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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026
Risk Associated with Management of Engineering Projects Using
Intelligent Based Control System
Akaninyene M. Joshua
1
& Chukwuagu M. Ifeanyi
2
1
Department Electrical and Electronic Engineering, Enugu State University of Science and Technology.
2
Department of Electrical and Electronic Engineering Caritas University Amorji-Nike, Emene, Enugu
State
DOI: https://doi.org/10.51583/IJLTEMAS.2026.150600134
Received: 01 July 2026; Accepted: 07 July 2026; Published: 15 July 2026
ABSTRACT
Some abandoned Engineering projects observed in some parts of the country this present era has arisen as a
result of these factors Scope Creep, Schedule Delays, Budget Overruns, Safety Hazards, Technical Challenges
and Additional Risk Factors. This is surmounted by introducing reducing risk associated with management of
engineering projects using intelligent based control system. To achieve this, it was done in this manner,
characterizing and establishing the causes of the risk associated with management of engineering projects,
designing a conventional SIMULINK model for the risk associated with management of engineering projects,
designing management rule base that will reduce the percentage of the risk associated with management of
engineering projects, training ANN in the management rule base to enhance the efficacy of reducing the
percentage of the risk associated with management of engineering projects, developing an algorithm that will
implement the process, designing a SIMULINK model for reducing risk associated with management of
engineering projects using intelligent based control system and validating and justifying the percentage
improvement in the reduction of risk associated with management of engineering projects with and without
intelligent based control system. The results obtained are the conventional Scope Creep that caused risk
associated with management of engineering project was12%. On the other hand, when an intelligent based
control system was introduced in the system, the percentage of risk observed in management of engineering
project was reduced to10.4%, the conventional Safety Hazards that caused high risk associated with management
of engineering projects was7%. However, when an intelligent based control system was integrated in the system
the risk associated with management of engineering projects reduced to6.07% and the conventional Additional
Risk Factors that caused risk associated with management of engineering projects was32% while when an
intelligent based control system was imbibed in the system, it wittingly reduced to27.75%. Finally, with these
results obtained, it shows that the percentage improvement in the reduction of risk associated with management
of engineering projects when an intelligent based control system was integrated in the system over the
conventional competitor was 4.25%.
Keyword: Reducing, risk, associated, management, engineering, project, intelligent, based, control, system
INTRODUCTION
Engineering projects are inherently complex, often involving intricate designs, tight deadlines, and significant
financial investments. Throughout the project lifecycle, various uncertainties and challenges can arise, leading
to significant risks that can impact project success. These risks can manifest as schedule delays, budget overruns,
safety hazards, and ultimately, project failure. This paper explores the potential of intelligent control systems to
mitigate these risks and enhance the overall management of engineering projects. By leveraging artificial
intelligence (AI) and machine learning (ML) techniques, intelligent control systems can offer a data-driven
approach to project management, enabling proactive risk identification, mitigation strategies, and improved
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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decision-making. We will delve into the specific functionalities of intelligent control systems and how they can
be applied to various aspects of engineering project management. We will explore how these systems can:
METHODOLOGY
To characterize and established the causes of the risk associated with management of engineering projects
Tabe1 characterize and established the causes of the risk associated with management of engineering projects
S/N
Causes of risk associated with
management of engineering
projects
Causes of risk associated with
management of engineering
projects (%)
1
Scope Creep
12
2
Schedule Delays:
8
3
Budget Overruns:
13
4
Safety Hazards:
7
5
Technical Challenges:
28
6
Additional Risk Factors
32
To design a conventional SIMULINK model for the risk associated with management of engineering projects
Fig 1 conventional SIMULINK model for the risk associated with management of engineering projects
Technical Challenges
In 1
Out1
Subsystem6
In1
In2
Out1
Subsystem1
In1
In2
Out1
Out2
Scope Creep
In 1
Out1
Schedule Delays :
In 1
Out1
Safety Hazards
In 1
Out1
Kasami
Sequence
Generator
Kasami
Sequence
Generator
Display 5
32
Display 4
28
Display 3
7
Display 2
13
Display 1
8
Display
12
CONVENTIONAL
1
Budget Overruns
In 1
Out1
Additional Risk Factors
In1
Out1
AWGN
Channel 1
AWGN
AWGN
Channel
AWGN
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The results obtained are as shown in figures9 through14.
