication difficulty however despite diagonal cracking pattern of shear failure in deep beams, inclined position is effective compared to vertical position [8]. Minimum shear reinforcement must be provided according to the code to resist the shear cracks in deep beams. 0.2% is the appropriate shear reinforcement decided in theoretical analysis [9]. However, the final failure in deep beams occurs due to ultimate deflection with crushing of compression concrete [10]. The increase in web reinforcement increases the loading capacity of deep beams [11].

    The finite element method gives a specific and realistic solutions to determine the nonlinear behavior of reinforced concrete deep beams. So, ABAQUS [12] software was used to predict the behavior of deep beams under 3-point bending configuration. The modeling technique was verified by validating the model prediction with the experimental work. Later, the results were compared with ACI-318-08 [13] recommendations. With the increase in load, the finite element analysis gives good results in the strain distribution of longitudinal reinforcement [14]. It also gives same crack patterns and load displacement response compared to experimental results. The method of introducing strut is more effective with the angle of inclination of more than 30° [15].

    Different codes of practice and several researchers propose different shear strength expressions for RC deep beams, a research gap still exists to minimize the strength ratio (VTESTnVPREn) as scattering near the safety line (VTESTnVPREn=1.0) and to reduce the number of overestimated beams. In order to fill the gap, a refined shear strength equation is proposed by incorporating effect of web reinforcement, beam size, shear span-to-depth ratio and limitations of compressive strength of concrete on shear strength. The accuracy of prediction by the proposed equation is compared with finite element results and the experimental results. Further, two discrepancies of STM such as over conservativeness and negligence of effect of web reinforcement are refined in the proposed equation.

    All the specimens were tested under three-point bending with simply supported condition under the loading frame of capacity 2000 kN. Surface strain gauges were used to calculate the strains and linear variable displacement transducer was used to monitor deflections. Crack width is measured by using micrometer with an accuracy of 0.01 mm. Table 1 shows the percentage of horizontal and vertical shear reinforcement. Keeping the compressive strength constant, horizontal reinforcement is varied from 0.45% to 0.55% and vertical shear reinforcement is taken as 0.4% and 0.6%.

    Table 1.  Cross section and percentage of horizontal and vertical shear reinforcement of deep beams.
    S. No Beam ID L (mm) B (mm) D (mm) l/d a/d ρh% ρv%
    1 1D500 900 150 500 1.8 0.9 0.45 0.4
    2 2D500 900 150 500 1.8 0.9 0.5
    3 3D500 900 150 500 1.8 0.9 0.55
    4 4D500 900 150 500 1.8 0.9 0.45 0.6
    5 5D500 900 150 500 1.8 0.9 0.5
    6 6D500 900 150 500 1.8 0.9 0.55

     | Show Table
    DownLoad: CSV

    The experimental study consists of 6 deep beams of 150 mm width and 500 mm depth for a span of 900 mm with varying percentages of horizontal and shear reinforcement. The first three beams have been flexurally reinforced with three 16 mm diameter bars of HYSD 500 in a single layer and the tension region depth is 90 mm. The last three beams have been flexurally reinforced with four 16 mm diameter bars of HYSD 500 in double layer. Figure 1 shows the reinforcement details of all six deep beam specimens.

    Figure 1.  Cross-section details of deep beam specimens.

    Table 2 shows the values of compressive strength of concrete. All the specimens were cast using M 35 grade concrete to attain a target strength of 43.25 N/mm2. The water–cement ratio is considered as 0.45 confirming to IS 10262-2009. The quantity of cement content and fine agammaegate content obtained is 350.51 and 651.47 kg/m3. A mixture of 10 and 20 mm coarse agammaegates have been used for smooth finishing of specimens and quantity obtained for three sample cubes is 498.3 and 747.45 kg/m3. 16 mm diameter high yield strength deformed bars of grade Fe 500 were used for flexural reinforcement and 8 and 12 mm diameter bars of grade Fe 500 were used for shear reinforcement (both horizontal and vertical). Table 3 determines the yield strength and ultimate strength of bars used for the experimental work. Figure 2 represents the arrangement of deep beam under the loading frame to measure ultimate load and deflection.

    Table 2.  Average compressive strength of concrete.
    S. No Age of specimen Group-I mix load (kN) Group-II mix load (kN) Group-III mix load (kN) Group-IV mix load (kN)
    1 7 days 870 860 880 920
    2 14 days 890 900 910 940
    3 28 days 930 920 930 1040
    Compressive strength (N/mm2) 39.90 39.71 40.29 42.96

     | Show Table
    DownLoad: CSV
    Table 3.  Mechanical properties of steel reinforcement.
    S. No Bar diameter (mm) Area (mm2) Yield strength (MPa) Ultimate strength (MPa)
    1 8 50.24 540.45 631.3
    2 12 113.1 569.8 637.1
    3 16 201 564.3 652.8

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    DownLoad: CSV
    Figure 2.  Loading frame setup.

