*DOI: http://dx.doi.org/10.5772/intechopen.104905 Introduction to Monitoring of Bridge Infrastructure Using Soft Computing Techniques*

In [36], an SVM model was carried out using four different kernel functions including the Gaussian radial basis function (RBF), Polynomial, Sigmoid, and Linear kernel functions. Experimental modal analysis of a bridge structure was performed to generate the modal parameters as the input database for the model creation. A number of damage cases were conducted to predict the damage severity. As shown in **Figure 17**, amongst all patterns, SVM-Polynomial achieved the most accurate predicted outputs. To offer an explanation, kernel functions were used in order to bring the data from a lower dimension to a higher dimension. To this end, SVM classifier divided the data with a new plane, i.e., hyperplane. Therefore, despite the better learning power in RBF kernel amongst others, this local function could not provide a satisfying dissemination efficiency. Instead, the polynomial kernel, which is a global function, performed a superior data dissemination strategy. Nonetheless, the learning process of polynomial function experienced a lower level of learning capacity.

A finite element model reduction methodology using a Bayesian inference was developed in [53] for structural bolted-connection damage detection. A novel likelihood-free Bayesian inference method for structural parameter identification has also been proposed in [54]. To aid the aim, the numerical simulations of a three-dimensional bridge structure were carried out to validate the performance and accuracy of the proposed approach (see **Figure 18**). It was concluded that the reported Bayesian model updating technique was capable of predicting the posterior probabilities of unknown structural parameters.

**Figure 18.** *Finite element model of a bridge structure [54].*

**Figure 19.** *Nam O railway bridge [55].*

#### **Figure 20.**

*25-member plane truss [58].*

#### **Figure 21.**

*SHM of the infante D. Henrique bridge (a) bridge image, and (b) sensors locations (a and T represent acceleration and temperature, respectively) [60].*

The Nam O Railway Bridge is a large-scale steel truss bridge located on the unique main rail track from the north to the south of Vietnam (see **Figure 19**). In [55], the experimental measurements obtained from this bridge were carried out under ambient vibrations using piezoelectric sensors, and a finite element model was also created in MATLAB to represent the physical behavior of the structure. By model updating, the discrepancies between the experimental and the numerical results were minimized. For the success of the model updating, the efficiency of the optimization

*DOI: http://dx.doi.org/10.5772/intechopen.104905 Introduction to Monitoring of Bridge Infrastructure Using Soft Computing Techniques*


#### **Table 1.**

*Summary of bridge monitoring using advanced computational techniques.*

algorithm was essential. Particle swarm optimization (PSO) algorithm and genetic algorithm (GA) were employed to update the unknown model parameters. The authors claimed that not only the PSO result showed better accuracy but also reduced the computational cost compared to GA. This study focused on the stiffness conditions of typical joints of truss structures. According to their results, the assumption of semi-rigid joints (using rotational springs) could most accurately represent the dynamic characteristics of the truss bridge.

A damage assessment based on ACO was proposed in [56] from changes in natural frequencies. A plane truss structure was considered in this study to validate the efficiency and robustness of the presented methodology. The authors reported that their method was capable of correct damage assessment even with a noisy dataset. An inverse vibration-based approach using ACO and natural frequencies changes has also been carried out in [57]. The authors of this research made a comparison between ACO and PSO. Furthermore, the performance of a continuous ACO and PSO was also evaluated in [58] for damage detection of plane and space truss structures based on frequency and mode shapes-based objective function. The details of the truss structure are displayed in **Figure 20**.

A regression-based damage detection approach was developed by [59] using the natural frequencies of the Z24 Bridge. In this research, the traditional regression, as well as the developed regression, was applied to the illustrative structures for identifying the existence of damage. It was established that both methods could detect the presence of damage. However, better outcomes were acquired by the developed regression-based damage detection approach. Another regression model has been developed by [60] as an up-to-date damage detection scheme in a fieldmonitored bridge. To aid the aim, the Infante D. Henrique Bridge in Portugal has been continuously monitored since 2007 using two synchronized Global Positioning System (GPS)-based data analyzers, temperature and vibration accelerometers (see **Figure 21**). It is worth noting that the recorded data were less contaminated by noise through GPS connection. The measured natural frequencies change corresponding to the damage scenarios by reduction in the vertical bending inertia of the arch were employed in this work to obtain damage-sensitive features.

**Table 1** summarizes the latest applications of artificial intelligence methods in bridge health monitoring.
