AL-Rafidain Engineering Journal (AREJ)

In recent decades, the number, and height of buildings have increased dramatically. As height has become a kind of
urbanization all over the world, people tend to build high-rise buildings, but the higher the building height, the greater the
risk. Safety is one of the main tributaries on which the local economy is based, and it occupies an important place at the
level of international organizations anywhere to protect lives and property. These towers accommodate a large population
density, and given the large number of floors, the difficulty of controlling the spread of fire and evacuating the building's
occupants appears. Therefore, the study aims primarily at a general evaluation of the available research dealing with
design variables in high-rise buildings and their effects on the spread of fire, whether in terms of temperature, smoke, or
toxic gases. The architect’s role is activated through the development and improvement of design methods and the efficiency
of the building's functional performance and protection measures when fire hazards occur in these buildings.

Fiber Reinforced Polymer (FRP) materials have become more popular according to contemporary
developments in civil engineering applications. These materials have been used to repair and rehabilitate traditional
structures. In turn, the majority of currently used applications are the result of research and recommendations made by
fiber composite manufacturers or the experience of the designer. Therefore, optimization techniques try to achieve the
best design under different conditions in structural design. An extensive search was conducted on existing research in the
literature on applying optimization techniques such as artificial intelligence (AI) methods to reinforced concrete
members that were externally strengthened with FRP materials. This paper provides a concise assessment of many
studies that have been done in the literature on the behavior and strength of fiber-reinforced concrete elements,
particularly in shear and bending scenarios. Each study's methodology and key findings are summarized.

The soil reinforcement technique is used to improve the engineering characteristics of soils. In this technique,
various types of reinforcement elements have been used in constructions for a very long time. As mentioned in the previous
literature, the use of synthetic fibers and plastic waste materials has shown promising results. when these fibers inclusion in
soils, significant enhancements are demonstrated in the overall mechanical properties (especially strength properties) of
reinforced soils. Sequentially, the randomly distributing methods of fibers have involved increasing attention in geotechnical
applications due to its efficiency in improving soil properties. In this study, the soil-reinforcement mechanism, types of fibers
(synthetic and plastic waste material), and applications of these fibers in various types of constructions were reviewed. The
advantages and disadvantages of synthetic and plastic waste material fibers were also discussed. As well, some
recommendations were also mentioned in this review paper in order to fully understand the behavior of reinforced soils if
these recommendations were taken into account.

In the pursuit of a sustainable environment, and facing the threat of the continuous growth of solid waste in
Iraq, this study investigated the impact of using blowdown, a carbon-sulfur byproduct material of sulfur purification, as a
mineral filler on durability and rutting resistance in dense-graded asphalt mixtures using Styrene-Butadiene-Styrene (SBS)-
modified asphalt. Three different blowdown rates (4%, 5%, and 6% by weight of aggregate) were used as potential calcium
carbonate (CaCO3) replacements. Laboratory tests and parameters, including Marshall stability, indirect tensile strength,
tensile strength ratio (durability), and deformation strength, were conducted, and the results were analyzed statistically
using Minitab software. The results showed that using 5% blowdown in SBS-modified asphalt mixtures increases the
deformation strength, indirect tensile strength at 25 °C, Marshall stability, and Marshall quotient by 51.23%, 0.21%,
14.27%, and 31.44%, respectively, compared to the control mixture that contains CaCO3 filler indicating that using of
blowdown as a filler in asphalt mixtures may offer benefits such as enhancing the cracking and rutting resistance of asphalt
mixtures while meeting Marshall properties and moisture susceptibility standards and the potential to provide financial
advantages and reduce landfill use.