    Concrete in between the diagonal cracks acts as an inclined strut, and the tension reinforcement acts as a tie. The strut and tie intersect at the node point. The arch action forms only after the diagonal crack, which facilitates in enhancing the shear strength. Moreover, due to arch action, the conventional sectional approaches are not applicable for deep beam design. The internal force transfer mechanism of a deep beam is shown in Figure 3.

    Figure 3.  Strut-and-tie mechanism for RC deep beam.

    Based on the strut-and-tie model approach, the force applied on the strut is V/sin θ. The internal forces resist the horizontal component of the strut force V cot θ. Furthermore, the inclination of the diagonal crack θ is idealized as tan1(0.8da). Using force equilibrium, the following formulation is arrived (Eq 1).

    V cot θ=F sin θ (1)

    The total force F is the sum of contribution of concrete, horizontal and vertical web reinforcement, namely, FC, FH, and FV as shown in Figure 4 (Eq 2).

    V cot θ={Fc+FH+Fv} sin θ (2)
    Figure 4.  Internal forces resisting transverse tension.

    In Figure 4 idealized diagonal splitting crack is shown in dashed lines.

    Bazant proposed size-effect law for beams made of brittle materials in the form of ft=Bfct1+dλda [16], and this size-effect equation is re-written as Eq3.

    ft=Bfct(1+dλda)α1 (3)

    where ft = nominal stress at failure, B and λ = empirical constants, fct = direct tensile strength of concrete, da = maximum size of agammaegate, d = depth of the beam. Eurocode 2 expression for tensile strength of concrete 0.30 f(23)c is replaced by Af(23)c.

    To overcome the compression softening effect, the concrete contribution is formulated by diagonal tension resistance rather than by axial compressive strength of the strut. The diagonal splitting crack is resisted by the tensile strength of concrete and the web reinforcement as shown in Figure 3. The contribution of concrete Fc acting normal to the diagonal (AB) can be determined from the equation given below (Eq 4).

    Fc=ftbz sin θ (4)

    where, lever arm z=jd and (bz sin θ) is the area contributing for diagonal tensile strength. Rao and Sundaresan derived a relation between lever arm and the percentage of tension reinforcement as j=0.62ρ0.08 [17]. The expression is rewritten as j=Cρα2. By substituting ft and z, the following formulation has been proposed (Eq 5).

    Fc=A f(23)c b(Cρα2 d) sin θ(B(1+dλda)α1) (5)

    FH and FV are the internal force components perpendicular to the diagonal AB, which is offered by horizontal and vertical shear reinforcements. These force components are the multiples of total bars and tension force in each bar. Inclination and length of the idealized diagonal crack are given by " θ " and " dsinθ ", and the spacing of bars "Sh" and "Sv" are shown in Figures 5 and 6. The crack lengths are " d " and " dtanθ " and these are multiplied by " 1x1Sh " and " 1y1Sv " for contribution to shear. Based on test data reported in the literature, the constants x1 and y1 have been approximately found to be 2.0.

    Figure 5.  Web reinforcement mechanism.
    Figure 6.  Effective area contributing shear resistance [18].

    In the above equations (Eqs 4 and 5), the number of horizontal and vertical bars is nh=dy1×sh and nv=dx1×s v tan θ. Thus, overestimation of web reinforcement is mitigated. Also, the effective shear span area is accounted for in the proposed equation (Eq 10).

    To obtain the inclined force component, resistance offered by each horizontal and vertical bar is multiplied by sinθ and cosθ (Eqs 6 and 7).

    FH=nhfyhAsh sin θ (6)
    FV=nvfyvAsv cos  θ (7)

    The shear strength expression is obtained by incorporating the values of FC, FH and FV.

    The contribution of web reinforcement and concrete are significantly dominated by a/d ratio. Thus, (a0.8 d) is substituted for  cot θ in Eq 8. The constant D replaces the constants A, B and C.

    V={Df(23)cρα2   cot   θ.   sin   θ   sin   θ   (1(1+dλda)α1)bd+FH   sin   θ+Fv   sin   θ   cot   θ}={Df(23)c ρα2a0.8   d(1(1+dλda)α1)bd+0.5fyh ρsh   sin   θ.   sin   θ+0.5fyv ρsv   cos  θ.   sin   θ   tan   θa0.8   dbd    } (8)

    The a/d ratio of the beams influences the shear strength, crack orientation, size dependence, web reinforcement and concrete contributions for the shear resistance. Effectively addressing the influence of a/d ratio on the shear strength is a challenge. The term D(a0.8 d) is replaced by a consistent formulation 1log10(1+ad). Thus, the generic form of the shear resistance is given in Eq 9.

