Brief summary of engineering doctoral thesis: Analysis of influencing parameters and basics of determining resistance factors of drilled shafts used in bridge substructures in Ho Chi Minh city

Chia sẻ: Trần Khánh Dư | Ngày: | Loại File: PDF | Số trang:27

Thêm vào BST

Báo xấu

69
lượt xem 6
download

Download Vui lòng tải xuống để xem tài liệu đầy đủ

Objectives of doctoral thesis Analysis of influencing parameters and basics of determining resistance factors of drilled shafts used in bridge substructures in Ho Chi Minh city: Determine resistance factors according to soil base strength which is equivalent to methods presented in specifications being applied.

Chủ đề:

Bình luận(0) Đăng nhập để gửi bình luận!

Lưu

Nội dung Text: Brief summary of engineering doctoral thesis: Analysis of influencing parameters and basics of determining resistance factors of drilled shafts used in bridge substructures in Ho Chi Minh city

1 MINISTER OF EDUCATION AND TRAINING UNIVERSITY OF TRANSPORT AND COMMUNICATIONS NGO CHAU PHUONG ANALYSIS OF INFLUENCING PARAMETERS AND BASICS OF DETERMINING RESISTANCE FACTORS OF DRILLED SHAFTS USED IN BRIDGE SUBSTRUCTURES IN HO-CHI- MINH CITY MAJOR: BRIDGE AND TUNNEL ENGINEERING CODE: 62.58.02.05.03 BRIEF SUMMARY OF ENGINEERING DOCTORAL THESIS Hanoi-2014
2 This thesis completed at: Faculty of Civil Engineering University of Transport and Communications SUPERVISORS: 1. Assoc.Prof. Dr. Tran Duc Nhiem 2. Assoc.Prof.Dr. Nguyen Ngoc Long Reviewer 1: Prof. Dr. Nguyen Nhu Khai, National University of Civil Engineering Vietnam. Reviewer 2: Prof. Dr. Nguyen Dong Anh, Institute of Mechanics Vietnam. Reviewer 3: Dr. Do Huu Thang, Institute of Transport Science and Technology Vietnam. Thesis is defended in front of the University-Graded Committee of thesis evaluation according to Decision #1359/QĐ-ĐHGTVT, on date 17th June 2014 signed by the Rector of University of Transport and Communications on date………………….. 2014. Readers can find this thesis at: - Vietnam National Library - Library of the University of Transport and Communications
1 INTRODUCTION By applying statistics and probability and reliability theory to engineering foundations, the thesis proposes a pattern to determine resistance factors of drilled shafts used in bridge substructures based on statistics characteristics of the ratio between real measured values and estimated values for Resistance (R) and load effect (Q). Then, by analyzing statistics characteristics of the capacity based on 24 results of static axial compressive load test of drilled shafts that were constructed by the wet method (bentonite) in cohesive and non-cohesive composite soils in Ho- Chi-Minh city, the thesis captures the determination of resistance factors for four different calculation methods of pile resistance design based on soil base strength condition. Literature Review: Drilled shafts construction technology was first used in America (1890), in over the word (1950) and in Vietnam (1990). However, the calculation theory has been developed more slowly. One of the trends in the world is to research new problems of applying statistics and probability theories and reliability theory to correct resistance factors based on statistics characteristics of the ratio between real measured values and estimated values for Resistance (R) and load effect (Q) extracted from a reasonable number of construction projects. Research results are gradually applied to update and to implement for some clauses in standards or specifications and design instructions of developed countries such as in Europe, Japan…and America. Over two more recent decades in Vietnam, accompanied with the development of infrastructure in great scale built in soft soil bases or in urban areas, drilled shafts foundations becomes one of the best solutions. The foundations are also widely applied in Ho-Chi-Minh city. However, there is not any backgrounds to determine resistance factors of drilled shafts based on analyzing statistics characteristics and reliability analysis using current advanced theories. Therefore, studying of the backgrounds to determine the resistance factors based on reliability analysis is new and attracts international and domestic researchers. It is the reason why I selected this topic to research.
