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Seismic Fragility Assessment of Balulang-Macasandig Bridge in Cagayan De Oro City with Consideration of Soil-Structure Interaction

Chub Kreino T. Bokingo*, Israel A. Baguhin

Department of Civil Engineering, University of Science and Technology of Southern Philippines, Cagayan de Oro City, Philippines

Abstract: Regions located along the Pacific Ring of Fire, including the Philippines, are particularly susceptible to frequent and potentially damaging seismic events. Historical records have shown that seismic events in the Philippines have caused significant damage to infrastructure, including bridges, which are highly vulnerable to large deformations and displacement-induced failures. This study evaluates the seismic performance of the Balulang-Macasandig bridge in Cagayan de Oro City, through nonlinear static pushover analysis, the ATC-40 Capacity Spectrum Method, and probabilistic fragility assessment, incorporating both fixed-base and soil-structure interaction (SSI) conditions. The inclusion of SSI increased ultimate base shear in the X-direction by approximately 43% and 26%, while reducing it in the Y-direction by about 12.6%. Displacement demand increased with seismic intensity. At about 1g, moderate damage states became common and severe damage states began appearing at about 2g. SSI consistently resulted in higher displacement demands and earlier exceedance of damage thresholds. Fragility curves exhibited a leftward shift under SSI, indicating higher probabilities of damage at lower peak ground accelerations (PGA). Median PGA values in the X-direction ranged from approximately 0.32g for slight damage to 3g for collapse, while the Y-direction only reached moderate damage within the analyzed range of 0.84g-2.21g, reflecting greater transverse resistance. Lognormal dispersion values ranged from 0.85-1.59 and 0.93-1.05 in the X and Y-direction, respectively, indicating consistent variability in structural response. These findings emphasize the need to incorporate soil-structure interaction and site-specific geotechnical characterization in seismic assessment to ensure more accurate and reliable evaluation of bridge performance.

Keywords: Bridge Engineering, Capacity Spectrum Method, Peak Ground Acceleration, Pushover Analysis, Soil-Structure Interaction

Received March 01, 2026; Revised April 02, 2026; Accepted April 09, 2026

Citation: Chub Kreino T. Bokingo, Israel A. Baguhin, Seismic Fragility Assessment of Balulang-Macasandig Bridge in Cagayan De Oro City with Consideration of Soil-Structure Interaction. American Journal of Civil Engineering and Architecture, 14 (2). 39-45. January 2026. doi:10.12691/ajcea-14-2-2

1. Introduction

Regions located along the Pacific Ring of Fire, including the Philippines, are particularly susceptible to frequent and potentially damaging seismic events with relatively short recurrence intervals [1]. Historical records documented significant damage to infrastructure, including bridges, which are highly vulnerable to large deformations and displacement-induced failures. Bridges serve as critical components of transportation networks, supporting mobility, economic activity, and emergency response. However, their structural response to seismic loading is inherently complex due to the interaction of material nonlinearity, geometric configuration, and varying ground motion characteristics [2]. Despite adherence to design standards, unforeseeable factors often interfere and could hasten eventual collapse, even in newly constructed bridges. Such instance is the fall of the Cabagan-Sta. Maria Bridge in Isabela only a week after its opening to the public [3]. Although, failure primarily results from loading issues, it demonstrates that scrutiny of newly constructed structures is equally important. In lieu of the latter, studies on structural response to seismicity project possible failure mechanisms [4].

The Balulang-Macasandig Bridge, located in Cagayan de Oro City, Philippines, is a prestressed concrete deck girder bridge that serves as a major river crossing that connects urban routes and supports daily transportation activities. As part of the expanding transport network in the city, the bridge plays a significant role in maintaining mobility and economic activity. Despite its importance, limited studies have evaluated its seismic performance using integrated approaches that combine structural capacity assessment, displacement-based evaluation, and probabilistic fragility analysis, particularly with consideration of soil-structure interaction effects.

Nonlinear static pushover analysis has become a widely accepted method for evaluating the seismic capacity of structures. Pushover analysis involves the incremental application of lateral loads to simulate structural response beyond the elastic range, enabling identification of critical performance parameters such as yield displacement, ultimate displacement, and maximum base shear [5]. The resulting pushover curves offer valuable insights into structural behavior and failure mechanisms [6]. A study on the seismic fragility assessment of Plaridel Bridge proved pushover analysis to be effective in identifying critical structural components, in most cases piers, which often govern the seismic response of bridge systems [4]. Pushover-based evaluation can also provide reliable estimates of structural capacity [7].