    V={Dρα2log10(1+ad)f(23)c(1(1+dλda)α1)+0.5fyhρsh( sin θ)2a0.8d+0.5fyvρsvcos  θ× sin θ}bd (9)

    In the Eq 9, various constants to be evaluated from the experimental data include D, α1, α2 and λ.

    From the experimental data, using trial and error procedure, the constants α1, α2 and λ are determined. Thus, a shear strength expression is formulated, which is given below (Eq 10).

    V={f(23)c ρ12log 10(1+ad)(1(1+d350da   )0.75  )+0.4dafyhρsh( sin θ)2+0.25fyvρsv sin 2θ}bd (10)

    Limiting the design expression to Vmax can give rise to conservative results. The limit is kept to resist cracking in concrete within service loads and to withstand against diagonal compression failure (Eq 11).

    Vmax={7 f(13)c  ρ14log  10(1+ad)}bd4/3 (11)

    The various power coefficients in the expression for Vmax are arrived at by plotting Vu/bd versus d, f'c, log10(1+ad) and ρ. The determined power coefficients are f(13)c, ρ14, log10(1+ad)1 and d4/3. The above coefficients are adjusted so that the strength ratios (VnTEST/VnPRO) fall in the range of 1.0 to 2.0.

    Using the ABAQUS program along with codal provisions of ACI 318-14, a statistical study was done to know the shear strength of deep beams. CDP model was used to know its behavior. Figures 7 and 8 represents the concrete compression behavior and tension behavior for concrete. Figure 9 represents the stress strain relationship for reinforcement in ABAQUS model.

    Figure 7.  Concrete compression behavior in ABAQUS Software.
    Figure 8.  Tension behavior for concrete based on stress strain relationship in ABAQUS.
    Figure 9.  Stress strain relationship for reinforcement in FE model.

    Table 4 gives the values of young's modulus, Poisson's ratio, dilation angle, eccentricity for the analysis of deep beams using solid 65 model type in Abaqus.

    Table 4.  Modal parameters for deep beams in Abaqus.
    S. No Modal parameter Value
    1 Youngs modulus (Mpa) 29580.4
    2 Poisson's ratio 0.2
    3 Dilation angle (ψ) 31
    4 Eccentricity (e) 0.1
    5 Second stress invariant (K) 0.67
    6 σbo/σco 1.16
    K = ratio of second stress invariant on tensile meridian to compressive meridian; σbo/σco = ratio of initial equibiaxial compressive yield stress to initial uniaxial compressive yield stress.

     | Show Table
    DownLoad: CSV

    The shear strength of beams has been evaluated using the following codes: IS 456-2000 [19], ACI 318-14 [20] strut-and-tie provision. In addition to that shear strength of beams is evaluated using the expressions suggested by Zsutty, Tang et al., Russo et al. [21,22,23]. Aforesaid equations are briefly named as Zsutty, Tang, Russo whereas the shear force calculated using these equations are VZUTTYn, VTANGn, VRUSSOn. Table 5 represents the existing shear strength expressions by various codes and researchers.

    Table 5.  Existing shear strength expressions.
    Modals Shear strength expression
    Zutty [21] VZUTTYn=((2.5da)2.2(fcρda)1/3+ρvfyv)bd;for a/d2.5
    IS 456 [19] VIS 456n=fy Astz; where z =  {0.2(l+2D) when 1lD20.6 l when lD<1
    Tang et al. [22] Astr = bw(la  cos θs + lb   sin θs); Act=bwzs/sin θs
    zs=hla2 - lc2;   tan θs=hla2lc2a
    Vds = fct Act+fyw Aw   sin (θw+θs)+2fy As   sin θs2cos θs
    Vdc = fc Astr   sin θs
    1VTANGn =  1Vds  + 1Vdc
    Russo et al. [23] VRUSSOn=0.545(kχfc  cos θ+ 0.25ρhfyh   cot θ+0.35adρvfyv) bd
    χ=[0.74(fc105)31.28(fc105)2+0.22(fc105)+0.87]
    ACI 318-14 [20], Strut-and-tie provision VA1=0.80×0.85fc lpb
    VB1=0.85fc lpb
    VAD=Asfys   tan θ
    VA2=0.800.85fc wtb   tan θ
    VB2=  0.85fc dab   tan θ
    VA3=0.800.85fc    sin θ b(lp   sin θ+wt  cos θ)
    VB3=0.85fc    sin θ b(lb   sin θ+da  cos θ)
    VACI 318STMn=min[VA1;VB1;VAD ;VA2;VB2;VA3;VB3 ]
    Proposed equation VPROn={Dρα2log10(1+ad)f(23)c(1(1+dλda)α1)+ 0.5fyh  ρsh(sin θ)2 a0.8 d                 +0.5fyv ρsv   cos θ.   sin θ}bd

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    Zsutty [21] equation and proposed equation predict for small, medium and large size beams uniformly. At the same time, for the deep beams with depth nearly 1800 mm, strut-and-tie models such as Tang [22], ACI-STM [20] predicts in the range of 25% to 60 % of the ultimate strength. Hence, Tang [22] and ACI-STM [20] models are over conservative for the large size beams.