2 Thesis title: “Analysis of influencing parameters and bases to determine resistance factors of drilled shafts used in bridge substructures in Ho-Chi- Minh City”. Objectives: Determine resistance factors according to soil base strength which is equivalent to methods presented in specifications being applied. Structure: Drilled shafts used in bridge substructures. Research Scope: Predictive resistance and real resistance obtained from the results of static axial compressive load tests for drilled shafts in Ho- Chi-Minh City casted in cohesive and non-conhesive composite soils (sand, sandy, clay, clay mud,...) by wet method; to determine general resistance factors according to soil base strength condition for four different methods of pile resistance design: 1) Russian Method specified in TCXDVN 205- 98; 2) Japanese Method (JRA 2002 SHB -Part IV); 3) Reese&O'Neill (1988) and 4) O'Neill&Reese (1999). Problems relating to load statistics characteristics, general resistance factors for various types of soil, local areas, and type of structure as well as pile shalf and tip resistance factors are not performed in this thesis and they are recommented for future studies. Scientific and practical meaning of the topic: - Apply advanced theories of statistics analysis and reliability to propose a pattern to determine resistance factors of drilled shafts based on statistics data of the ratio between real measured values and estimated values for Resistance (R) and load effect (Q). - The thesis has analyzed and determined statistics characteristics of the ratio between real measured values and estimated values; to determine resistance coefficients for the four methods from 24 static load test results of drilled shafts constructed in cohesive and discrete composite soils in Ho- Chi-Minh city subjected to static axial compression and other applicable data. - Research results of the thesis can be used as reference documents in design bearing capacity of drilled shafts used for bridge substructures constructed in Ho-Chi-Minh city or similarly geological areas. Chapter 1. GENERAL 1.1. Drilled shafts and its application in infrastructure construction
3 1.1.1. Definitions, structural characteristics and technology Drilled shafts of bridge substructures (Drilled Shafts) : are a part of piers and abutments; they are constructed by raw concrete casting in pre-bored holes with or without steel case inside. The piles are subjected to loads transferred from foundation foots and then transfers the loads into surrounding soil base. Wet drilled shafts construction method (wet method): to drill holes and to cast the piles in water or in bore mud and a temporary tube wall segment is put in the boring hole top. Applicable for cohesive, discrete and high groundwater level areas. Drilled shafts cross-section maybe cylindrically constant throughout the pile length, this pile type is called simple one; or cylindrical-shaped but widened at bore hole tip area. 1.1.2. The utilization of the pile in Vietnam and in the world Through analysis, the author recognizes the need to use the drilled shafts is growing both in Vietnam and in the world. Almost foundation solutions for traffic, civil and industries from medium to large scale in Vietnam are using drilled shafts foundations. 1.1.3. Current status and characteristics of drilled shafts used in HCM City Through analysis, the drilled shafts foundation for constructions here is also applied a lot in recent years. Most of the piles are constructed by the wet method (in bentonite) through the mixture soil layers with combined cohesive and discrete soils, these layers can be weak, average or good in load bearing. 1.1.4. Some structural characteristics, drilled shafts technology in Vietnam Due to the characteristics of the technology, the complexity of geology; experience level of the participants in the management, design and construction limits and especially the system of processes, standards are still in the process of integration and are not complete and existing many problems. Therefore, the quality of the drilled shafts or pile resistance depends very much on the aforementioned elements. 1.2. Design drilled shafts based on reliability according to Load and Resistance Factor Design method (LRFD) The design method LRFD is based on reliability,as the load effects with their particular factors (Qtk) shall not exceed the resistance with their particular factors (Rtk) Through analysis of the historical development of the design philosophies and design standards such as Allowable Stress Design (ASD), Limited States Design or Load Factor Design (LSD; LFD), Reliability- Based Design (RBD) and the method with reliability factors separately or Load and Resistance