To relate structural capacity with seismic demand, the Capacity Spectrum Method (CSM) based on ATC-40 procedures is commonly employed. The method is described as a transformation of the pushover curve into spectral coordinates, allowing direct comparison with seismic demand spectra to determine the structural performance point [8]. The performance point represents the expected displacement of a joint under a given seismic intensity and serves as a basis for defining damage states [9]. It is further emphasized that performance points derived from multiple ground motion inputs can be used to evaluate displacement demand across varying levels of seismic intensity [4]. Capacity Spectrum Method in conjunction with pushover analysis is used to assess bridge performance and derive displacement-based damage thresholds [10].

Beyond deterministic evaluation, seismic fragility analysis provides a probabilistic framework for assessing structural vulnerability. Fragility curves are described as functions that express the probability of a structure reaching or exceeding specific damage states as a function of seismic intensity measures such as peak ground acceleration (PGA) [11]. The Federal Emergency Management Agency established standardized damage states through the HAZUS framework, which has been widely adopted in fragility analysis [9,12]. Integrating performance-based analysis with probabilistic modeling allows for a more comprehensive assessment of structural behavior under uncertainty [13]. Fragility analysis has evolved into a fundamental tool in modern earthquake engineering, enabling risk-informed decision-making for infrastructure systems [14]. In particular, the use of median seismic intensity measures, such as the peak ground acceleration corresponding to a 50% probability of exceedance, provides a concise and meaningful representation of structural vulnerability and facilitates comparison between different modeling conditions [4].

An important factor influencing seismic response is soil–structure interaction (SSI), which accounts for the coupled behavior between the structure and the supporting soil [15]. Soil flexibility can be represented using equivalent stiffness parameters such as the modulus of subgrade reaction, allowing practical incorporation of soil behavior into structural models [16]. Additionally, simplified spring-based approaches can effectively simulate soil response in engineering analysis [17]. SSI was also known to alter the dynamic properties of bridge systems, including displacement demand and force distribution [18]. Neglecting the former may lead to inaccurate estimation of seismic vulnerability, as soil parameters bear a profound impact on structural behavior as it interacts with the ground [18,19]. Accuracy is substantially improved by the inclusion of SSI in the assessment of probable seismic risk such as seismic fragility analysis [2].

2. Methodology

This section details the method of evaluating the seismic performance of Balulang-Macasandig Bridge through an integrated analytical framework combining Pushover Analysis, the ATC-40 Capacity Spectrum Method and seismic fragility curves that utilized lognormal cumulative distribution of damage threshold occurrences [4,7,10,20].

2.1. Research Framework

The study followed a quantitative deterministic approach by deriving probabilities of exceeding certain damage thresholds at various levels of peak ground acceleration. The methodology is separated into five stages: normalization of ground motion data, bridge and foundation modeling, pushover analysis, fragility curve development and linear regression analysis.

2.2. Study Area

The study was conducted at the Balulang–Macasandig Bridge, a major bridge crossing over the Cagayan River located in Cagayan de Oro City, Misamis Oriental, Philippines. The bridge connects Barangay Balulang on the western bank and Barangay Macasandig on the eastern bank, serving as a critical link within the urban transportation network in the city. The approximate geographic coordinates of the site are 8.449088° N latitude and 124.637415° E longitude. The surrounding area is characterized by dense residential communities, local road networks, and urban infrastructure.

2.3. Data Sources

The analysis utilized structural, geotechnical, and seismic input data obtained from reliable institutional sources to ensure consistency and validity of the results. Bridge design specifications and boring log data, were acquired from the Department of Public Works and Highways (DPWH) Region X and served as the basis for the development of the numerical model. Historical earthquake records were sourced from the Philippine Institute of Volcanology and Seismology (PHIVOLCS) to produce response spectra at different target peak ground accelerations (PGA). A total of thirty (30) ground motion records were selected based on predefined criteria. The seismic event should have occurred within 350 km from Cagayan de Oro City, moment magnitude of 5.0 and above, and focal depths not exceeding 70 km.