    By introducing the strength reduction factor in ACI 318-14, most of the strength ratios VTESTnVPREn  of high strength concrete beams are higher than 1.0. Also, it is noted that ACI 318-08 VnMax scatters equally above and below the safety line. Thus, mean of shear strength ratio of ACI 318-08 VnMax is equal to 1.00. In order to improve the conservativeness, ACI 318-14 introduced the strength reduction factor in maximum design shear force (0.75), which gives reasonable amount of conservativeness.

    Zsutty [21] equation and proposed equation predicted the beams provided with low and a high percentage of tension reinforcement uniformly. As the percentage of tension reinforcement increases, strength ratios increase in Tang [22], ACI-STM [20]. Beams with a low percentage of tension reinforcement exhibit flexure as well as shear crack. Empirical equations such as ACI 318-14 VnMax are derived only from the shear test results. Due to this reason, these equations overestimate some beams with a low percentage of tension reinforcement. While comparing direct strut-and-tie models with the proposed equation, effect of percentage of tension reinforcement on shear strength is accounted for properly.

    Zsutty [21] equation did not address the influence of vertical web reinforcement effectively. At the same time, the proposed equation predicts very close to the strength ratio line 1.0. As usual, Russo [23] equation predicted the beams with low and high quantity of vertical web reinforcement uniformly. Other strut and tie models Tang [22] and ACI-STM [20] show reasonable scatter. While comparing the proposed equation with all other equations, it is clear that almost all the beams are underestimated.

    The ratio of the predicted shear strength-to-experimentally observed shear strength is defined as the strength ratio. Among all the predictions, mean of the strength ratios according to ACI-STM [20] and proposed equation are close to the safety line 1.0, where the proposed equation has a mean value of 0.94 and 1.26 according to ACI-STM [20]. Moreover, the mean of the strength ratio according to IS 456 [19] has been observed to be the lowest (0.448). The mean values of Zsutty [21] and Tang [22] equations are 0.84.

    The standard deviation (SD) of IS 456 strength ratio is the least among all the predictions according to all the standards. Subsequently, equations such as the proposed equation, ACI 318-STM [19] and Zsutty [21] showed a 12 % deviation from the mean.

    Though ACI 318-14 equation showed very close average value to the safety line, it exhibits the highest coefficient of variation (COV) (2.4%) than all other predictions. Conversely, IS 456 [19] equation predicts the strength ratio with least COV of 0.4%. Equations such as Zsutty [21], Tang [22], Russo [23,24,25], and proposed equation showed almost same COV. From the above analysis, it is observed that irrespective of the size, the proposed equation gives uniform prediction with mean 0.94 and standard deviation 0.14. Since the Indian standard does not consider the effect of size, compressive strength and web reinforcement, strength of all the beams were overestimated. Evaluation of ACI 318-14 [20] strength reduction factor using the experimental results is discussed in the following section.

    Table 6 shows the strength reduction factor of ACI 318-14 [19] expression for maximum design shear strength of RC deep beams has been evaluated. Strength ratios of ACI 318-14 [19] vnACI31814vnTEST is given in Table 6. For all the beams, the ratio vnACI31814vnTEST is greater than 1.0. Thus, strength reduction factor is adequate to consider the shear compression mode of failure. The mean value of ACI 318-14 [20] of strength ratio is 1.26 and standard deviation is 0.15. From the above analysis, it can be concluded that the strength reduction factor is effective when the beams failed in shear compression.

    Table 6.  Shear strength predictions.
    S. No Beam ID vnACI 31814vnTEST vnis 456vnTEST vnZUTTYvnTEST vnTANGvnTEST vnRUSSOvnTEST vnPREvnTEST
    1 1D500 1.33 0.46 0.88 0.87 0.79 0.98
    2 2D500 1.33 0.46 0.88 0.84 0.79 0.98
    3 3D500 1.19 0.41 0.79 0.75 0.70 0.85
    4 4D500 1.04 0.36 0.69 0.66 0.60 0.72
    5 5D500 1.22 0.45 0.83 0.88 0.76 1.00
    6 6D500 1.50 0.55 1.02 1.08 0.90 1.14
    MEAN 1.26 0.448 0.84 0.84 0.75 0.94
    SD 0.156 0.06 0.1 0.14 0.1 0.14
    COV 0.024 0.004 0.01 0.02 0.01 0.02

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    Table 7 gives the comparison of ultimate loads between analytical and experimental results. From Figure 10, it can be seen that FE model gave high prediction of ultimate loads for deep beams. An average of 96.3% was observed between the experimental and FE model results and thus FE model shows a good efficiency in simulating the deep beams.

