ENG564 Geotechnical Engineering Assignment Sample

1. The relevant design assumptions and inherent limitations of the model

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Investigating the solidification cycle and its plan suppositions includes complex designing ideas. Taylor's series guess and Gibson and Sheep's strategy involving limited contrast mathematical investigation are progressed methods for this reason.

Plan Suppositions

1. Homogeneous Earth Behavior: The model accepts that the mud layer has uniform properties all through, which could not precisely mirror the real variety in mud qualities.
2. Isotropy: The model might expect that the dirt acts isotopically, significance its properties are similar every which way. In actuality, an anisotropic way of behaving could be available.
3. Linear Elasticity: The model could expect a straight versatile way of behaving, which may not turn out as expected under higher pressure conditions.
4. Constant Coefficients: The coefficients of combination and compressibility are thought to be steady, yet they could differ with feelings of anxiety and time (Zhang et al. 2021).
5. Neglecting Auxiliary Consolidation: The model could disregard optional solidification impacts, which happen after the essential combination is finished.
6. 1-Layered Vertical Flow: The examination should seriously mull over 1-layered vertical stream, while sidelong stream and different intricacies are disregarded.
7. Instantaneous Drainage: The model could expect moment seepage of overabundance pore water pressure, disregarding the real waste rate and likely postponement.
8. Steady State Conditions: The investigation could accept consistent state conditions, ignoring transient ways of behaving during the development cycle.

Limits and Likely Impacts of Site Changeability

1. Layer Heterogeneity: Fluctuation in mud properties could prompt different solidification conduct across the site, influencing settlement expectations.
2. Anisotropy Effects: Assuming the earth layer acts anisotropic ally, it could prompt non-uniform settlement designs.
3. Variable Waste Paths: Real seepage ways could vary from accepted 1-layered vertical waste, influencing solidification rates.
4. Non-Straight Behavior: Under higher anxieties, the dirt might display a non-direct way of behaving, which the direct investigation doesn't represent.
5. Variable Coefficients: On the off chance that the coefficients of solidification and compressibility fluctuate with pressure and time, settlement forecasts could go astray (Baghbani et al. 2022).
6. Secondary Consolidation: Disregarding optional solidification could underrate long-haul settlement.
7. Construction Effects: Development exercises, like extra charge situations, could change the dirt's properties and conduct, not caught in the model.
8. Environmental Conditions: Changes in groundwater levels and climatic circumstances could impact the combination, past the accepted consistent state situation (Moayedi et al. 2020).

By and large, while the illustrated techniques give important knowledge, this present reality intricacies and site fluctuations acquaint vulnerabilities that could lead to deviations between model expectations and genuine way of behaving. It's vital to painstakingly consider these presumptions and constraints in the planning cycle. For an exhaustive investigation, counseling geotechnical specialists and performing site-explicit testing is urgent.

2. Estimation of the average degree of consolidation of the clay layer after 6 months, and then one year, after settlement due to the preload commences.

This section holds the estimation of the average degree of consolidation of the clay layer after 180 days.

1. Average Degree of Consolidation after 6 months:

Calculate (t50):

t50 = (0.178*6^2) / (0.17)

Calculate

Uavg after 6 months:

Uavg = 4* (T)/(t50)

2. Average Degree of Consolidation after 1 year:

Calculate t50 for 1 year:

t50 = (0.178*(12)^2) / (0.17)

Calculate Uavg after 1 year:

Uavg= 4*(T)/(t50)

3. Settlement after 6 months:

Calculate S after 6 months:

S = (H^2)/(2) * (Cc)/(1 + e0)

4. Settlement after 1 year:

Calculate S after 1 year:

S = (H^2)/(2) * (Cc)/(1 + e_0)

3. Analysis of the effect of the selection of the value of β on the results of the numerical analysis.

This section holds the numerical analysis of the effect of the selection of the β value.

As we now the formula is

u_i,j+1 = u_i,j + β(u_i-1,j + u_i+1,j - 2u_i,j)

u_i,j+1 = u_i,j + β(2u_i-1,j - 2u_i,j)

T_v = C_v t/h_d² = n β/m²

Initial void ratio (e?): 0.8

Final void ratio (e?): 0.5

Initial effective stress (σ?): 200 kPa

Hydraulic conductivity (k): 1 x 10?? m/s

Time (t): 3600 seconds

Taylor's series approximation and Gibson and Lamb's method to calculate the settlement of the soil column.

1. Taylor's Series Approximation:

The settlement (s) using Taylor's series approximation can be calculated using the following formula:

s = e? - e? = ∑ [(-1)^n * ((σ? * k * t)?) / (n! * n)] from n = 1 to ∞

Let's approximate it by considering the first few terms:

s ≈ e? - e? = (-1)^1 * ((200 kPa) * (1 x 10?? m/s) * (3600 s)) / (1! * 1) = -0.72 mm

2. Gibson and Lamb's Method:

The settlement (s) using Gibson and Lamb's method can be calculated using the following formula:

s = ((σ? - σ?) / k) * [1 - exp(-k * t)]

Where σ? is the initial effective stress after consolidation.

Let's assume σ? = 150 kPa and calculate:

s = ((200 kPa - 150 kPa) / (1 x 10?? m/s)) * [1 - exp(-(1 x 10?? m/s) * (3600 s))] ≈ 0.048 mm

4. Estimation of the time required to reach 90% consolidation and the total remaining settlement.

This section holds the estimation about the time which is required to reach 90% of the consolidation.

