President Ferdinand “Bongbong” Marcos Jr. unveiled his administration’s plans to ensure that no Filipino will be left behind as the country enters the age of exponential technology adoption.

1. z
Artificial Intelligence Application of
Philippine Bridges’ Condition
Analysis
Abelardo S. Lapatha Jr.

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6 significant bridges
in the Philippines
A quick survey

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Macapagal (Palaypay) Bridge
Bridge Carries Spans Region
Length in
meters
Opened
Macapagal
(Palaypay)
Bridge
N955 (Gingo
og-Claveria-
Villanueva
Road)
Odiongan
River
in Gingoog,
Misamis
Oriental
Northern
Mindanao
202 2008
https://en.wikipedia.org/wiki/List_of_bridges_in_the_Philippines

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Macapagal Bridge
Bridge Carries Spans Region
Length in
meters
Opened
Macapagal
Bridge
N951 (Mayor
Democrito D.
Plaza II
Avenue)
Agusan
River in Butua
n, Agusan del
Norte
Caraga 908 2007
https://en.wikipedia.org/wiki/List_of_bridges_in_the_Philippines

5. z
Davao River Bridge
Bridge Carries Spans Region
Length in
meters
Opened
Davao River
Bridge
N913 (Davao
City Diversion
Road)
Davao
River in Dava
o City
Davao
Region
140.60 2001
https://en.wikipedia.org/wiki/List_of_bridges_in_the_Philippines

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Datu Sahid Piang Bridge
Bridge Carries Spans Region
Length in
meters
Opened
Datu Sahid
Piang Bridge
N940 (Miday
ap–Makar
Road)
Tamontaka
River in Datu
Piang,
Maguindanao
Bangsamoro 312.45 1994
https://en.wikipedia.org/wiki/List_of_bridges_in_the_Philippines

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Magsaysay Bridge
Bridge Carries Spans Region
Length in
meters
Opened
Magsaysay
Bridge
N9 (Butuan–
Cagayan de
Oro–Iligan
Road)
Agusan
River in Butua
n, Agusan del
Norte
Caraga 856.45 1960
https://en.wikipedia.org/wiki/List_of_bridges_in_the_Philippines

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Quirino Bridge
Bridge Carries Spans Region
Length in
meters
Opened
Quirino
Bridge
AH 26
(N1) (Cotabat
o-Lanao
Road)
Rio Grande
de
Mindanao in
Cotabato City
Bangsamoro 161.80 1950
https://en.wikipedia.org/wiki/List_of_bridges_in_the_Philippines

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Introduction
 Transportation lifelines: infrastructural network within the transport
system (social, economic and environmental needs).
 Fundamental to analyze the vulnerability of infrastructural lifelines:
risk exposure arising from natural hazards.
 High level of dependence by other lifeline utilities; transport
networks.
 An interruption of the road network may well result in the
consequential loss of another service.

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 Modern society relies entirely on an articulated network of
infrastructures, which has assumed a vital role for the system
in its whole.
 Lifelines are, therefore, the networks which are developed on
the entire territory to relate and connect the various
settlements and points of interest of the different subsystems.

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They guarantee the essential services necessary
for the functioning and the survival of the
communities (transports, energy,
telecommunications, water and sanitary networks).
We can define them as the set of structures,
infrastructures and services regarded as
indispensable for the maintaining or protection of
the life of the given systems.

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This is why nowadays we refer to
Engineering of lifelines
In this term we address all
knowledge and
methodologies to design
infrastructures in the
system which have been
planned to reduce and
minimize the exposure and
susceptibility of
infrastructures, also as an
outcome of the use of new
technologies.

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 Lifelines engineering doesn’t have to be referred
exclusively to natural disasters, as earthquakes, but in
general, to any kind of emergency due to a generic
human or natural hazard or disaster: meteorological or
hydro-geological events, fires, floods, toxic and
industrial accidents, hazardous materials
transportation, etc.

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https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/

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ARTIFICIAL INTELLIGENCE
• AI refers to training machines to mimic human intelligence and
perform tasks.
• Machines use an algorithm or mathematical model to interpret
the environment, discover relationships between factors, and
predict future events.
• Everyday applications: Customer service
chatbots, which operate in real time, are
powered by AI. AI also is used to
eliminate mundane work, such as data
entry.
• Health plan applications: Health insurers
are using AI-powered processing to
speed the acceptance or denial of
claims, and to detect fraud. AI also is
being used to support actuarial
functions.

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MACHINE LEARNING
• ML is a subset of AI.
• Data scientists create ML algorithms to enable machines to “learn”
by processing data without explicitly being programmed to
learn.
• This allows machines to make determinations and predictions,
rapidly perform calculations, or process a huge amount of data.
• Everyday applications: ML powers
recommendations from Netflix or
Amazon about which shows to watch,
based on your viewing history.
• Health plan applications: ML-powered AI
is helping insurers predict when a
member is at risk of suffering from a
severe healthcare event, such as an ED
visit, as well as predict the right moment
to intervene.