2.4. Structural Modeling and SSI

A three-dimensional numerical model of the Balulang-Macasandig Bridge was developed using CSIBridge that incorporated the geometric configuration, material properties, and boundary conditions. Two modeling conditions were considered in the analysis: a fixed-base model and a soil–structure interaction (SSI) model [4,20].

The SSI condition was implemented using the substructure method wherein the supporting soil was represented by equivalent spring elements at the foundation level [20]. Due to current software limitations, a concrete alternative to steel H-piles were modeled instead, emulating the same load bearing capacities. Equivalent vertical and lateral spring stiffness values were assigned to the modeled soil profile from boring log data based on empirical relationships derived for the modulus of subgrade reaction [16,17]. This approach allowed the simulation of foundation flexibility and its influence on structural response while maintaining computational efficiency. Figure 2.1 presents a visual of the structural model as produced using CSIBridge incorporating SSI. The representative piles in yellow are split into broken lines which represent soil effects as springs [20]. Figure 2.2 depicts a focused illustration of the representative piles.

Figure 2.1. Structural Model of Balulang-Macasandig Bridge in CSIBridge with Pile Foundation System

Figure 2.2. Pile Group Foundation Model in CSIBridge

2.5. Normalization of Ground Motion Data

The selected ground motion records obtained from PHIVOLCS were processed to ensure consistency in seismic input across all analyses. Each acceleration time history was normalized to a unit peak ground acceleration (PGA) to eliminate variability in original record amplitudes [4,7]. Following normalization, the ground motion records were scaled to target PGA levels ranging from 0.1g to 3.0g, representing varying seismic intensity levels. This procedure enabled systematic evaluation of structural response under progressively increasing seismic demand while maintaining the inherent characteristics of each ground motion record. The normalized and scaled accelerograms were subsequently used to generate response spectra using PRISM for input into the bridge model via CSIBridge [4].

2.6. Nonlinear Static Analysis

The yield displacement, ultimate displacement and maximum base shear response were obtained using pushover analysis. Displacements from performance points at different target PGAs were obtained using the capacity spectrum method. The values were used to determine damage thresholds along with the yield and ultimate displacements at targeted pier joints [4,7,10].

2.6.1. Pushover Analysis

Structural capacity was evaluated through nonlinear static pushover analysis by incrementally applying lateral loads to the bridge model in the principal directions. The analysis was performed until a target displacement was reached, specifically at joints at the middle pier and end piers, allowing the structure to undergo inelastic deformation [2,4]. Joint 102 is the joint at the topmost part of the middle pier while Joint 110 is representative of the two piers at the both ends of the bridge. For clarity, in the model considering SSI, these joints were renumbered, with Joint 102 as 574 and 110 as 1054.

Due to symmetry, joint consideration was narrowed down to two critical joints. The resulting pushover curves, expressed as base shear versus displacement, were used to determine key capacity parameters, which are the yield displacement, maximum displacement, and maximum base shear. These parameters characterize the nonlinear behavior of the bridge and served as fundamental inputs for subsequent performance evaluation [4].

2.6.2. ATC-40 Capacity Spectrum Method

Seismic demand was assessed using the Capacity Spectrum Method (CSM) following ATC-40 procedures. Selected ground motion records were normalized and scaled to represent a range of seismic intensities. The processed time history data were converted into response spectra, which were used as demand spectra for the analysis [4,7]. The pushover curves were transformed into spectral coordinates and intersected with the corresponding demand spectra to determine the structural performance points. These performance points represent the expected displacement response of the bridge under varying levels of seismic excitation and were used as the basis for evaluating joint displacements and structural performance [10].

2.6.3. Damage State Determination

Damage states were defined based on displacement thresholds derived from the structural capacity parameters. For each ground motion input and target seismic intensity, the corresponding joint displacements at the performance points were evaluated and classified into predefined damage states in Figure 2.3 [21]. Using the same ductility demands, the corresponding damage state definitions were relabeled as shown in Figure 2.4 to be timely with actual structural behavior, assuming damage already transpires at the lowest damage state, albeit negligible [4,7]. The definitions for each state are based on the HAZUS Technical Model that notes on the possible appearance of damage even at the weak onset of seismic activity, noting it as the realistic assumption [12].