To design management rule base that will reduce the percentage of the risk associated with management of
engineering projects
Fig 2 designed management fuzzy inference system that will reduce the percentage of the risk associated
with management of engineering projects
Fig 2 has six inputs of the core causes of risk associated with management of engineering projects. These inputs
ate Scope Creep, Schedule Delays, Budget Overruns, Safety Hazards, Technical Challenges and Additional Risk
Factors. It also has an output result. This was done in Fuzzy tool box in MATLAB environment
Fig 3 designed management rule base that will reduce the percentage of the risk associated with
management of engineering projects
These rules are comprehensively written in table 2
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Table 2 comprehensive management rule base that will reduce the percentage of the risk associated with
management of engineering projects
1
if Scope
Creep is
high
reduce
And
Schedule
Delays is
high
reduce
And
Budget
Overruns is
high reduce
And Safety
Hazards is
high
reduce
And
Technical
Challenges
is high
reduce
And
Additional
Risk Factors
is high
reduce
Then result is
bad high risk
associated
with
Engineering
management
project
2
if Scope
Creep is
partially
high
reduce
And
Schedule
Delays is
partially
high
reduce
And
Budget
Overruns is
partially
high reduce
And Safety
Hazards is
partially
high
reduce
And
Technical
Challenges
is partially
high reduce
And
Additional
Risk Factors
partially
is high
reduce
Then result is
bad high risk
associated
with
Engineering
management
project
3
if Scope
Creep is
low
maintain
And
Schedule
Delays is
low
maintain
And
Budget
Overruns is
low
maintain
And Safety
Hazards is
low
maintain
And
Technical
Challenges
is low
maintain
And
Additional
Risk Factors
is low
maintain
Then result is
good no risk
associated
with
Engineering
management
project
Fig 4 management rule base in operation to reduce the percentage of the risk associated with
management of engineering projects
To train ANN in the management rule base to enhance the efficacy of reducing the percentage of the risk
associated with management of engineering projects
Out 1
1
Demux
Demux
Demux
Zero Firing Strength ?
>
0
Total Firing
Strength
TechnicalChallenges
Input MF
Switch
ScopeCreep
Input MF
ScheduleDelays :
Input MF
SafetyHazards
Input MF
Rule 3
Rule
Rule 2
Rule
Rule 1
Rule
Result
Output MF
MidRange
-C-
Demu
Defuzzification 1
COA
BudgetOverruns
Input MF
AggMethod 1
max
AdditionalRiskFactors
Input MF
In 1
1
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Fig 5 trained ANN in the management rule base to enhance the efficacy of reducing the percentage of
the risk associated with management of engineering projects
In fig 5, it shows that ANN was trained twenty times in the three management rules
Fig 6 trained ANN in the management rule base to enhance the efficacy of reducing the percentage of
the risk associated with management of engineering projects This was done in MATLAB environment
In fig 6 ANN was trained twenty times in the three management rules 20 x 3 =60 to give sixty neurons that look
exactly like human brain that performs effectively what it is instructed to do.
Fig 7 result obtained at the course of the training.
To develop an algorithm that will implement the process.
1. Characterize the causes of the risk associated with management of engineering projects
number of training
20
X(2Y)
Graph
TRAINING TIMES
20
Plant
(Magnet Levitation )
N
S
NARMA -L2 Controller
Plant
Output
Reference
Control
Signal
f g
Double click
here for
Simulink Help
?
Clock
Current
Position
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0.2
0.3
0.4
0.5
0.6
0.7
0.8
W(i,1)
W(i,2)
REDUCING RISK ASSOCIATED WITH MANAGEMENT OF ENGINEERING PROJECTS USING INTELLIGENT BASED CONTROL SYSTEM
y{1}x{1}
Input 1
Neural Network
x{1}
y {1}
Display
1.2
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2. Establish the causes of the risk associated with management of engineering projects
3. Identify high percentage of Scope Creep.
4. Identify high percentage of Schedule Delays.
5. Identify high percentage of Budget Overruns.
6. Identify high percentage of Safety Hazards
7. Identify high percentage of Technical Challenges
8. Identify high percentage of Additional Risk Factors
9. Design a conventional SIMULINK model for the risk associated with management of engineering
projects and integrate 3 through 8
10. Design management rule base that will reduce the percentage of the risk associated with
management of engineering projects
11. train ANN in the management rule base to enhance the efficacy of reducing the percentage of the
risk associated with management of engineering projects
12. Integrate 10 and 11.
13. Integrate 12 in 9.
14. Do the high percentage of the established causes of the risk associated with management of
engineering projects reduce?.
15. If NO go to 13
16. If YES go to 17
17. Reduced risk associated with management of engineering projects
18. Stop.
19. End
To design a SIMULINK model for reducing risk associated with management of engineering projects using
intelligent based control system
Fig 8 designed SIMULINK model for reducing risk associated with management of engineering projects
using intelligent based control system
The results obtained after the simulation of fig 8 are as shown in figures 9 through 14
To validate and justify the percentage improvement in the reduction of risk associated with management of
engineering projects with and without intelligent based control system.
