**4. Structural health monitoring (SHM)**

Engineering assets such as high-rise buildings, long-span bridges, dams, oil platforms, hydraulic structures, wind turbines, transmission towers, ships, offshore structures, aircraft, and rail tracks may experience damage during

*Geographical origin of recorded bridge failures. (a) By country. (b) By continent.*

#### **Figure 4.**

*The distribution of bridge failure causes between 2009 and 2018. (a) Percentage of collapse reasons. (b) Proportion of natural factors and anthropic factors leading to bridge failures.*

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

#### **Figure 5.**

*Number of bridge collapses and casualties between 2009 and 2018 [31].*

**Figure 6.**

*Potential factors influencing the observed failure frequency of bridges.*

construction or while in service induced by different reasons such as common weakening of material properties, fatigue, aging, delamination, wear, corrosion, creep, environmental effects (e.g., microstructural defects, cracking, thermal stress, residual stress, instability, and fastening or adhesive faults), overloading, changes in loading patterns or various unexpected causes such as wind excitations, earthquake, vehicle impact or blast loads during their service life, which can critically disturb their integrity, serviceability, and safety [32, 33]. Therefore, the unpredicted structural failure in such assets can produce catastrophic collapse, economic costs, human injuries, and death.

SHM is a transdisciplinary area of engineering applied to guarantee the operational safety and structural integrity of the materials, different components, or whole structure [34]. Engineers define health monitoring as the measurement of the operating and loading environment and the critical responses of a structure in order to track and evaluate the symptoms of operational anomalies and deterioration or damage that may impact service or safety reliability. The functionality of SHM systems is extremely similar to the human nervous system, as shown in **Figure 8**. The nervous system of humans consists of a complex collection of nerves and specialized cells and the main processing unit (brain). The nerve cells transmit signals between different

#### **Figure 7.**

*Causes of catastrophic bridge failures in the USA. (a) Silver Bridge in Ohio, collapsed on 1967. (b) Mianus River Bridge, Greenwich, collapsed on 1983. (c) Schoharie Creek Bridge—Albany, NY, collapsed on 1987. (d) Route 69 Tennessee River Bridge—Clifton, TN, collapsed on May 1995. (e) I-35W Bridge in Minneapolis, collapsed on August 2007. (f) I-580 Connector Ramp –Oakland, California, collapsed on April 2007. (g) Prosperity Pedestrian Bridge—FIU/University City, Florida, collapsed on March 2018. (h) I 40 Hernando deSoto Bridge Memphis, TN, crack found on May 2021.*

parts of the body and the brain. The brain is the main control unit for receiving and processing information as well as issuing instructions. In the same manner, SHM consists of a sensory network to gather information and a control unit for data processing and decision making [35]. Broadly speaking, the aims of conducting an SHM system

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

**Figure 8.** *Similarity of SHM and human nervous system.*

are as follows: (1) To determine the current condition, (2) To predict future behavior, and (3) To early detect the structural damages.

The first bridge health monitoring was conducted in 1937 on the Golden Gate Bridge in San Francisco. Likewise, the direct application of data mining in structural damage identification was started in 2014 [36]. An overview of SHM and soft computing evolution over the years is shown in **Figure 9**. Eventually, the monitoring process in SHM generates massive data. Hence, precious information has to be acquired from unprocessed datasets [37]. However, data analysis of the generated big data obtained from the sensor network is a challenging task [36, 38]. To overcome this, data mining can therefore be employed to develop damage detection schemes [39, 40]. In computer science, data mining has become a research hotspot in recent years [41]. Consequently, data mining is offering a bright future ahead for other inquiries in various areas, such as aerospace, civil, industrial, and mechanical engineering. It is because data mining has a key role in the extraction of valuable information from different databases [6, 42].

With demanding needs to generate a massive volume of datasets, there has been a revolution in measuring and monitoring systems in the 1990s such as the development of sophisticated signal technologies, wireless networks, optical sensors, and global positioning systems. In this direction, the growth in the number of sensors installed on several important bridges worldwide during the past 20 years is shown in **Figure 10** [43].

#### **Figure 9.**

*An overview of SHM and soft computing evolution over the years.*

**Figure 10.**

*An example of the evolution of the number of sensors for bridge monitoring during the past 20 years, adopted from [43].*
