Mohd Faiz Mohammad Zaki
Thumbnail Image
Preferred name
Mohd Faiz Mohammad Zaki
Official Name
Mohd Faiz , Mohd Faiz
Alternative Name
Zaki, M. F.M.
Zaki, Mohd Faiz Muhammad
Main Affiliation
Scopus Author ID
57008394600
57209687959
Researcher ID
IQB-2030-2023

Research Output

Filters

2 1
5
Reset filters

Settings
Now showing 1 - 5 of 5
  • Publication
    Influence of Twin Tunnel Depth in Numerical Ground Movement Prediction Using Mohr Coulomb and Hardening Soil Model
    ( 2020-01-01)
    Govindasamy D.
    ;
    Ismail M.A.M.
    ;
    Mohd Faiz Mohammad Zaki
    In urban area tunnel construction growing rapidly due to urbanization and increase in population rate. Tunnels are excavated at different depths from ground surface, but this will cause ground settlement in the excavation area which impact the surrounding structures. This paper is focused on effects of the surface ground movement prediction using numerical approach at various depth with Mohr Coulomb (MC) model and Hardening Soil (HS) model. Kenny Hill Formation used as the study area particularly chainage NB 1960. In this paper, the shape and pattern of the ground movement that obtained from simulation in PLAXIS 2D using MC model was compared with HS model output. Various tunnel depth location was used in the analysis such as real site condition tunnel depth and the relationship of 1d, 2d, 3d and 4d where d is the diameter of the tunnel. From this study, it can be seen that when tunnel depth increases the surface settlement decreases for both MC and HS model. But, the MC model’s ground surface settlements were undoubtedly lower than HS model.
  • Publication
    Correlation Between SPT and PMT for Sandy Silt: A Case Study from Kuala Lumpur, Malaysia
    ( 2020-10-01)
    Mohd Faiz Mohammad Zaki
    ;
    Ismail M.A.M.
    ;
    Govindasamy D.
    In this study, the pressuremeter modulus (EM) and the unload–reload modulus (Eur) consisted of a wide range of data were correlated to blow counts (N60) using a maximum of 50 blows/300 mm and the extrapolated N60 of 300 blows/300 mm. A 3D model and statistical analysis were used to provide relevant justifications for the selection of this extrapolation method, considering that N60 was limited to 50 blows. In generating a 3D model, the N60 profile was developed using the inverse distance weighting method for predicting unsampled data between boreholes. Correlations were established for the sandy silt soil type that was observed as the dominant lithology in the Klang Valley Mass Rapid Transit line 1 project in Kuala Lumpur, Malaysia. A total 52 pressuremeter test and standard penetration test data pairs were obtained at depths ranging from 6 to 41.7 m within the Kenny Hill Formation (KHF) and the contact zone between the KHF and the limestone formation. This contact zone has shown distinct geological features with the characteristic of a lower N60 value underlying stiff strata. According to the EM/Eur ratio, the maximum value of 7 indicated that this zone is unpredicted in initial and unload–reload stiffness compared to the non-contact zone (the KHF only), with a maximum ratio of 3. Therefore, separate correlations were established to distinguish these zones. Strong correlations between N60 and EM were identified by splitting these zones. The proposed correlation was then compared with the previous research.
  • Publication
    Assessment of the Twin-Tunnel Interaction Mechanism in Kenny Hill Formation Using Contraction Ratio Method
    ( 2020-10-01)
    Govindasamy D.
    ;
    Ismail M.A.M.
    ;
    Mohd Faiz Mohammad Zaki
    ;
    Ken T.Y.
    ;
    Cheah F.
    ;
    Likitlersuang S.
    Volume loss during tunneling excavation leads to ground deformation, which can damage adjacent surfaces or subsurface structures. Thus, tunnel design with proper estimation of ground deformation and realistic geotechnical simulation is essential for large-scale urban underground construction. In this study, subsurface characterization of the tunnel excavation section in Kenny Hill Formation (KHF) was conducted to develop 3D ground model and tunnel-filtered models and obtain ground sections through the spatial interpolation of borehole data using the Inverse Distance Weighting (IDW) method. Six greenfield ground sections were selected by using the tunnel-filtered model’s configuration of tunnels and available