The number of occurrences for each damage state was recorded across all ground motion inputs and seismic intensity levels. This allowed the characterization of structural response in terms of damage distribution and probability of occurrence under increasing seismic demand.

Figure 2.3. The FEMA Damage State Denotation and Ductility Demand Parameters

Figure 2.4. Damage States with corresponding color notation as shown in the seismic fragility curves and definitions based on the HAZUS Technical Model

2.7. Fragility Curve Development

Seismic fragility curves were developed to quantify the probability of the bridge reaching or exceeding specific damage states as a function of seismic intensity. The probability of exceedance was computed based on the recorded damage state occurrences for each intensity level. The resulting fragility curves provide a probabilistic representation of structural vulnerability and enable comparison of seismic performance under fixed-base and soil–structure interaction conditions [4,7]. This approach facilitates a comprehensive assessment of how foundation flexibility influences the likelihood of damage under varying levels of ground motion. The fragility curve function is defined as [22]:

P(DS ≥ i | IM) = Φ((ln (IM) – ln (θi))/βi) (1)

The notation IM represents seismic intensity measure, taken in this study as peak ground acceleration (PGA). The parameter θi denotes the median capacity corresponding to a damage state i, which represents the intensity level at which there is a 50% probability of exceedance. The term ßi is the lognormal dispersion parameter that accounts for the variability and uncertainty in structural response. The function Φ, corresponds to the standard normal cumulative distribution function. This formulation assumes that the plot of probability of exceedance with respect to PGA follows a lognormal distribution, which is commonly adopted in seismic fragility analysis due to its ability to represent the nonlinear and uncertain nature of structural behavior under earthquake loading. From the same relationship, the median PGA and lognormal dispersion can be calculated through the inverse of the standard normal cumulative distribution function [22].

P(DS ≥ i | IM) = Φ⁻¹((ln (IM) – ln (θi))/βi ) (2)

3. Results and Discussion

The seismic fragility assessment of Balulang-Macasandig Bridge reveals a directional dependency in structural capacity [4]. A similar observation persists in the fragility curves. Soil-structure interaction is strongly direction-dependent, incurring higher displacements at progressively increasing target PGA and earlier manifestations of higher damage states specifically at the longitudinal direction, along the X-axis of the bridge. It is also determined that soil-structure interaction causes earlier manifestations of higher damage states as influenced by higher displacements across all target PGA and joints of interest [2,20]. In conjunction with the primary objectives, the results are presented in detail:

3.1. Pushover Curves

The pushover curve comparison in Figure 3.1 indicates a pronounced directional dependency in the effect of soil-structure interaction. In both structural configurations, the Y-direction exhibits substantially higher lateral resistance than the X-direction, signifying that the X-axis is the weaker structural direction [4,7].

Figure 3.1. Pushover Curve Comparison with SSI vs without SSI

In contrast, for the Y-direction, SSI reduces the ultimate base shear by approximately 12.6% in both cases, indicating that soil compliance decreases the effective lateral resistance in that axis [2]. These results demonstrate that SSI effects are strongly direction-dependent and should be explicitly incorporated in nonlinear seismic performance evaluation rather than simply assuming fixed-base conditions [2,20].

3.2. Displacement Demand

The displacement demand of the Balulang-Macasandig Bridge, obtained from performance points using the Capacity Spectrum Method, exhibited a consistent increase with rising seismic intensity from 0.1g to 3.0g [23]. At lower PGA levels (0.1g–0.5g), the structural response remained largely within the elastic to slight damage range, with negligible occurrence of higher damage states. As seismic intensity increased to approximately 1.0g, moderate damage began to emerge with increasing frequency, as it exceeds approximately 50% occurrence at higher intensities [4,7]. Extensive and complete damage states became more prominent beyond 2.0g, indicating significant structural degradation under strong ground motion. A comparison between modeling conditions shows that soil–structure interaction (SSI) consistently produced higher displacement demands, resulting in earlier exceedance of damage thresholds and increased occurrence of moderate to complete damage states at lower PGA levels compared to the fixed-base model [2,20]. These findings confirm that SSI amplifies displacement response especially at the X-direction and accelerates the progression of structural damage, a behavior also apparent in the fragility curves [2].

