ENGINEERING
DATA ANALYSIS
2ND SEMESTER SY 2024-2025
ENGR. ALEX M. BALUBAL, MEE, CSEE
ASSOCIATE PROFESSOR V
Isabela State University
City of Ilagan Campus
CIVIL ENGINEERING DEPARTMENT
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Introduction
Data Analysis involves gathering
and studying data to form insights
that can be used to make decisions.
The information derived can be
useful in several different ways, such
as ensuring the safety and efficiency
of an engineering project.
Definition
Data analysis focuses on the direct
interpretation and understanding of
data to inform engineering decisions or
validate existing designs.
Data analytics involves a more
advanced, often data-driven approach,
where complex algorithms and tools
are used to predict future outcomes,
optimize processes, and enhance the
efficiency of engineering projects.
Data Analysis vs. Data Analytics
Integrating data analysis in the civil
engineering discipline can significantly
enhance the effectiveness, efficiency,
and accuracy of various engineering
processes.
Here are some ways to relate or
integrate data analysis to the civil
engineering discipline:
Data Analysis in Civil Engineering
1. Design Optimization
Structural Analysis: Data analysis can be used to
assess the performance of existing structures or
simulate various load scenarios. For example, by
analyzing sensor data from bridges, buildings, or
other infrastructure, engineers can identify
weaknesses, optimize structural designs, and
prevent failure.
Material Selection: By analyzing the performance
data of various materials (e.g., concrete, steel,
composites), engineers can select the best materials
that offer the required strength, durability,
and sustainability for specific projects.
Data Analysis in Civil Engineering
2. Construction Monitoring
Real-time Data: During construction, engineers can use
data analysis to monitor various factors such as
temperature, humidity, and load-bearing conditions in
real-time. This can ensure that the construction process
adheres to safety standards and quality controls, and it
can alert engineers to potential issues like material
curing or structural alignment deviations.
Construction Scheduling: Data analysis can improve
scheduling accuracy by analyzing past project data,
assessing delays, and predicting potential bottlenecks.
This helps in better resource allocation and maintaining
project timelines.
Data Analysis in Civil Engineering
3. Geotechnical Engineering
Soil and Foundation Analysis: Data from soil tests,
such as compaction and bearing capacity tests, can be
analyzed to determine the most appropriate foundation
designs for different soil types. By analyzing historical
data and sensor data from test sites, engineers can
predict soil behavior under different load conditions
and design more effective foundations.
Groundwater Flow Simulation: Data analysis is used
to simulate groundwater movement, which is crucial in
designing drainage systems, underground structures,
and mitigating risks related to water table fluctuations.
Data Analysis in Civil Engineering
4. Environmental Impact Assessment
Sustainability Analysis: Civil engineering projects are
increasingly focused on sustainability. Data analysis
allows for the evaluation of environmental impacts by
analyzing data on air and water quality, waste levels,
energy consumption, carbon emissions, and material
usage. This helps in designing more sustainable
infrastructure by identifying opportunities to minimize
waste, energy usage, and environmental footprint.
Climate Data: Analyzing climate data (e.g., temperature,
precipitation patterns) helps in designing resilient
structures capable of withstanding extreme weather
events such as floods, storms, and
temperature fluctuations.
Data Analysis in Civil Engineering
5. Transportation Engineering
Traffic Flow and Pattern Analysis: Data analysis of
traffic patterns can help in designing roads, bridges,
and highways. For example, data collected from
traffic sensors and cameras can be analyzed to
optimize the design of intersections, reduce
congestion, and improve traffic flow.
Road Maintenance: By analyzing historical data on
road conditions (e.g., crack patterns, wear and tear),
engineers can predict when roads need
maintenance, helping optimize resource allocation
for road repairs and improving the longevity
of road infrastructure.
Data Analysis in Civil Engineering
6. Smart Cities and IoT Integration
Smart Infrastructure: With the increasing use of
Internet of Things (IoT) devices in civil engineering, data
analysis can be applied to monitor and manage smart
infrastructure. Sensors placed in buildings, roads, and
bridges can send real-time data about their condition.