In all fault detection techniques, fault signal feature extraction is crucial and challenging. Convolutional
neural network and continuous wavelet transform (CNN)-the based technique of SLGF detection in distribution power
system protected by Petersen coil proposed in this paper. By using the CWT on the zero-sequence current signals of the
faulty feeder and healthy feeders, time-frequency RGB scale images acquired. A few RGB scale pictures under different
types of faults circumstances, which will extract characteristics of RGB scale image adaptively, trained that which is
CNN. A trained CNN could extract features and detect faulty feeder simultaneously. The distribution power system
protected by Petersen coil simulated in MATLAB simulated and record the Zero Sequence Current ZSC and analyzed it
by Orange big mining tool. The efficacy and the performance the suggested method for detecting faulty feeders are
compared and confirmed under various faults scenarios, two methods for identifying faulty feeders on conventional
machine learning and artificial feature extraction for comparison, concluded that The CNN best to detected the fault in
different condition, for Test 1and Test 2 were classification accuracy 100%, and Test 3 was 99.5%, and Test 4 was
70.9%.

The current project dealing with communication system through adoption of the particular digital modulation
by using solid state components as a semiconductor device, using the phase- Shift keying (PSK) signal that has been adopted
by combined coding with phase modulation. To increase the coded signal's minimum free distance as an objective of the
work, by using the coding (Channel Coding) is a preferred approach to enhance the signal to noise ratio (SNR) performance
of the transmission of digital signals, and for completing of the project task, a simulation methodology has been
implemented the required investigation. The results reveal that the minimum free distance is equal the root of 2, i.e D=
1.414, the output encoder with rate of 2/3 i.e 8-PSK signal and the change of phase of the signal point does affect the
minimum free distance of the coded signal.

The fault diagnosis of electric vehicle motors is one of the exciting topics, and machine learning-based artificial
intelligence proved its worth in this field. The primitive methods of machine learning, such as the support vector machine
(SVM) and Artificial nural network (ANN) suffered from feature extraction problems, the efficiency of the system depended
on the quality of these extracted features until deep learning and deep neural networks came to solve This problem,
Although the efficient performance of the deep neural network, it needs excellent experience in selecting parameters and
building the structure of the neural network. The emergence of deep reinforcement learning, capable of handling raw data
directly and constructing end-to-end systems to link raw fault data with its corresponding mode, represents a significant
advancement in the field of machine learning. Furthermore, deep reinforcement learning exhibits greater intelligence
compared to previous methods. In this Research, Deep reinforcement learning will be applied in diagnosing inter-turn
short circuit faults and finding the level of fault in the built-in wheel permanent magnet synchronous motor of electric
vehicles. The proposed method proved highly effective for detecting faults, with an efficiency of 99.9 % and has a promising
future in building a general system capable of predicting faults in the early stages.

Fast Fourier Transform (FFT) is a commonly used method in electronic support systems for frequency
parameter estimation. If the frequency of the radar signal is not an exact multiple of the frequency resolution, the
frequency of this signal will usually appear in an inter-line position when FFT is applied. To improve the accuracy of the
estimated frequency, interpolation techniques are used to find the peak between two spectral lines. In this study, the
frequency of the radar signal is estimated by employing three different interpolation techniques (Ding, Voglewede and
Hanning window based interpolation) to the output obtained by applying N-point FFT to the intermediate frequency (IF)
signal. In addition, unlike the literature, the behavior of signals contaminated with Laplace noise as well as Gaussian
noise were analyzed with these three techniques and their performances were compared. From the analysis results, Ding
and Voglewede techniques reduced error rate at all frequency. However, the Hanning window-based interpolation
method improved the frequency accuracy values at 500MHz and 750MHz, but it increased the error at 250MHz and
1000MHz frequencies. The error rates of the estimated frequencies can be sorted from the lowest to the highest as
follows: Ding, Voglewede and Hanning window based interpolation.