3.3. Seismic Fragility Curves

The developed seismic fragility curves illustrate the probability of exceedance for various damage states as a function of peak ground acceleration (PGA) for both fixed-base and soil-structure interaction (SSI) conditions [4,22]. A consistent trend observed across all joints and directions is the leftward shift of fragility curves under SSI, indicating an increased probability of damage at lower seismic intensities [20].

For Joints 110 to 1054 in the X-direction, the probability of exceeding slight damage states (DS-D) reaches approximately 0.60 at around 0.5g under SSI, compared to a lower probability under fixed-base conditions at the same intensity. Similarly, in the Y-direction, SSI results in a steeper increase in probability, with DS-D exceeding 0.70 at approximately 1.0g, a higher probability of exceedance in contrast to 0.44 at the same PGA for the fixed-base condition.

A comparable pattern is observed for Joint 102–574, where SSI consistently produced higher exceedance probabilities across all damage states. In the X-direction, the probability of DS-D approaches 0.80 at approximately 1.0g, while in the Y-direction, similar probability levels are reached at slightly higher intensities. Across all cases, lower damage states (DS-D and DS-B) exhibit gradual increases in probability, whereas higher damage states (DS-A and DS-As) show more rapid transitions, particularly under SSI conditions [2,18].

The comparative analysis indicates that SSI amplifies structural vulnerability by increasing the likelihood of damage at lower PGA levels and accelerating the progression between damage states. This effect is more pronounced in the X-direction, which aligns with the previously identified weaker structural axis, thereby reinforcing the directional dependency of seismic response [4,7].

In summary, the fragility curves demonstrate that neglecting SSI may lead to underestimation of damage probabilities, particularly for moderate to severe damage states [20]. The results confirm that soil-structure interaction plays a critical role in seismic vulnerability assessment and must be explicitly considered in fragility-based evaluation of bridge systems [2].

Figure 3.2. Seismic Fragility Curves of Critical Joints at the Piers

3.4. Median PGA and Lognormal Dispersion

The fragility curves developed in this study were expressed using the lognormal distribution model, defined by the median peak ground acceleration, denoted by θ, and the logarithmic standard deviation, also the lognormal dispersion, denoted by β. The median PGA represents the seismic intensity at which there is a 50 percent probability of exceeding a particular damage state, while the dispersion parameter reflects the degree of uncertainty or variability in the structural response for that damage state [12,22].

For the X-direction, the median PGA for Joint 110 and Joint 1054 begins at θ = 0.32009 for slight damage (DS-D), increases to 1.3127 for moderate damage (DS-C), 2.16113 for extensive damage (DS-B), 2.74311 for heavy damage (DS-A), and reaches 3.0 for collapse (DS-As). A similar increasing trend is observed for Joint 102 and Joint 574, where the median PGA values are 0.32009, 1.18372, 1.87337, 2.06662, and 2.4808, respectively. These results indicate that the X-direction experiences a complete damage progression from slight damage to collapse within the investigated PGA range.

In contrast, the Y-direction exhibits a markedly different behavior. All joints show identical median PGA values of 0.83862 (DS-D) and 2.21159 (DS-C), while higher damage states (DS-B to DS-As) were not reached within the considered PGA range. This indicates that only slight and moderate damage occurred in the transverse direction, implying a significantly higher resistance to severe damage compared to the X-direction [4,7]. The absence of higher damage states suggests that stronger seismic excitation beyond the analyzed range would be required to induce extensive or collapse-level damage.

The dispersion parameter, 𝛽, provides additional insight into the variability of structural response. In the X-direction, 𝛽 ranges from approximately 0.85 to 1.59, with higher values observed for slight and collapse damage states. This indicates greater uncertainty in the onset of initial damage and final failure which is common to higher probabilities of exceedance, while intermediate damage states exhibit relatively lower dispersion, reflecting a more concentrated range of exceedance [22]. In the Y-direction, dispersion values are more limited, with 𝛽 = 0.92673 for slight damage and 𝛽 = 1.05131 for moderate damage across all joints, indicating a more stable and consistent structural response in the transverse direction.