This data can then be analyzed to detect early signs of
deterioration or inefficiency, leading to timely
interventions and maintenance.
Urban Planning: Urban planners can use data analysis
to study population growth, housing needs, and
infrastructure demands, ensuring that cities develop in a
sustainable, efficient, and well-connected manner.
Data Analysis in Civil Engineering
7. Risk Assessment and Safety
Risk Prediction: Data analysis helps in predicting
potential risks, such as landslides, earthquakes, or
floods, by examining historical and real-time data by
applying reliability analysis, fault tree analysis, or
failure mode effect analysis (FMEA). Engineers can
then incorporate these insights into their designs to
minimize risk and enhance safety.
Workplace Safety: By analyzing data from
construction sites, engineers can monitor safety
conditions, assess the effectiveness of safety
protocols, and identify patterns in accidents or nearmisses, leading to improved safety measures.
Data Analysis in Civil Engineering
Building Information Modeling (BIM): BIM can be
integrated with data analysis tools to visualize
designs, simulate different scenarios, and improve
decision-making.
Geographic Information Systems (GIS): GIS data
can be analyzed to study topography, environmental
impacts, and the most efficient placement of
infrastructure in urban planning.
Machine Learning & AI: Machine learning
algorithms can be used to analyze vast amounts of
data from sensors, simulations, and past projects to
predict future outcomes and optimize designs.
Tools and Technologies for Integrating Data Analysis
Obtaining
Data
TOPIC 1
TOPIC 1 –
Obtaining
Data
ENGR. ALEX M. BALUBAL, MEE, CSEE
OBJECTIVES
Upon successful completion of
this topic, students should be
able to:
• understand the different
methods of data collection.
• compare and contrast
between a survey and an
experiment.
TOPIC 1 –
Obtaining
Data
ENGR. ALEX M. BALUBAL, MEE, CSEE
1.1 METHODS OF DATA COLLECTION
In the engineering environment,
the data are almost always a
sample that has been selected
from the population. An effective
data-collection procedure can
greatly simplify the analysis and
lead to improved understanding
of the population or process
that is being studied.
TOPIC 1 –
Obtaining
Data
1.1 METHODS OF DATA COLLECTION
1.1.1 Retrospective Study
1.1.2 Observational Study
1.1.3 Designed Experiments
ENGR. ALEX M. BALUBAL, MEE, CSEE
TOPIC 1 –
Obtaining
Data
ENGR. ALEX M. BALUBAL, MEE, CSEE
1.1.1 RETROSPECTIVE STUDY
A retrospective study would use
either all or a sample of the
historical process data archived
over some period of time. It may
involve a significant amount of
data, but those data may contain
information that are relatively
of little use about the problem.
TOPIC 1 –
Obtaining
Data
ENGR. ALEX M. BALUBAL, MEE, CSEE
1.1.1 RETROSPECTIVE STUDY
Furthermore, some of the relevant
data may be missing, there may
be transcription or recording
errors resulting in outliers, or
data on other important factors
may not have been collected
and archived.
TOPIC 1 –
Obtaining
Data
ENGR. ALEX M. BALUBAL, MEE, CSEE
1.1.2 OBSERVATIONAL STUDY
In an observational study, the
engineer observes the process
or population, disturbing it as little
as possible, and records the
quantities of interest.
Because these studies are
usually conducted for a relatively
short period, sometimes variables
that are not routinely measured
can be included.
TOPIC 1 –
Obtaining
Data
ENGR. ALEX M. BALUBAL, MEE, CSEE
1.1.3 DESIGNED EXPERIMENTS
In a designed experiment, the
engineer makes deliberate or
purposeful changes in the
controllable variables of the
system or process, observes the
resulting system output data,
and then makes an inference or
decision about which variables
are responsible for the observed
changes in output performance.
TOPIC 1 –
Obtaining
Data
1.2 PLANNING AND CONDUCTING
SURVEYS
A survey is a way to ask a lot of people a
few well-constructed questions. The survey
is a series of unbiased questions that
respondents must answer.