    Table 7.  Comparison of ultimate loads between analytical and experimental results.
    S. No Specimen Pu Exp (kN) Pu Abaqus (kN) Pu Exp/Pu Abaqus (%)
    1 1D500 611.7 638.7 95.77
    2 2D500 643.4 678.2 94.87
    3 3D500 684.3 712.3 96.07
    4 4D500 611.93 641.9 95.33
    5 5D500 666.1 674.3 98.78
    6 6D500 693.4 712.8 97.28

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    DownLoad: CSV
    Figure 10.  Comparison of ultimate loads of all deep beam specimens.

    Table 8 gives the comparison of ultimate deflection between analytical and experimental results. It can be seen that experimental results gave high prediction of deflection values in comparison with FE model for all the deep beams. An average of 107.8% was observed between these two and FE model gave slightly less prediction in terms of deflection.

    Table 8.  Comparison of ultimate deflection between analytical and experimental results.
    S. No Specimen Pu Exp (mm) Pu Abaqus (mm) Pu Exp/Pu Abaqus (%)
    1 1D500 5.47 5.14 106.42
    2 2D500 6.12 5.86 104.44
    3 3D500 6.82 6.14 111.07
    4 4D500 6.46 6.08 106.25
    5 5D500 6.43 5.91 108.80
    6 6D500 6.62 6.02 109.97

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    To verify the proposed equation, a comparison is done with the load and deflection behavior Figure 11 gives the experimental and analytical comparison of load vs deflection of all deep beam specimens. A close correlation between Fe model predicted results and experimental results have been found. Deep beams 5D 500 and 6D 500 are in close agreement and almost they are similar. In the remaining deep beams, the FE model results have over predicted.

    Figure 11.  Experimental and analytical comparison of load vs deflection of deep beam specimens.

    Three dimensional isoparametric solid elements (SOLID65) were used in the FE model. Figure 12 shows the tensile crack patterns due to tensile stress of the FE model (DAMAGET). The tension cracks started from the position of support and reached to the point of application of load at an average angle of 38°. In the deep beam 3D 500, the maximum tension is found near the top left 1/3rd portion whereas in the deep beam 2D 500 and 6D 500, the maximum tension is found near the top right 1/3rd position. Flexural cracks were also obtained in both experimental and FE model in all the deep beams.

    Figure 12.  Crack pattern of FE damaged tension model.

    The strain distribution is considered very important to know about the tied arch mechanism and its formation in the specimens. Figure 13 gives the strain distribution at the bottom layer of the beam 6D 500 as the load is increasing from 0 to ultimate load. The strain readings increased rapidly at the first crack in the mid span. Since more cracks appeared near the supports, measured strains also increased near the supports whereas in the uncracked region, the strain readings showed minimal strain changes.

    Figure 13.  Strain distribution along the bottom layer in beam 6D 500 as the load is increasing.

    Six reinforced concrete deep beams were analyzed numerically and compared with experimental results and these were analyzed to study the parameters like size effect, compressive strength effect, web reinforcement mechanism, shear span-depth ratio, load deflection behavior, tension damage and strain distribution and the following conclusions were drawn.

    a. The proposed shear strength equation predicts 80% of the experimental data in the range of 66–110% of measured shear strength, whereas other design procedures are over either conservative, which is uneconomical, or overestimating, which is unsafe.

    b. Moreover, the proposed equation is validated with the experimental results.

    c. The predicted shear strength is compared with the experimental shear strength. The calculated nominal shear stress γPREn versus the measured shear stress γTESTn is compared.

    d. It has been observed that prediction by design equations and experimental shear strength are in close relation with each other.

    e. Although ACI 318-08 maximum shear force equation gives a mean value of 1.08, a strength reduction factor is introduced in ACI 318-14. It shows the importance of analysis based on the percentage of overestimation. But in general, STM code equations give conservative results.

    f. A variation of 8% has been found between experimental and FE model results in terms of displacement whereas a variation of 22% has been found in terms of ultimate loads.

    g. The stress contours suggested high stresses in the path of cracks and low stresses in the uncracked regions.

    I would like to acknowledge Dr. P. Poluraju and Dr. C. Khed Veerendra Kumar my research guide for helping in investigating the behavior of deep beams. I am grateful to Koneru Lakshmaiah Education Foundation (KL Deemed to be university) for their continuous support and assistance.