In recent years, Switched Reluctance Machines (SRMs) have attracted increasing interest in Electric Vehicles
(EVs) applications due to the fluctuating prices of rare earth magnets (Permenant Magnets - PMs) used in Permanent
Magnet Synchronous Machines. PMSMs). It is also characterized by a solid and strong construction, in addition to that it
works at high speeds and high temperatures. However, the negatives are the loud noise and high torque ripples. In
addition to the relatively low torque density, it poses great challenges for researchers in finding appropriate solutions.
Where engineering structure optimization techniques are used to overcome these challenges and enable impedance
switched machines (SRMs) to compete with permanent magnet synchronous machines (PMSMs). On the other hand, it is
possible to improve the distribution of the materials used in the engineering structure of the key impedance machines
within a certain design space within the machine structure by using the engineering structure improvement techniques.
This study presents a review on techniques for improving the geometry of SRMs to improve machine performance, since
optimizing machine geometry and material distribution at the design stage is of great importance in improving the
performance and characteristics of SRMs

In this work, linear and nonlinear designs of active suspension models are proposed to develop and improve
quarter-car systems. To simplify stability assessment, a second-order system is proposed for both linear and nonlinear
cases. The linear system consists of mass, spring, and damper components, while the nonlinear system includes the same
components with additional nonlinear parts for stiffness and damper. Moreover, the state space of the linear and nonlinear
is presented as apreparatory step before applying the analysis methods to validate the models. After that, the stability of
linear and nonlinear systems is characterized using Matlab simulations to compare suspension performance parameters
such as rise time (tr), settling time (ts), and peak overshoot (Mp). The simulation results of the linear system for each of tr,
ts, Mp were0.097612sec, 2.3 sec, and 0.3839 cm, respectively, while the results of the nonlinear system were 0.52237 sec,
20.16 sec, and 0.3064 cm, respectively. In addition, the results for linear and nonlinear systems indicate the need to improve
ride comfort and road handling using PID controller design. Consequently, it is possible to reach a better compromise
than is possible using pure elements, without a controller). Finally, the active suspension system for both linear and
nonlinear systems is improved through the application of a PID controller, resulting in the following values for the linear
system: tr = 0.10721sec, ts = 1.693 sec, and Mp = 0.3682cm. Similarly, the nonlinear system showed improved
performance with tr = 0.259775sec, ts = 1.325 sec, and Mp = 0.0734cm.

Tapered beams are more effective than uniform beams because they offer a superior distribution of mass and
strength and also satisfy unique functional requirements in many engineering applications. This study calculates the
mode shapes and natural frequencies of straight and various tapered Euler-Bernoulli beams by finite elements using
ANSYS 16.5 software. The dynamic response for a cantilever beam was obtained for different taper angles. The results
were compared with the dynamic response of a straight cantilever beam, to show the effect of the taper ratio on the
dynamic response of the cantilever beam, when its volume and mass are constant. The results showed that the natural
frequencies were increased as the taper angle increases, and the torsional natural frequencies were shifted from the
fourth natural frequency to the fifth one as the taper angle increases.

Thermal performance parameters for a ground-coupled warehouse cooling system with an underground
Thermal Energy Storage (TES) tank are investigated in this study. The system comprises a warehouse, a cooling unit, and
an underground TES tank. A MATLAB program was created to evaluate the system's performance on an hourly basis,
considering various design parameters and operational conditions. To solve the hourly heat transfer to the surrounding
TES tank, the similarity transformation and Duhamel's superposition principle are employed. The results indicate that
the volume of the TES tank and the type of earth surrounding it are critical factors affecting the system's performance.
Among the tested surrounding materials (granite, limestone, and clay earth types), granite exhibits the highest system
performance. The results also reveal that the water temperature in the TES tank reaches a maximum of 25.33 °C in July,
significantly below the maximum ambient air temperature of 45.67 °C, resulting in a higher COP of 3.42. This
performance is achieved with a TES tank volume of 600 m3, a Carnot efficiency of 40%, and using granite as the earth
type. Moreover, the system demonstrates periodic operation starting from the 9th year onwards, continuing for 15 years
of operation. These improvements lead to significant energy savings and reduced environmental effects.