4. Conclusions and Recommendation

The seismic fragility assessment of Balulang-Macasandig Bridge demonstrates that soil–structure interaction (SSI) significantly influences structural capacity, displacement demand, and seismic vulnerability. A pronounced directional dependency was observed, with SSI increasing ultimate base shear in the X-direction by approximately 43% and 26%, while reducing it in the Y-direction by about 12.6% [4,7]. Fragility curves further indicate that SSI increases the probability of damage at lower seismic intensities, as evidenced by their leftward shift [2,20]. This is supported by median PGA values, where the X-direction shows a full damage progression from approximately 0.32g to 3.0g, while the Y-direction only reaches moderate damage within 0.84g to 2.21g. Dispersion values at β ≈ 0.85–1.59 for X and 0.93–1.05 for Y, suggest that SSI primarily affects the central tendency of response rather than its variability [20]. Neglecting SSI may lead to unconservative seismic assessments, particularly for bridge systems supported by flexible foundations [15].

4.1. Site-Specific Geotechnical Investigation

Future studies are recommended to incorporate detailed geotechnical investigation data, including site-specific soil stiffness parameters and dynamic soil properties, to improve the accuracy of soil-structure interaction modeling. The use of in-situ testing and advanced soil characterization methods would allow more reliable estimation of foundation behavior and reduce uncertainties associated with simplified spring-based representations [2].

4.2. Retrofitting and Structural Monitoring

Given the increased vulnerability observed in the X-direction and the amplified displacement response under SSI conditions, it is recommended that the Balulang-Macasandig Bridge be subjected to regular structural health monitoring and seismic evaluation [4,7]. Installation of monitoring instruments such as accelerometers and displacement sensors can provide real-time performance data. In addition, retrofit strategies such as column confinement and bearing enhancement should be considered to improve ductility and reduce the likelihood of severe damage under strong ground motion.

4.3. SSI in Design and Evaluation

The results of this study highlight the significant influence of SSI on structural response and fragility characteristics. It is therefore recommended that future bridge design and seismic assessment practices explicitly incorporate SSI effects rather than relying on fixed-base assumptions. The adoption of SSI-informed analysis in engineering guidelines and infrastructure evaluation frameworks would lead to more realistic and reliable seismic performance predictions [15,21].

ACKNOWLEDGEMENTS

I would like to recognize the unwavering support of the University of Science and Technology of Southern Philippines, the Department of Public Works and Highways Region X and the Philippine Institute of Volcanology and Seismology.

