PSI - Issue 84

Lorenzo Principi et al. / Procedia Structural Integrity 84 (2026) 73–80

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3. ANNs for the Seismic Risk Assessment of Existing Highway Bridges The framework illustrated in Figure 1 was employed to develop an ANN-based model aimed at predicting the seismic risk of bridges, expressed in terms of the Class of Earthquake Risk (CER). All analyses were performed in Python (v3.11.2150.0, 64-bit, released 08/02/2023, Rossum et al., 2010) using the scikit-learn library (v1.2.0, released 12/2022, Pedregosa et al., 2010). 3.1.1. Data Collection Following the approach proposed by Principi et al. (2025), a set of input features was defined for the prediction of the CER. In addition, two new variables were included: the Peak Ground Acceleration (PGA) associated with a 475-year return period under rock and stiff soil conditions, and a binary variable indicating compliance with seismic design provisions. The full set of input features considered in this study is reported in Table 1. 3.1. Phase I: Data Collection and Data Preprocessing

Table 1. Input features and descriptions (V = Vulnerability, E = Exposure, H = Hazard according to IG criteria).

Input Features CER Features Description Max span length V Indicates the length of the longest span Number of spans V Indicates the number of spans of the structure Static scheme V Indicates the static scheme of the bridge Deck material V Indicates the deck material typology Design code class V Class of design code (1) Bridge age class V

Range in which the date of construction of the bridge falls into (≤1945, 1945 - 1980, ≥1980) (1) V Whether designed according to post-2003 Italian standards (O.P.C.M. No. 3274 – March 20, 2003)

Seismic Design Obstacle type Alternative routes

E Indicates the category of obstacles the bridge allows to cross E Presence of other roads to service bridge traffic E Indicates the Average Daily volume of Traffic (ADT) E Indicates the Average Daily Trucks volume of Traffic (ADTT)

ADT

ADTT

H Maximum allowable mass on the bridge (60 tons, ≤ 44 tons, ≤ 26 tons, ≤ 8 tons, ≤ 3,5 tons) (1) H Peak Ground Acceleration (PGA) (a g <0.05g, 0.05g ≤ a g <0.10g, 0.10g ≤ a g < 0.15g, 0.15g ≤ a g <0.25g, a g ≥ 0.25 g) (1)

Load Limit

PGA

Total length

-

Indicates the length of the structure

(1) Ranges and classes as standardized in MIT (2020). The dataset adopted for training the algorithm consists of 521 highway bridges distributed across Italy. Descriptive statistics of the quantitative variables are presented in Table 2, whereas the categorical features in Table 3.

Table 2. Statistical report for the quantitative input features.

Total Length [m]

Max Span Length [m]

Number of Spans

ADT ADTT

Format

Float64 129.34 189.53

Float64

Integer

Integer 11109 12516

Integer

Mean Value

25.47 15.67

5 6 1

656 771

Standard Deviation Minimum Value Maximum Value

1.00

0.90

0

0

1943.00

167.00

56

70199

5547