    All authors declare no conflicts of interest in this paper



    [1] Y. Zhong, L. Sun, C. Ge, Key technologies and development status of smart city, J. Phys. Conf. Ser., 1754 (2021), 012102. https://doi.org/10.1088/1742-6596/1754/1/012102 doi: 10.1088/1742-6596/1754/1/012102
    [2] A. I. Tahirkheli, M. Shiraz, B. Hayat, M. Idrees, A. Sajid, R. Ullah, et al., A survey on modern cloud computing security over smart city networks: Threats, vulnerabilities, consequences, countermeasures, and challenges, Electronics, 10 (2021), 1811. https://doi.org/10.3390/electronics10151811 doi: 10.3390/electronics10151811
    [3] K. Dooley, Direct passive participation: aiming for accuracy and citizen safety in the era of big data and the smart city, Smart Cities, 4 (2021), 336–348. https://doi.org/10.3390/smartcities4010020 doi: 10.3390/smartcities4010020
    [4] K. Gokmenoglu, B. M. Eren, S. Hesami, Exchange rates and stock markets in emerging economies: new evidence using the Quantile-on-Quantile approach, Quant. Finance Econ., 5 (2021), 94–110. https://doi.org/10.3934/QFE.2021005 doi: 10.3934/QFE.2021005
    [5] T. Li, J. Zhong, Z. Huang, Potential dependence of financial cycles between emerging and developed countries: Based on ARIMA-GARCH copula model, Emerging Mark. Finance Trade, 56 (2020), 1237–1250. https://doi.org/10.1080/1540496X.2019.1611559 doi: 10.1080/1540496X.2019.1611559
    [6] M. Castells, The Network Society: A Cross-Cultural Perspective, Edward Elgar Publishing, Incorporated, 2004. https://dl.acm.org/doi/abs/10.5555/993619
    [7] J. Zhao, W. Dong, L. Shi, J. Bi, Z. Wang, Y. Liu, et al., Smart city construction and rendering based on virtual city space, in 2020 International Conference on Virtual Reality and Visualization (ICVRV), 2020. https://doi.org/10.1109/ICVRV51359.2020.00066
    [8] M. R. Sanfilippo, Y. Shvartzshnaider, Data and privacy in a quasi-public space: disney world as a smart city, in Diversity, Divergence, Dialogue. iConference 2021. Lecture Notes in Computer Science, 12646 (2021), 235–250. https://doi.org/10.1007/978-3-030-71305-8_19
    [9] X. Li, H. Li, B. Sun, F. Wang, Assessing information security risk for an evolving smart city based on fuzzy and grey FMEA, J. Intell. Fuzzy Syst., 34 (2018), 2491–2501. https://doi.org/10.3233/JIFS-172097 doi: 10.3233/JIFS-172097
    [10] R. Fistola, A. Rastelli, Envisaging urban changes for the smart city: The live city information modeling (LCIM), in Innovation in Urban and Regional Planning. INPUT 2021. Lecture Notes in Civil Engineering, 146 (2021), 161–169. https://doi.org/10.1007/978-3-030-68824-0_17
    [11] T. T. X. Huong, T. T. T. Nga, T. T. K. Oanh, Liquidity risk and bank performance in Southeast Asian countries: a dynamic panel approach, Quant. Finance Econ., 5 (2021), 111–133. https://doi.org/10.3934/QFE.2021006 doi: 10.3934/QFE.2021006
    [12] Z. Li, C. Yang, Z. Huang, How does the fintech sector react to signals from central bank digital currencies, Finance Res. Lett., 50 (2022), 103308. https://doi.org/10.1016/j.frl.2022.103308 doi: 10.1016/j.frl.2022.103308
    [13] A. Aldairi, L. Tawalbeh, Cyber security attacks on smart cities and associated mobile technologies, 109 (2017), 1086–1091. https://doi.org/10.1016/j.procs.2017.05.391
    [14] K. Zhang, J. Ni, K. Yang, X. Liang, J. Ren, X. Shen, Security and privacy in smart city applications: Challenges and solutions, IEEE Commun. Mag., 55 (2017), 122–129. https://doi.org/10.1109/MCOM.2017.1600267CM doi: 10.1109/MCOM.2017.1600267CM
    [15] C. Lim, K. J. Kim, P. P. Maglio, Smart cities with big data: Reference models, challenges, and considerations, Cities, 82 (2018), 86–99. https://doi.org/10.1016/j.cities.2018.04.011 doi: 10.1016/j.cities.2018.04.011
    [16] P. Hui, Construction of information security risk assessment model in smart city, in 2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS), 2020. https://doi.org/10.1109/TOCS50858.2020.9339614
    [17] M. Kalinin, V. Krundyshev, P. Zegzhda, Cybersecurity risk assessment in smart city infrastructures, Machines, 9 (2021), 78. https://doi.org/10.3390/machines9040078 doi: 10.3390/machines9040078