tunneling-induced ground movement data. Conceptual models for finite element modeling were developed based on soil profiles, and the corresponding soil parameters were determined from ground sections. The strength and stiffness parameters of the Hardening Soil (HS) model were established using data from site investigation, in situ and laboratory tests, and empirical correlations with standard penetration test. The effectiveness of empirical correlations was determined through back analysis of twin-tunnel excavation in 2D finite element analysis using the contraction method and verified with monitored ground movement data. The numerical back-analyzed results of twin-tunnel excavation simulation using HS parameters obtained from a selected empirical correlation showed good agreement with construction-monitored ground movements. The application range of the values of contraction ratio was from 0.3 to 0.95%.
  • Publication
    Carbon footprint hotspots of prefabricated sandwich panels for hostel construction in Perlis
    ( 2017-10-16)
    Norashikin Razali
    ;
    Afizah Ayob
    ;
    Muhammad Erwan Shah Chandra
    ;
    Mohd Faiz Mohammad Zaki
    ;
    Abdul Ghapar Ahmad
    Sustainable design and construction have gained increasing research interest, and reduction of carbon from building construction has become the main focus of environmental strategies in Malaysia. This study uses life cycle assessment and life cycle inventory analysis frameworks to estimate the amount of carbon footprint expressed in carbon dioxide equivalent tons (CO2e) produced by manufacturing prefabricated Industrialized Building System sandwich panels and its installation for a five-story hostel in Perlis, Malaysia. Results show that the carbon footprint hotspots were centered on boiler machine operation and cement with 4.52 and 369.04 tons CO2e, respectively. This finding is due to the extensive energy used for steam heating and high engine rating for the boiler. However, for cement, the carbon footprint hotspots are caused by the large quantity of cement applied in shotcrete mixture and its high extraction and production CO2 emission values. The overall onsite materials generated 96.36% of the total carbon footprint. These carbon footprint hotspot results constitute a necessary base for the Malaysian government in accomplishing an adequate dimensioning of carbon emissions in the building sector.
  • Publication
    Prediction of pressuremeter modulus (E M) using GMDH neural network: a case study of Kenny Hill Formation
    ( 2020-05-01)
    Mohd Faiz Mohammad Zaki
    ;
    Ismail M.A.M.
    ;
    Govindasamy D.
    ;
    Leong F.C.P.
    Soil investigation (SI) work is a process of collecting subsurface ground profile information in evaluating soil engineering properties for a construction project. The standard penetration method (SPT) is widely accepted worldwide as a feasible and relatively inexpensive method over other field tests. Pressuremeter test (PMT), on the other hand, is costly and more popular in situ method in interpreting soil deformation behavior. Considering SPT is available in almost site investigation works for all sizes of project, it was tempting to establish the correlation between SPT and PMT results, specifically in local geologic setting. The study was conducted in Kenny Hill Formation, Kuala Lumpur, Malaysia. The correlation established in this study is between pressuremeter modulus (EM) and SPT blow count (N60). In addition to in situ methods, the physical properties of soil extruded from boreholes were tested in geotechnical laboratories to determine soil properties, such as particle size distribution, liquid limit, and plastic limit. These parameters need to be considered during prediction of EM. Group method of data handling (GMDH) neural network has been used to achieve this objective. The mean absolute error (MAE) results show that the GMDH neural networks produce values of 8.04 and 8.59 for training and testing. The root mean square error (RMSE) yields 10.61 and 10.84 for training and testing, respectively. Further, the results of the coefficients of determination (R2) are 0.794 and 0.726 for training and testing demonstrates a good correlation exists between predicted and measured values. Based on the GMDH results, N60, sand, and clay are required input variables for determination of EM.
      3  3