The effect of erosion on the economy is widely acknowledged, with solid particle erosion (SPE) being recognized
as one of the most significant forms of erosion resulting from the collision of solid particles with materials. While studying
and comprehending erosion can be challenging, researchers have dedicated their efforts to this field and have devised
models to anticipate the erosion rate of material elimination from the surface of an object, based on the material's response
to solid particle impact. Most erosion models for composite materials take into account various physical and mechanical
properties of the material, such as its density, porosity, modulus, strength, and fracture toughness. They also examine the
characteristics of the particles that cause erosion, such as their size, shape, and hardness. Erosion models for composite
materials are used to study the impact of different factors on erosion, such as the effect of particle size, velocity, and
impingement angle. They are also used to optimize the design of composite materials and structures for specific
applications and to evaluate the performance of protective coatings and erosion-resistant materials. Erosion models for
composite materials can be either empirical or process-based. Empirical models use statistical relationships to predict
erosion rates based on observed data, such as the size and shape of the particles, the velocity of the impacting particles,
and the impingement angle. While the process-based models, on the other hand, use mathematical equations to simulate
the physical processes that drive erosion, such as the deformation and fracture behaviour of the composite material under
impact loading. Overall, erosion models for composite materials provide a valuable tool for understanding and predicting
the complex processes that drive the erosion of composite materials, and for developing effective strategies to mitigate its
impact on their performance and durability in various applications.

Considering the potential impacts of climate change on meteorological data is likely to be more
reliable in the design of hydrological systems. One of the recognized methods for the assessment the
influence of climate changes by analyzing the weather data. The study aims were inspecting the maximum,
minimum temperature and rainfall data for any discernible trends in the city of Delhi between 1901 – 2010
periods. Mann-Kendall non-parametric test including correlation was adopted to analyze the available data
for three periods: 1901-2010; 1901-1955 and 1956-2010. Annual, monsoonal and pre-monsoon rainfalls
have been examined for trends analysis. Results show that the maximum and minimum annual temperatures
exhibit an increasing and decreasing trend, respectively. On the other hand, monsoonal and annual rainfall
shows increasing trends. A strong negative correlation is noted between annual rainfall and annual
maximum temperature for 1901-1955 and 1956-2010 periods as well as for the entire period of analysis. A
strong negative correlation is also noted between the monsoonal rainfall and the annual maximum
temperature. In addition to previous findings, a noticeable correlation is shown between minimum
temperature and annual rainfall for 1956-2010 periods of records. However, a weak and negative
correlation was reported between minimum temperature and annual rainfall for the periods between 1901-
1955 and 1956-2010.

The daily flow of rivers is one of the most important components of the hydrological cycle and plays an important
role in the planning and management of various water resources projects, as the process of predicting such flow is
very necessary in the operation of reservoirs, planning to prevent flooding and estimating water abundance or
scarcity. This study aims to use two types of neural network models to predict the daily flow to the Great Zab river.
The first type is the neural network of Feed Forward Back Propagation (FFBP) and the second one is the neural
network of Multilayer Perceptron (MLP). Time series data of daily flow for the period (2012 – 2021) were used for
training and testing the artificial neural networks. The results showed that the neural network of MLP of (3-14-7-1)
structure better than FFBP based on the values of determination coefficient (R2) which were 0.91 and 0.86 and the
values of root mean square errors (RMSE) which were 69.9 and 51.7 for both models, respectively. The effect of
including a time index within the inputs of the networks is also investigated by using different input patterns. It was
found that there is a noticeable increase in R2 and a decrease in RMSE values when using three previous days
compared to other delays. In addition, the current study examined the ability of the proposed models to predict
minimum and maximum flows of the Great Zab River.