•
•
•
ENGR. ALEX M. BALUBAL, MEE, CSEE
Advantages
An efficient way of
•
collecting information from
many people
Relatively easy to
•
administer.
A wide variety of
information can be
collected It can be focused
(researchers can stick to
just the questions that
interest them.)
Disadvantages
It depends on the subjects’
motivation, honesty, memory,
and ability to respond.
Answer choices to survey
questions could lead to
vague data (i.e, the choice
“moderately agree” may mean
different things to different
people or to whoever ends up
interpreting the data.)
TOPIC 1 –
Obtaining
Data
ENGR. ALEX M. BALUBAL, MEE, CSEE
1.2.1 CONDUCTING A SURVEY
Methods for Administering a Survey:
•Face-to-face interview or a phone
interview where the researcher is
questioning the subject. A different
option is to have a
•Self-administered survey where the
subject can complete a survey on
paper and mail it back or complete the
survey online. There are advantages
and disadvantages to each of these
methods.
TOPIC 1 –
Obtaining
Data
ENGR. ALEX M. BALUBAL, MEE, CSEE
FACE-TO-FACE INTERVIEW
Advantages
Disadvantages
• Fewer misunderstood • It can be expensive
questions
• Time-consuming
• Fewer incomplete
• May require a large
responses
staff of trained
• Higher response rates
interviewers
• Greater control over
• Response can be
the environment in
biased by the
which the survey is
appearance or
administered
attitude of the
• Can collect additional
interviewer
information if any of the
respondents’ answers
need clarifying
TOPIC 1 –
Obtaining
Data
ENGR. ALEX M. BALUBAL, MEE, CSEE
SELF-ADMINISTERED SURVEY
Advantages
Disadvantages
• Less expensive •
than interviews
• Does not require
•
a large staff of
experienced
interviewers
•
• Anonymity and
privacy
encourage more •
candid and
honest
responses
• Less pressure on
respondents
Respondents are more likely
to stop participating mid-way
through the survey
Respondents cannot ask the
researchers to clarify their
answers
Lower response rates than in
personal interviews
Often the respondents who
bother to return surveys
represent extremes of the
population – those people
who care about the issue
strongly, whichever way their
opinion leans.
TOPIC 1 –
Obtaining
Data
1.2.2 DESIGNING A SURVEY
Steps
ENGR. ALEX M. BALUBAL, MEE, CSEE
1. Determine the goal of your survey: What questions
do you want to answer?
2. Identify the sample population: Whom will you
interview?
3. Choose an interviewing method: face-to-face
interview, phone interview, self-administered paper
survey, or internet survey.
4. Decide what questions you will ask in what order,
and how to phrase them. (This is important if there
is more than one piece of information you are
looking for.)
5. Conduct the interview and collect the information.
6. Analyze the results by making graphs and drawing
conclusions.
TOPIC 1 –
Obtaining
Data
ENGR. ALEX M. BALUBAL, MEE, CSEE
SAMPLE SURVEY
An interview survey was done to all BSCE
2-E students of ISU-Ilagan. The objective
of the survey was to determine which
among the three mathematics subjects:
Differential
Calculus
(DC),
Integral
Calculus (IC), and Differential Equations
(DE) is the most difficult in their opinion.
Factors which contributed to the difficulty of
the subject were also identified such as:
teaching modality, classroom environment,
teacher factor, student factor, and other
factors identified by the respondents. The
results of the survey are presented below.
TOPIC 1 –
Obtaining
Data
RESULT 1 – OPTION A
Table 1. Frequency Distribution and Rank of the Most Difficult Math Subject
Subject
Frequency
5
Rank
15
30
ENGR. ALEX M. BALUBAL, MEE, CSEE
Differential Calculus
5
3
Integral Calculus
15
2
Differential Equations
30
1
Total
50
DC
IC
DE
Figure 1. Pie Distribution of the Most Difficult Math Subject
Table 1 and Figure 1 above indicate that Differential
Equations poses the greatest challenge, with a
frequency of 30. This is attributed to its perceived
complexity, stemming from the fact that it was not
covered during their high school. In contrast, Differential
Calculus emerges as the least challenging of the three
subjects, with a frequency of only 5. This can be
attributed to the prior exposure of STEM strand
graduates to a significant portion of the college-level
topics being covered.