    [18] M. Qamruzzaman, T. Tayachi, A. M. Mehta, M. Ali, Do international capital flows, institutional quality matter for innovation output: the mediating role of economic policy uncertainty, J. Open Innov. Technol. Mark. Complex., 7 (2021), 141. https://doi.org/10.3390/joitmc7020141 doi: 10.3390/joitmc7020141
    [19] T. L. Saaty, K. P. Kearns, The analytic hierarchy process, in Analytical Planning: The Organization of Systems, (1985), 19–62. https://doi.org/10.1016/B978-0-08-032599-6.50008-8
    [20] X. Xing, Smart city evaluation based on analytic hierarchy process, in Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017), Atlantis Press, (2017), 1112–1115. https://doi.org/10.2991/fmsmt-17.2017.219
    [21] P. T. Chang, K. C. Hung, Applying the fuzzy-weighted-average approach to evaluate network security systems, Comput. Math. Appl., 49 (2005), 1797–1814. https://doi.org/10.1016/j.camwa.2004.10.042 doi: 10.1016/j.camwa.2004.10.042
    [22] Y. Gao, J. Luo, Information security risk assessment based on grey relational decision-making algorithm, J. Southeast Univ., 2009. https://doi.org/10.1360/972009-1549 doi: 10.1360/972009-1549
    [23] C. Wagner, H. Hagras, Toward general type-2 fuzzy logic systems based on zSlices, IEEE Trans. Fuzzy Syst., 18 (2010), 637–660. https://doi.org/10.1109/TFUZZ.2010.2045386 doi: 10.1109/TFUZZ.2010.2045386
    [24] J. Huang, L. Dou, H. Fang, J. Chen, Q. Yang, Distributed backstepping-based adaptive fuzzy control of multiple high-order nonlinear dynamics, Nonlinear Dyn., 81 (2015), 63–75. https://doi.org/10.1007/s11071-015-1973-9 doi: 10.1007/s11071-015-1973-9
    [25] C. L. P. Chen, C. Ren, T. Du, Fuzzy observed-based adaptive consensus tracking control for second-order multiagent systems with heterogeneous nonlinear dynamics, IEEE Trans. Fuzzy Syst., 24 (2016), 906–915. https://doi.org/10.1109/TFUZZ.2015.2486817 doi: 10.1109/TFUZZ.2015.2486817
    [26] H. A. Hagras, A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots, IEEE Trans. Fuzzy Syst., 12 (2004), 524–539. https://doi.org/10.1109/TFUZZ.2004.832538 doi: 10.1109/TFUZZ.2004.832538
    [27] F. Liu, An efficient centroid type-reduction strategy for general type-2 fuzzy logic system, Inf. Sci., 178 (2008), 2224–2236. https://doi.org/10.1016/j.ins.2007.11.014 doi: 10.1016/j.ins.2007.11.014
    [28] L. A. Lucas, T. M. Centeno, M. R. Delgado, General type-2 fuzzy inference systems: analysis, design and computational aspects, in 2007 IEEE International Fuzzy Systems Conference, 2007. https://doi.org/10.1109/FUZZY.2007.4295522
    [29] S. Greenfield, R. John, Optimised generalised type-2 join and meet operations, in 2007 IEEE International Fuzzy Systems Conference, (2007), 1–6. https://doi.org/10.1109/FUZZY.2007.4295355
    [30] M. Deveci, D. Pekaslan, F. Canıtez, The assessment of smart city projects using zSlice type-2 fuzzy sets based Interval Agreement Method, Sustainable Cities Soc., 53 (2020). https://doi.org/10.1016/j.scs.2019.101889 doi: 10.1016/j.scs.2019.101889
    [31] H. Zhao, Y. Wang, X. Liu, The assessment of smart city information security risk in China based on zGT2FSs and IAA method, Sci. Rep., 12 (2022). https://doi.org/10.1038/s41598-022-07197-1 doi: 10.1038/s41598-022-07197-1
    [32] M. Alali, A. Almogren, M. M. Hassan, I. A. L. Rassan, M. Z. A. Bhuiyan, Improving risk assessment model of cyber security using fuzzy logic inference system, Comput. Secur., 74 (2017), 323–339. https://doi.org/10.1016/j.cose.2017.09.011 doi: 10.1016/j.cose.2017.09.011
    [33] H. Du, H. Li, L. Yuan, X. Li, Risk assessment model for air traffic control based on fuzzy-ANP method, China Saf. Sci. J., 20 (2010), 79–85.
    [34] C. Wang, G. Lin, The model of network security risk assess based on fuzzy algorithm and hierarchy, J. Wuhan Univ., 52 (2006), 622–626. https://doi.org/10.1360/jos172601 doi: 10.1360/jos172601
    [35] Y. Ou Yang, H. M. Shieh, G. H. Tzeng, A VIKOR technique based on DEMATEL and ANP for information security risk control assessment, Inf. Sci., 232 (2013), 482–500. https://doi.org/10.1016/j.ins.2011.09.012 doi: 10.1016/j.ins.2011.09.012
    [36] A. P. H. D. Gusmo, L. Silva, M. M. Silva, T. Poleto, A. P. C. S. Costa, Information security risk analysis model using fuzzy decision theory, Int. J. Inf. Manage., 36 (2016), 25–34. https://doi.org/10.1016/j.ijinfomgt.2015.09.003 doi: 10.1016/j.ijinfomgt.2015.09.003