In recent years, climatic changes have had a greater impact on the hydrological cycle, leading to continuous
changes in climate on both temporal and spatial scales. Therefore, this study aimed to verify the credibility and
homogeneity of the data, so when conducting any study in the field of climate and hydrological change, the homogeneity
of the data used must be tested. In the current study, eight climatic stations distributed in Nineveh Governorate were
selected, using climatic data represented by (rainfall, maximum and minimum temperatures, and maximum and minimum
humidity) for the time period 1990-2020. Four statistical methods were used, namely, Von Neumann test (VONT),
Standard Normal Homogeneity test (SNHT), Buishand test (BRT) and the Pettitt test at a significance level of 5%. The
results showed that the monthly rainfall was homogeneous for all stations except for three months (2, 2, 11) for the
stations of Tal-Abta, Ba’aj, and Al-Sheikhan. As for the temperature and humidity, they were heterogeneous for most of
the stations, as the percentages for months that were heterogeneous in temperature reached 35% and 42% for the
maximum and minimum, respectively. As for the humidity, the percentage of the heterogeneous months were 18% and
14% for the maximum and minimum, respectively. The study showed that the SNHT and VON tests are the most sensitive
to the breakpoint and the Pettitt test is the least sensitive in most tests. The heterogeneous climatic data were also
corrected by using the double mass curve method and converted into homogeneous climatic data.

Erosion is defined as the displacement of the bottom materials due to the flow energy and moving them to the
farthest distance, which negatively affects the stability of the hydraulic structures. In this research, the effect of the
properties of the screen walls on scour in open channels was studied. Therefore, the study included evaluating the effect of
the diameters of the screen walls openings (φ), wall thickness (t), and the ratio of the distance of the screen walls from the
gate to the height of the opening below the gate on the scour process. A total of 140 experiments were conducted in concrete
channel including one size of gravel, two opening heights (4 and 5 cm) at the bottom of the gate, three diameters of the
screen wall openings (0.8, 1.2 and 1.6) cm, two thicknesses of the screen wall (0.4 and 0.8) cm, one value of screen wall
porosity (40%), using five discharges in the range of (50.06-27.98) l/sec when x=40d, 45d, and in the range of (44.8-23.68)
l/sec when x=50d, 56.25d. The results showed that the screen walls with a diameter of (1.2) cm resulted in the lowest
volume of the scour hole while experiments conducted without screen walls led to largest volume of scour hole. Moreover,
the screen walls of (0.8) cm thickness leads to less volume of the scour hole as compared to those for the screen walls of
(0.4) cm, especially for high discharge i.e. for high values of Fr number. Two empirical relations were proposed to compute
the scour depth ratio and its length in term of dimensionless variables using dimension analysis.

Bridges are built to facilitate transportation between the two banks of the rivers and waterways. They are usually
supported by piers with geometric shapes that differ from one bridge to another, such as a complex pier. Due to the
intersection between bridge piers and water flow, many water vortices are generated, causing local scour around the bridge
piers. Local scour around the complex piers is affected by many factors, including the geometry of the piers and other
hydraulic factors. In this study, a number of laboratory experiments were conducted to investigate the effect of the pile cap
elevation and water depth on local scour around complex pier piers using materials with a regular gradient and under the
condition of sediment-free water flow. Eight ratios of the pile cap elevation to the cap thickness Z/Tpc were studied, ranging
from -32% to 50%, and six different flow depths (y) between 10.5 cm and 18 cm. The results showed that the maximum scour
depth was recorded to be 10.8 cm when the pile cap was partially covered with a sand bed (Z/TPC = 50%), the depth of
scour increased with the increase in water depth up to 13.5 cm, then the effect of the increase in water depth was slight,
although Increasing the intensity of the vortices due to the presence of the piles covering that prevent the impact of these
vortices from reaching the soil surface.