TOPIC 1 –
Obtaining
Data
RESULT 1 – OPTION B
Table 1. Frequency Distribution and Rank of the Most Difficult Math Subject
30
Subject
Frequency
Rank
Differential Calculus
5
3
25
20
15
ENGR. ALEX M. BALUBAL, MEE, CSEE
Integral Calculus
15
2
Differential Equations
30
1
Total
50
10
5
0
DC
IC
DE
Figure 1. Frequency Distribution of the Most Difficult Math Subject
Table 1 and Figure 1 above indicate that Differential
Equations poses the greatest challenge, with a
frequency of 30. This is attributed to its perceived
complexity, stemming from the fact that it was not
covered during their high school. In contrast, Differential
Calculus emerges as the least challenging of the three
subjects, with a frequency of only 5. This can be
attributed to the prior exposure of STEM strand
graduates to a significant portion of the college-level
topics being covered.
TOPIC 1 –
Obtaining
Data
RESULT 2
Table 2. Frequency Distribution and Rank of Factors Affecting
Difficulty in Math Subjects
9
Factors
Teaching Modality
Classroom Environment
Teacher Factor
Student Factor
Other Factors (Lovelife)
Other Factors (Bullying)
Other Factors (Workload)
Total
ENGR. ALEX M. BALUBAL, MEE, CSEE
Freq
11
5
18
9
4
1
2
50
Rank
2
4
1
3
5
7
6
1
18
2
7
4
5
11
Teaching Modality
Classroom Environment
Teacher Factor
Other Factors (Lovelife)
Other Factors (Bullying)
Other Factors (Workload)
Student Factor
Figure 2. Pie Distribution of Factors Affecting Difficulty in Math Subjects
Table 2 and Figure 2 depicted above reveal that the
primary factor contributing to the challenges in learning
math subjects is the teacher factor. According to the
respondents, the difficulty arises from the fact that the
instructors handling their math subjects are mostly
inexperienced or newcomers, leading to inadequate
explanations of the topics in the syllabus. Additionally,
the second most influential factor contributing to the
difficulty in learning math subjects is the teaching
modality, particularly for topics taught online.
TOPIC 1 –
Obtaining
Data
RESULT 2 (cont)
Table 2. Frequency Distribution and Rank of Factors Affecting
Difficulty in Math Subjects
Factors
Teaching Modality
Classroom Environment
Teacher Factor
Student Factor
Other Factors (Lovelife)
Other Factors (Bullying)
Other Factors (Workload)
Total
ENGR. ALEX M. BALUBAL, MEE, CSEE
Freq
11
5
18
9
4
1
2
50
Rank
2
4
1
3
5
7
6
9
18
4
7
1
2
5
11
Teaching Modality
Classroom Environment
Teacher Factor
Other Factors (Lovelife)
Other Factors (Bullying)
Other Factors (Workload)
Student Factor
Figure 2. Pie Distribution of Factors Affecting Difficulty in Math Subjects
The third factor identified as a cause of difficulty is the
student factor. Respondents, particularly those who are
not STEM graduates, highlighted the challenge of
keeping pace with their STEM graduate classmates.
Classroom environment is among the least of their
worries. Additionally, respondents mentioned other
factors not included in the questionnaire, such as love
life, bullying, and workload, as additional elements
contributing to the overall difficulty in learning math
subjects.
TOPIC 1 –
Obtaining
Data
ENGR. ALEX M. BALUBAL, MEE, CSEE
CONCLUSION
The data reveals that Differential Equations is the most
challenging subject for students due to its complexity
and lack of prior exposure in high school, while
Differential Calculus is the least challenging, particularly
for STEM graduates who have already encountered the
subject.
The primary factors contributing to difficulty in learning
math are the teacher's inexperience, ineffective online
teaching methods, and the struggle of non-STEM
students to keep up with their STEM peers. Although
the classroom environment is less of a concern,
personal issues like love life, bullying, and workload
also impact students' learning. These findings suggest
that improving teaching quality, supporting online
learning, and addressing both academic and personal
challenges could help ease the difficulty students face
in math subjects.