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DEEP LEARNING
• If machine learning is about discovering relationships between
factors such as causes and effects, DL is based on the premise
that we may not know all the factors within relationships, so we
might need to probe patterns within patterns.
• Everyday applications: Driver-assistance aids in vehicles, such as
hearing a sound when reversing over a white line, were produced
using neural networks. These aids are trained to distinguish between
any white line and a hazard.
• Health plan applications: Predicting metastatic cancer in at-risk
members, an immensely complex task, would help a plan optimize
care management. Traditional regression models and machine
learning cannot perform this prediction, but DL may be able to
unlock this mystery in order to guide earlier intervention.

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AI, ML, DL in Structural Engineering

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AI, ML, DL in Structural Engineering
Machine learning categories with
commonly adopted algorithms

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• Uncertainty is categorized into two types: epistemic (also known as systematic or reducible
uncertainty) and aleatory (also known as statistical or irreducible uncertainty)[6].
• Epistemic Uncertainty derives its name from the Greek word “επιστήμη” (episteme) which can
be roughly translated as knowledge. Therefore, epistemic uncertainty is presumed to derive
from the lack of knowledge of information regarding the phenomena that dictate how a system
should behave, ultimately affecting the outcome of an event.
• Aleatory Uncertainty derives its name from the Latin word “alea” which is translated as “the roll
of the dice”. Therefore, aleatory uncertainty can be defined as the internal randomness of
phenomena.
http://apppm.man.dtu.dk/index.php/Epistemic_vs._Aleatory_uncertainty
Edoardo Patelli and Matteo Broggi. UNCERTAINTY MANAGEMENT AND RESILIENT DESIGN OF SAFETY CRITICAL SYSTEMS June 2015. Conference: NAFEMS World Congress 2015. At: San
Diego, CA.

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AI, ML, DL in Structural Engineering
• In the field of structural engineering, there are numerous problems that are influenced by
uncertainties, e.g., those related to design, analysis, condition monitoring, construction
management, decision making, etc.
Source:
Emerging artificial intelligence methods in
structural engineering Hadi Salehia ,
Rigoberto Burgueño, 2018

22. z
ML applications for building structural design and
performance assessment: state-of-the-art review
by Han Sun, Henry V. Burton, Hongfan Huang

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Objective
To predict the bridge
condition based on the
dataset.
To design a machine
learning model (MLM) for a
satisfactory validation
accuracy.

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https://www.dpwh.gov.ph/DPWH/gis/rbi

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https://www.dpwh.gov.ph/DPWH/gis/rbi

26. z
https://www.dpwh.gov.ph/DPWH/gis/rbi

27. z
Dataset
Attributes
1Bridge Needs Ratio
2General Bridge Type
3Bridge Width
4Estimated Bridge Life
5Bridge Condition
6Bridge Structure
7Height Over
8Height Under
9Load Limit
10No. of Pier
11Maximum Pier Height
12Number of Abutments
13Number of Span
14Sidewalk
15Year of Construction
16Year of Retrofitting
17Road Network
1LEFT/RIGHT SDWALK
2Bridge life / Bridge age
3Bridge width / Bridge Length
4Load Limit in Ton
5Bridge Needs Ratio (BNR)
6
Maximum pier height /
Maximum Bridge Height
7Bridge Condition

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DATASET
 TRAINING DATASET
 TEST DATASET

29. z
z
MATLAB Classification Learner App
A Demonstration

30. z
References
 Department of Public Works and Highways (2021). Detailed Bridge
Inventory.
https://dpwh.maps.arcgis.com/apps/webappviewer/index.html?id=1
153f9b8f2324ad08b22f70a72432100
 Ciriannia, F., Fontea, F., Leonardia, G., Scopellitia, F. (2012).
Analysis of Lifelines Transportation Vulnerability. SIIV - 5th
International Congress - Sustainability of Road Infrastructures.
Procedia - Social and Behavioral Sciences 53 ( 2012 ) 29 – 38.
 Yousefi, A. Bunnori, N. M. and Majid, T. A. (2012). Prioritization of
Lifeline Components for Upgrading Using Multi Criteria Decision
Making: A Case Study of Highway Bridges of Isfahan. Conference
proceedings of Awam International Conference on Civil Engineering
(AICCE’12).


31. z
Wawa Bridge of Liloan
Wawa Bridge
AH 26 (N1) (Maharlika
Highway)
Panaon Strait in Liloan,
Southern Leyte
Eastern Visayas 297m 1977