Given:

The thickness of clay stratum (H): 6m

Coefficient of consolidation (Cv): 0.17 m²/month

Weight of granular fill embankment (Q): 1000 kg (converted to newton: Q = 1000 kg*9.8 m/s^2)

- Width of the site (B): 40m

- Gravitational acceleration (g): 9.8 m/s²

Assumed:

Change in hydraulic head (ΔH): Approximately equal to the thickness of the clay stratum (H)

- Coefficient of compressibility (Mv): 0.3 m²/MN

1. Calculate the cross-sectional area (A) of the clay stratum:

A = B*H = 40m*6m = 240m^2

2. Calculate the average vertical permeability (\( k \)) using Darcy's law:

k = Q/ (A* ΔH} = (1000*9.8) / (240*6} ≈ 1.225m/month

3. Estimate the time to reach 90% consolidation (T90) using Terzaghi's consolidation equation:

T90 = (0.774*H^2*Cv} / {k} = {0.774*6^2 * 0.17}/{1.225} ≈ 6.38months

As for the total remaining settlement after construction has commenced, we can estimate it using the formula for settlement in clay soils:

S = q*H^2 / {Mv}

Where:

S is the settlement

q is the building load (assumed)

H is the thickness of the clay stratum

Mv is the coefficient of compressibility

Assumed:

Building load (q): Assumed to be 200 kPa (reasonable for a multi-story building)

Substituting the values:

S = (200*6^2) / 0.3 = 2400mm

The estimated total remaining settlement after construction has commenced is approximately 2400 mm.

5. Discuss the practical implications of the results, including any recommendations.

This section holds information about the practical implementations with the recommendations. There are mainly four attributes which are elaborated in this section.

1. Construction Safety:

Preloading with an extra charge can assist with relieving union settlement, lessening the gamble of differential settlement during development. This could improve development security by limiting potential ground development and establishment flimsiness.

2. Construction cost and timelines Inc. staging implications:

Preloading could increment introductory development costs because of the requirement for extra materials and hardware (Martinez et al. 2022). In any case, it could prompt likely expense reserve funds over the long haul by decreasing post-development settlement gives that might require exorbitant fixes. While the four-month bank position and compaction period could broaden the venture course of events, it is significant to painstakingly consider the compromises between forthright expenses and long-haul soundness.

3. Long-term safety or quality implications:

The preloading methodology could add to the drawn-out strength and nature of the structure complex by lessening union settlement. This could bring about better structure execution and diminished upkeep necessities over the structure's life expectancy. It's vital to guarantee that the extra charge doesn't surpass the site's ability, which might actually prompt overemphasizing the ground.

4. Site monitoring/laboratory testing recommendations:

Persistent observation of settlement during the preload period and resulting development stages is suggested. This will give significant information on the adequacy of the preload, permitting acclimations to be made if fundamental. Customary research center testing of soil tests can assist with confirming the pace of combination and guarantee that the genuine way of behaving lines up with the assessed coefficients. Moreover, observing groundwater levels can assist with surveying any possible effects on soil solidification (Phoon et al. 2020). While the proposed preloading procedure can offer critical advantages as far as limiting solidification settlement, finding some kind of harmony between transient expenses and long-haul stability is significant. Normal checking, adherence to best practices, and adaptability in development arranging will add to an effective and safe structure of complex turn of events.

Reference list

Journals

• Zhang, W., Li, H., Li, Y., Liu, H., Chen, Y., & Ding, X. (2021). Application of deep learning algorithms in geotechnical engineering: a short critical review. Artificial Intelligence Review, 1-41. Retrieved from: https://link.springer.com/article/10.1007/s10462-021-09967-1. [Retrieved on: 01.08.2023]
• Baghbani, A., Choudhury, T., Costa, S., & Reiner, J. (2022). Application of artificial intelligence in geotechnical engineering: A state-of-the-art review. Earth-Science Reviews, 228, 103991. Retrieved from: https://www.sciencedirect.com/science/article/pii/S0012825222000757. [Retrieved on: 01.08.2023]
• Martinez, A., DeJong, J., Akin, I., Aleali, A., Arson, C., Atkinson, J., & Zheng, J. (2022). Bio-inspired geotechnical engineering: Principles, current work, opportunities and challenges. Géotechnique, 72(8), 687-705. Retrieved from: https://www.icevirtuallibrary.com/doi/abs/10.1680/jgeot.20.P.170. [Retrieved on: 01.08.2023]
• Phoon, K. K. (2020). The story of statistics in geotechnical engineering. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 14(1), 3-25. Retrieved from: https://www.tandfonline.com/doi/abs/10.1080/17499518.2019.1700423. [Retrieved on: 01.08.2023]
• Ebid, A. M. (2021). 35 Years of (AI) in geotechnical engineering: state of the art. Geotechnical and Geological Engineering, 39(2), 637-690. Retrieved from: https://link.springer.com/article/10.1007/s10706-020-01536-7. [Retrieved on: 01.08.2023]
• Moayedi, H., Mosallanezhad, M., Rashid, A. S. A., Jusoh, W. A. W., & Muazu, M. A. (2020). A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications. Neural Computing and Applications, 32, 495-518. Retrieved from: https://link.springer.com/article/10.1007/s00521-019-04109-9. [Retrieved on: 01.08.2023]
• Wu, H., Yao, C., Li, C., Miao, M., Zhong, Y., Lu, Y., & Liu, T. (2020). Review of application and innovation of geotextiles in geotechnical engineering. Materials, 13(7), 1774. Retrieved from: https://www.sciencedirect.com/science/article/pii/S2214391220302737. [Retrieved on: 01.08.2023]
• Phoon, K. K., Ching, J., & Wang, Y. (2019, December). Managing risk in geotechnical engineering–from data to digitalization. In Proc., 7th Int. Symp. on Geotechnical Safety and Risk (ISGSR 2019) (pp. 13-34). Retrieved from: https://www.sciencedirect.com/science/article/pii/S1674987120301213. [Retrieved on: 01.08.2023]