    [37] Y. D. Cheng, J. D. He, F. G. Hu, Quantitative risk analysis method of information security-Combining fuzzy comprehensive analysis with information entropy, J. Discrete Math. Sci. Cryptogr., 20 (2017), 149–165. https://doi.org/10.1080/09720529.2016.1178913 doi: 10.1080/09720529.2016.1178913
    [38] Z. Wang, H. Zeng, Study on the risk assessment quantitative method of information security, in 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), 2010. https://doi.org/10.1109/ICACTE.2010.5579187
    [39] W. Liang, Y. Li, K. Xie, D. Zhang, K. Li, A. Souri, et al., Spatial-temporal aware inductive graph neural network for C-ITS data recovery, IEEE Trans. Intell. Transp. Syst., 2022 (2022), 1–12. https://doi.org/10.1109/TITS.2022.3156266 doi: 10.1109/TITS.2022.3156266
    [40] K. Peng, G. Yan, A survey on deep learning for financial risk prediction, Quant. Finance Econ., 5 (2021), 716–737. https://doi.org/10.3934/QFE.2021032 doi: 10.3934/QFE.2021032
    [41] Z. Z. Wang, Y. Q. Xie, X. Y. Wu, F. B. Ge, A survey of information security risk evaluation, Inf. Secur. Commun. Privacy, 2007 (2007).
    [42] Y. Song, Y. Shen, G. Zhang, Y. Hu, The information security risk assessment model based on GA-BP, in 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), IEEE, (2016), 119–122. https://doi.org/10.1109/ICSESS.2016.7883029
    [43] L. Wu, J. Zhou, Z. Li, Applying of GA-BP neural network in the land ecological security evaluation, IAENG Int. J. Comput. Sci., 47 (2020), 11–18.
    [44] W. Guo, Safety risk assessment of tourism management system based on PSO-BP neural network, Comput. Intell. Neurosci., 2021 (2021). https://doi.org/10.1155/2021/1980037 doi: 10.1155/2021/1980037
    [45] R. Deb, S. Roy, A Software Defined Network information security risk assessment based on Pythagorean fuzzy sets, Expert Syst. Appl., 183 (2021), 115383. https://doi.org/10.1016/j.eswa.2021.115383 doi: 10.1016/j.eswa.2021.115383
    [46] X. Huang, W. Xu, Method of information security risk assessment based on improved fuzzy theory of evidence, Int. J. Online Eng., 14 (2018). https://doi.org/10.3991/ijoe.v14i03.8422 doi: 10.3991/ijoe.v14i03.8422
    [47] M. Raikhan, K. Bolat, Z. Meiram, O. Altynay, Assessing information security risk with the fuzzy set theory, J. Theor. Appl. Inf. Technol., 96 (2018), 3142–3152.
    [48] B. Zhang, Z. Wang, W. Wang, Z. Wang, H. Liang, D. Liu, Security assessment of intelligent distribution transformer terminal unit based on RBF-SVM, in 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), 2020. https://doi.org/10.1109/EI250167.2020.9346959
    [49] Q. Liu, P. Sun, X. Fu, J. Zhang, H. Yang, H. Gao, et al., Comparative analysis of BP neural network and RBF neural network in seismic performance evaluation of pier columns, Mech. Syst. Sig. Process., 141 (2020), 106707. https://doi.org/10.1016/j.ymssp.2020.106707 doi: 10.1016/j.ymssp.2020.106707
    [50] R. Li, F. Li, C. Wu, J. Song, Research on vehicle network security situation prediction based on improved CLPSO-RBF, J. Phys. Conf. Ser., 1757 (2021), 012148. https://doi.org/10.1088/1742-6596/1757/1/012148 doi: 10.1088/1742-6596/1757/1/012148
    [51] J. Li, W. Du, F. Yang, G. Hua, The carbon subsidy analysis in remanufacturing closed-loop supply chain, Sustainability, 6 (2014), 3861–3877. https://doi.org/10.3390/su6063861 doi: 10.3390/su6063861
    [52] K. Govindan, D. Kannan, K. M. Shankar, Evaluating the drivers of corporate social responsibility in the mining industry with multi-criteria approach: A multi-stakeholder perspective, J. Cleaner Prod., 84 (2014), 214–232. https://doi.org/10.1016/j.jclepro.2013.12.065 doi: 10.1016/j.jclepro.2013.12.065
    [53] H. Liu, P. Wang, Z. Li, Is there any difference in the impact of digital transformation on the quantity and efficiency of enterprise technological innovation? Taking China's agricultural listed companies as an example, Sustainability, 13 (2021). https://doi.org/10.3390/su132312972 doi: 10.3390/su132312972
    [54] X. Wang, Z. Q. Tang, X. U. Shuo, Information security risk assessment based on fuzzy theory and BRBPNN, Comput. Simul., 36 (2019), 184–189.
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