To find percentage reduction of Scope Creep causes of risk associated with management of engineering projects
with intelligent based control system
Conventional Scope Creep causes of risk associated MEP =12%
Intelligent based control system Scope Creep causes of risk associated MEP =10.41%
y{1}
x{1}
Input 1
Technical Challenges
In1
Out1
Subsystem6
In 1
In 2
Out1
Out2
Subsystem1
In 1
In 2
Out1
Out2
Scope Creep
In 1
Out1
Schedule Delays :
In 1
Out1
Safety Hazards
In1
Out1
Neural Network
x{1}
y {1}
Kasami
Sequence
Generator
Kasami
Sequence
Generator
Fuzzy Logic
Controller
0.4153
Display 7
1.33
Display 6
1.2
Display 5
27 .75
Display 4
24 .28
Display 3
6.07
Display 2
11.27
Display 1
6.937
Display
10 .41
Budget Overruns
In 1
Out1
Additional Risk Factors
In 1
Out1
Add
AWGN
Channel 1
AWGN
AWGN
Channel
AWGN
In 1
1
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% reduction of Scope Creep causes of risk associated with management of engineering projects with intelligent
based control system =
Conventional Scope Creep causes of risk associated MEP - Intelligent based control system Scope Creep causes
of risk associated MEP
% reduction of Scope Creep causes of risk associated with management of engineering projects with intelligent
based control system = 12 %– 10.4%
% reduction of Scope Creep causes of risk associated with management of engineering projects with intelligent
based control system = 1.6%
RESULT AND DISCUSSION
Table 3 comparisons of conventional and intelligent based control system Scope Creep causes of risk associated
with management of engineering project
Time(s)
Conventional Scope Creep
causes of risk associated with
management of engineering
projects (%)
Intelligent based control
system Scope Creep causes of
risk associated with
management of engineering
projects (%)
1
12
10.4
2
12
10.4
3
12
10.4
4
12
10.4
10
12
10.4
Fig 9 comparisons of conventional and intelligent based control system Scope Creep causes of risk
associated with management of engineering project
1 2 3 4 5 6 7 8 9 10
10.4
10.6
10.8
11
11.2
11.4
11.6
11.8
12
pe Creep causes of risk associated with management of engineering projec
Time(s)
Conventional Scope Creep causes of risk associated with management of engineering projects (%)
Intelligent based control system Scope Creep causes of risk associated with management of engineering projects (%)
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In fig 9 the conventional Scope Creep that caused risk associated with management of engineering project
was12%. On the other hand, when an intelligent based control system was introduced in the system, the
percentage of risk observed in management of engineering project was reduced to10.4%.
Table 4 comparisons of conventional andintelligent based control system Schedule Delays causes of risk
associated with management of engineering project
Time(s)
ConventionalSchedule Delays
causes of risk associated with
management of engineering
projects (%)
Intelligent based control
system Schedule Delays
causes of risk associated with
management of engineering
projects (%)
1
8
6.94
2
8
6.94
3
8
6.94
4
8
6.94
10
8
6.94
Fig 10 comparisons of conventional andintelligent based control system Schedule Delays causes of risk
associated with management of engineering project
The conventional Schedule Delays that caused risk associated with management of engineering projects was
8%.Meanwhile, when an intelligent based control system was introduced in the system the Schedule Delays that
caused a lot of risk in management of engineering projects drastically reduced to 6.94%.
CONCLUSION
management of engineering projects, designing a conventional SIMULINK model for the risk associated with
management of engineering projects, designing management rule base that will reduce the percentage of the risk
associated with management of engineering projects, training ANN in the management rule base to enhance the
efficacy of reducing the percentage of the risk associated with management of engineering projects, developing
1 2 3 4 5 6 7 8 9 10
6.8
7
7.2
7.4
7.6
7.8
8
ule Delays causes of risk associated with management of engineering proje
Time(s)
Conventional Schedule Delays causes of risk associated with management of engineering projects (%)
Intelligent based control system Schedule Delays causes of risk associated with management of engineering projects (%
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an algorithm that will implement the process, designing a SIMULINK model for reducing risk associated with
management of engineering projects using intelligent based control system and validating and justifying the
percentage improvement in the reduction of risk associated with management of engineering projects with and
without intelligent based control system.The results obtained are the conventional Scope Creep that caused risk
associated with management of engineering project was12%. On the other hand, when an intelligent based
control system was introduced in the system, the percentage of risk observed in management of engineering
project was reduced to10.4%, the conventional Safety Hazards that caused high risk associated with management
of engineering projects was7%. However, when an intelligent based control system was integrated in the system
the risk associated with management of engineering projects reduced to6.07% and the conventional Additional
Risk Factors that caused risk associated with management of engineering projects was32% while when an
intelligent based control system was imbibed in the system, it wittingly reduced to27.75%. Finally, with these
results obtained, it shows that the percentage improvement in the reduction of risk associated with management
of engineering projects when an intelligent based control system was integrated in the system over the
conventional competitor was 4.25%.
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