ANY
QUESTIONS
CLASS?
Q&A
TOPIC 1 –
Obtaining
Data
ENGR. ALEX M. BALUBAL, MEE, CSEE
GROUP WORK #1
Conduct a face-to-face interview or selfadministered survey to all the BSCE
students belonging to certain year and
section, e.g., BSCE-1C and create at least
three questions covering a topic of your
choice. Present your data by making tables
and/or charts like the foregoing example.
Discuss, analyze, and draw conclusions
from the results of your survey. Send the
document file of your output to my email
address
following
the
format
GROUPNAME#1.docx not earlier nor later
than February 7, 2025.
TOPIC 1 –
Obtaining
Data
GROUP WORK #1 - RUBRICS
Criteria
Excellent (20)
Good (15)
Satisfactory (10)
Needs Improvement (5)
Data is presented using clear,
Data presentation includes
Data is presented with
Data presentation is
Presentation of appropriate charts/graphs (e.g., bar
basic charts/graphs but
mostly clear charts/graphs.
unclear, poorly chosen, or
lacks clarity or is not fully
Data (Charts/ charts, pie charts, histograms, etc.).
Some minor labeling issues
missing. Visuals are
Visuals are well-labeled, accurate, and
labeled. Some visuals may
Graphs)
or redundant visual types.
ineffective or absent.
enhance understanding.
be confusing.
The discussion of the data is detailed,
Discussion is basic, with
Discussion is clear and
Clarity and insightful, and directly relates to the
some relevant points, but
relevant to the data but may
lacks connection to data
Appropriatenes survey objectives. It offers deep insight
lack depth in explaining
patterns or the survey’s
s of Discussion into trends and patterns, with strong
some trends or patterns.
connections to the context.
social context.
Discussion is superficial,
unclear, or does not relate
directly to the data or
objectives.
Clear, accurate, and thorough analysis Good analysis with most
Basic analysis of data, with
Weak analysis, with little or
of the data. Trends, patterns, and
trends or patterns
some trends identified, but
no explanation of trends or
Data Analysis relationships are effectively identified identified, though some
lacks depth or important
patterns. Lack of supporting
and explained with strong supporting details may be
patterns. Evidence may be
evidence.
evidence.
underexplained or missing. incomplete.
ENGR. ALEX M. BALUBAL, MEE, CSEE
Conclusion
Conclusions are well-supported by the Conclusions are supported Conclusions are somewhat Conclusions are
data. They are actionable, insightful,
by the data, though they
supported by the data but unsupported or unclear,
and strongly tied to the survey findings, may be more general or lack are vague or not fully linked lacking connection to the
offering clear takeaways.
specific recommendations. to survey findings.
data or survey objectives.
Writing is mostly clear, with
Writing is understandable Writing contains frequent
only a few grammatical
Writing is clear, concise, and free from
but contains several
grammatical or spelling
errors. Sentence structure
grammatical or spelling
errors. Sentence structure
grammatical errors. Sentences are
Grammar and
is generally good, and
errors. Sentence structure is poor, affecting readability.
well-structured, and vocabulary is
vocabulary is mostly
Composition
is sometimes awkward or Report lacks clear
varied and appropriate. The report is
appropriate. Organization is
well-organized and flows logically.
unclear. Some parts of the organization and
clear, with minor issues in
report may be disorganized. coherence.
flow.
See you in the
next topic:
Design of
Experiments
TOPIC 1 (continuation)
Probability and Statistics for Engineers and Scientists, Sixth
Edition. Ronald E. Walpol, Ramond H. Myers, Sharon L. Myers.
Pearson Education Asia Pte Ltd, 2000.
Statistics for Engineers and Scientists, Fourth Edition. William
Navidi. McGraw-Hill Education, 2015.
Statistical Methods for Practice and Research, Second Edition.
Ajai S. Gaur, Sanjaya S. Gaur. Sage Publications India Pvt Ltd,
2009.
Various internet sources.
REFERENCES