References

[1]   | Applied Technology Council. (1996). Seismic Evaluation and Retrofit of Concrete Buildings (SSC 96-01; Vols. 1–2) [Ebook]. Seismic Safety Commission. Retrieved March 28, 2026, from https:// www.academia.edu/ 58193403/ Kupdf_net_ atc_40_seismic_ evaluation_ and_retrofit_of_concrete_buildings.
[2]   | Padgett, J. E., & DesRoches, R. (2008). Methodology for the development of analytical fragility curves for retrofitted bridges. Earthquake Engineering & Structural Dynamics, 37(8), 1157–1174.
[3]   | Shinozuka, M., Feng, M. Q., Kim, H. K., & Kim, S. H. (2000). Nonlinear procedure for fragility curves development. Journal of Engineering Mechanics, 126(12), 1287–1295.
[4]   | Chiou, J. S., Chiang, C. H., Yang, H. H., & Hsu, S. Y. (2011). Developing fragility curves for a pile-supported wharf. Soil Dynamics and Earthquake Engineering, 31(5), 830–840.
[5]   | Karim, K. R., & Yamazaki, F. (2003). A simplified method of constructing fragility curves for highway bridges. Earthquake Engineering & Structural Dynamics, 32(10), 1603–1626.
[6]   | Hwang, H., Liu, J. B., & Chiu, Y. H. (2001). Seismic Fragility Analysis of Highway Bridges. In ResearchGate. Center for Earthquake Research and Information. Retrieved March 28, 2026, from https:// www.researchgate.net/ publication/ 32962831_ Seismic_ Fragility_Analysis_of_Highway_Bridges.
[7]   | Naeini, S. A., Moayed, R. A., & Allahyari, F. (2014). Subgrade Reaction Modulus (Ks) of Clayey Soils Based on Field Tests. Journal of Engineering Geology, 8(1), 2021–2046. https:// tinyurl.com/mp2rrsef.
[8]   | Bowles, J. E. (1997). Foundation Analysis and Design (5th ed.) [Ebook]. The McGraw-Hill Companies, Inc. https:// books.google.com.ph/ books/ about/ Foundation _Analysis_ and_Design.html?id=iuBwtgAACAAJ&redir_esc=y.
[9]   | Calvi, G. M., Pinho, R., Magenes, G., Bommer, J. J., Restrepo-Vélez, L. F., & Crowley, H. (2006). Development of Seismic Vulnerability Assessment Methodologies over the past 30 years. ISET Journal of Earthquake Technology, 43(3), 75–104.
[10]   | Villaverde, R. (2007). Methods to Assess the Seismic Collapse Capacity of Building Structures: State of the Art. Journal of Structural Engineering, 133(1), 57–66.
[11]   | Federal Emergency Management Agency. (2021). Hazus Inventory Technical Manual. In Federal Emergency Management Agency. Retrieved March 28, 2026, from https:// www. fema.gov/ sites/default/files/documents/fema_hazus-inventory-technical-manual-4.2.3.pdf.
[12]   | Karim, K. R., & Yamazaki, F. (2001). Effect of earthquake ground motions on fragility curves of highway bridge piers based on numerical simulation. Earthquake Engineering & Structural Dynamics, 30(12), 1839–1856.
[13]   | Cuario, E. D., Tuason, J. A., & Teodoro, M. N. (2023). Seismic Assessment of MacArthur Bridge in Manila Using Pushover Analysis and Capacity-Spectrum Method [Thesis, Mapùa University].
[14]   | Federal Emergency Management Agency. (2005). Improvement of Nonlinear Static Seismic Analysis Procedures. In Mitigation Center Earthquake Engineering Research Institute. Retrieved March 28, 2026, from https:// mitigation.eeri.org/ wp-content/uploads/fema-440.pdf.
[15]   | Kanakeswararao, T., & Ganesh, B. (2017). Analysis of Pile Foundation Subjected to Lateral and Vertical Loads. International Journal of Engineering Trends and Technology, 46(1), 113–127.
[16]   | Freeman, S. A. (2004). Review of the Development of the Capacity Spectrum Method. ISET Journal of Earthquake Technology, 41(1), 1–13.
[17]   | Cupay, G. A., Legaspi, A. C., Amores, N., Legaspi, P. G., Cabanaz, I. A., Lo, D., & Baylon, M. B. (2019). Flood and seismic fragility curve analysis of Kagay-an bridge in Cagayan de Oro City [Thesis, Xavier University - Ateneo de Cagayan].
[18]   | Leslie, R. (2013). The Pushover Analysis, explained in its Simplicity. In 2nd National Conference – RACE’13. SaintGits College of Engineering. https:// www.researchgate.net/ publication/ 315686143 _The_ Pushover_ Analysis_ explained_ in_its_ Simplicity.
[19]   | Themelis, S. (2008). Push-over analysis for performance-based seismic design [Thesis, Heriot-Watt University]. In Computers & Structures. https://files01.core.ac.uk/download/pdf/29824587.pdf
[20]   | Baylon, M. (2017). Seismic Assessment of Plaridel Bridge using Fragility Curve. In Conference: 43rd Philippine Institute of Civil Engineers National Convention. https:// www.researchgate.net/ publication/ 321586268_ SEISMIC_ ASSESSMENT_ OF_PLARIDEL_ BRIDGE_USING_FRAGILITY_CURVE.
[21]   | Sampang, D. (2026, March 2). Isabela bridge now open to light vehicles. Philippine Daily Inquirer. https:// newsinfo.inquirer.net/ 2189336/isabela-bridge-now-open-to-light-vehicles.
[22]   | Stefanidou, S. P., Sextos, A. G., Kotsoglou, A. N., Lesgidis, N., & Kappos, A. J. (2017). Soil-structure interaction effects in analysis of seismic fragility of bridges using an intensity-based ground motion selection procedure. Engineering Structures, 151, 366–380.

[23]  
Panaligan, R., Oropel, J., Azuhata, T., Momo, Y., Kawamata, Y., Takahashi, A., Hamamoto, S., & Yamazaki, J. (2018). Earthquake Performance Evaluation of a Typical Bridge Structure Designed by Force-Based Design in the Philippines. In The 17th World Conference on Earthquake Engineering. https:// wcee.nicee.org/ wcee/article/17WCEE/2b-0110.pdf.