The paper presents a comprehensive methodology for simulating transient flow in pipeline systems induced by
valve closure, using the method of characteristics with an unsteady friction model. The research focuses on the
instantaneous acceleration-based (IAB) model, a mathematical model employed to describe water hammer behavior in
pipeline systems. The methodology includes the development of a mathematical model based on governing fluid dynamics
equations, numerical simulation using the proposed model, and validation against experimental data obtained from
laboratory-scale pipelines. The study compares the performance of steady and unsteady friction models, revealing the
limitations and strengths of each in simulating water hammer events. The paper also discusses the estimation of the
damping coefficient (k) using trial-and-error and Reddy's analytical method, and the influence of numerical parameters
on the IAB model performance. The numerical results demonstrate good agreement with experimental data, validating the
proposed model's accuracy. The methodology presented in this paper can serve as a valuable tool for analyzing and
designing pipeline systems subject to water hammer phenomena. It provides insights into transient flow characteristics
induced by valve closure and assists in identifying appropriate mitigation measures to prevent damage to the pipeline
system.

Universities all across the world give academic accreditation for degree programs significant attention. This
makes sense given that accreditation not only improves the programs' content and delivery but also enables these
institutions to recruit teachers and staff of the highest caliber. The Accreditation Board for Engineering and Technology
(ABET) is one reputable organization with the authority to accredit Engineering programs. A rising number of academic
institutions are requesting ABET accreditation for their computing programs in an effort to raise the standard of their
academic programs and student enrollment. This paper's additional value is that it serves as a road map for institutions
and their management as they prepare to begin the process of accrediting their computing (or other) programs. The lack
of information on the mechanics of implementation presents a problem because it leads to confusion and resource waste,
especially in the early stages. Additionally, there is a dearth of literature accessible describing methodology and the use
of effective accreditation strategies for computer programs. In light of this, it is necessary to record the methodology,
instructional practices, and tactics used by various institutes as they work towards accreditation.

Physical therapy is an important form of rehabilitation for patients suffering from a variety of disorders. Since
professional physiotherapists are not always available, there is a need to introduce an intelligent system that assets the
patients to perform the exercise by themselves. Any evaluation system consists of hardware interfacing, computers,
processing, and evaluation tools. These tools made it easier to build methods for automating the evaluation of patient
performance and advancement in functional rehabilitation. In this research, about one hundred research papers are
classified according to the above-mentioned system parts. The review of current tools for capturing rehabilitative motions
shows that the Kinect camera has been used in about 35% of the studies. This review concentrates on using machine
learning techniques to evaluate motion in rehabilitation. The most relevant research for physiotherapy evaluation using
deep learning have shown that the Convolutional Neural Network (CNN) is widely used by 44% of the researcher. A useful
overview the collection of the reference datasets illuminates that the KIMORE dataset is popular and used by 38% as
compared with other types of datasets. The advanced literature in the present peer-reviewed paper (2016–2022), includes
primary studies and organized reviews.

Day by day, machine learning and deep learning reduce the efforts needed by humans in many fields.
Handwriting recognition is one such field. In Handwriting Recognition (HWR), a machine can interpret and recognize
handwritten input from different sources like papers, touch screens, images, etc. by interpreting it into machine-readable
formats. Arab countries often use Arabic digits in addition to English digits. In banks, business applications, etc. This
article discusses four methods to recognize Arabic/English handwritten digits which are: random forest (RF), multi-layer
perceptrons (MLPs), convolutional neural network (CNN), and CNN-RF. These methods were implemented with the help
of the MNIST and MADBase datasets and the results appear that in comparison with the other algorithms, the highest
accuracy was obtained by the Convolutional Neural Network (CNN) with a value of 99.11%.

Stress and sudden difficult situations have raised the risks of accidents down the roads. The drivers’ attention
might be distracted out in seconds under unexpected circumstances, which could take place due to bad weather, vision
problems, fatigue for long driving hours, damaged or broken Traffic light , and even children's noise inside the car. In this
paper, I proposed to develop a special colourful Deep Back Propagation Neural Network to enhance drivers’ attention by
observing different traffic light cases using a suggested smart binary matching machine system in Python. The smart
machine system will analyse and identify the real Traffic light from art signs, broken or damaged ones; in addition to
pedestrian signs based on a Database symbols for each case, which have taken the basic Traffic light and signs, and
developed them to damaged cases or unreal one, before making the right decision by the learned network, then send an
enhanced feedback signal to the driver. The algorithm consisted of accurate image processing steps, with two long stages
of full contents features extraction vectors to be handled by Red-Yellow-Green Shallow and Deep Back Propagation
Neural Networks (SBPNN) and (DBPNN) for each complex case. As a result, the algorithm rated a high accuracy of
100%, which is the most important factor to maintain safety, recoding the true label output as 1-value, with a predicated
tested ouput 1.0-value. The suggested system does not replace the driver's one decision, yet it is an enhancing backup
classification and recognition system before things move out of control. The feedback signal calculated based on reducing
costs for 2500 iterations with The leas minimum value 000012,and can be developed as a voice signal warning Message,
to increase the awareness of the drivers, besides the warning text on the screen.

Systems for object detection and tracking are becoming increasingly important in practical applications today.
Many research and development groups are interested in improving the performance of such systems, and numerous
methods have been developed and proposed. Additionally, computer vision is constantly developing and implemented on
reconfigurable and embedded systems. The purpose of this study is to present past and recent research works in the field
of visual tracking systems that used FPGA and FPGA-SoC platforms. The study includes a brief description of several
popular algorithms related to the main characteristics and in which field is preferred. Resource utilization was also
considered in this study to present the most and the least resources used to implement different algorithms. The study
found that flip-flops (FF) and lookup tables (LUT) are usually used, while BRAM, DSP, and multipliers had the lowest
percentage utilization. Due to the recent development in the production of advanced processing systems, there is an
increase focusing on employing FPGA-SoC platforms in visual surveillance systems. The reason behind that is their
ability to implement complex processing using both hardware and software co-design to gain high performance in less
design time compared with using only FPGA-based platforms

Artificial intelligence (AI) can be a powerful tool in addressing some of humanity's biggest challenges named
global climate change. Monitoring climate change involves large and ever-evolving data sets. In order to track changes
in climatic conditions in real time, address vulnerabilities to reduce them, and provide essential opportunities for
humanity to find solutions that can have a positive impact on our planet more quickly, artificial intelligence systems can
assist in the analysis of sets of environmental data. Even though AI is only one tool in the difficult analysis of the factors
causing climate change, its capacity to handle vast amounts of data, find patterns, and occasionally anticipate data
affords us the chance to better comprehend the ecosystem.

In recent years, microplastics (MPs) contamination has become a serious concern in water distribution systems.
Few studies on microplastics' numbers and their characteristics have been described. The current study focused on the
abundance, characteristics, and polymer type of plastic particles in the tap water within 16 sites on the right side of
Mosul city, including eight areas equipped by the Alayman aljadid drinking water treatment plant (AYJ-DWTP) and eight
others that supplied by water from the Alayman alqadim plant (AYQ-DWTP). A stereomicroscope (SM) is used to detect
MPs abundance and morphology by capturing images of plastic particles. Fourier transform infrared spectroscopy
(FTIR) was applied to distinguish polymer types. In this study, results elucidated that the presence of microplastics in the
tap water of both the AYJ-water distribution network (WDN) and AYG-WDN ranged from 28 to 69 MPs/L, with an
average of 45 ± 10 MPs/L. Fibres and fragments were the dominant form of microplastics, estimated for (89-91%) of
total particles. The transparent colour of particles was the most abundant. Polyvinylchloride (PVC) and Nylon
Polyamide (PA) were the most common polymers of MPs around (39%) and (21%), respectively. Statistical analysis was
applied by ANOVA test. The PVC risk index was very high.