4 Factor Design (LRFD), the author found that design calculations of drilled shafts foundation according LRFD method is an advanced method and have being trusted and applied by many countries in the world. 1.3. Analysis of literature to determine drilled shafts resistance factors based on reliability used for bridge substructures in the world 1.4. Analysis of literature showing the LRFD application and determining resistance factors for bridge design in Vietnam 1.5. Current challenging problems Some current problems in bridge design standards 22TCN272-05 and AASHTO LRFD 2012 (2007) are shown in Table 1.1. Table 1.1. List of current problems in the standards 22TCN272-05 and AASHTO LRFD 2012 (2007) Problem 22TCN272-05 AASHTO LRFD 2012 (2007) Method for determination 05 methods existed 01 method of resistance in cohesive and from before 1988 O'Neill&Reese (1999) discrete soil Resistance factors are not Sandy soil, cohesive Cohesive and discrete soil specified for: and discrete soil Officially Applied Year 2005 2007 Many method based Determination of ultimate 5% pile diameter or merged on TCXDVN269- resistance under static load settlement pile 2002 The resistance factors are not the standard values for Recomendations when all states of America and of course not accurate for resistance factors used other countries, including Vietnam Some shortcomings of the related scientific studies: - The study of resistance factors correction of deep foundation of Paikowsky et al (2004): Didn’t mention the resistance factors of method O'Neill & Reese (1999), just mention method Reese & O'Neill (1988) for sandy and clay mixture soil conditions on the basis of 44 load test results of drilled shafts in Florida. - Liang (2009): Proposed resistance factors for method O'Neill & Reese (1999), but only suggested for sandy and clay conditions in the U.S. - Murad et al. (2013): Proposed resistance factors for method O'Neill & Reese (1999) for mixed cohesive and discrete soil condition in Louisiana & Mississippi on the basis of 34 pile load test results, but there were 26 values extrapolated to static load test results due to not try to break the pile. - There is no study regarding the research objectives of this thesis in Vietnam.
5 From the above-mentioned problems, the author proposes the targets, content and research methodology of the thesis as decribed in items 1.6 and 1.7. 1.6. Targets of the topic Quantitative study of factors affecting the estimated resistance results of the four methods compared with actual field resistance of drilled shafts under the ground conditions in the area of HCMC. This means that the author has determined the statistics characteristics of the ratio of the real measured resistance and the expected one (resistance bias factor, λR); To research the basis of determining the resistance factors and to propose the resistance factors for drilled shafts foundations of bridge substructures in HCMC area for the four methods. 1.7. Content and Research Methodology To research the basis of determining the resistance factors for drilled shafts using probability and statistics theory and advanced reliability theory. Specifically, the survey collected from 24 results of static pile load tests in HCM City, the author conducted a study to identify typical statistics of the ratio of the measured and estimated resistances (Resistance bias factor, λR); From that way, the authod determined the resistance factors for the four methods on the basis of reliability analysis. Chapter 2. DETERMINATION OF RESISTANCE FACTORS OF DRILLED SHAFTS BASED ON RELIABILITY THEORY According to AASHTO LRFD, drilled shalfs axial resistance factors according to soil base strength condition are factors determined based on the statistical characteristics of the nominal resistance, mainly calculated from the variability of characteristic parameters of the ground around the pile, the pile size, level of expertise (professional) of human - device participating in the implementation phase of the project and the uncertainty of prediction method for nominal resistance; but also related to the statistical characteristics of load effects through the identification process. 2.1 Method to analyze the statistical characteristics 2.1.1 Determination of minimum size of samples zα /2 + zθ )2 estimated by: n (= C Sample size is = (2.1) (ε /σ ) 2 (ES)2 In which: σ and zα/2, zθ: common standard deviation and standard deviation with error probabilities α, θ from the normal distribution; ɛ: allowable error; C: is a constant related to error probability Type I and Type II.
6 For example, to determine the sample size for the thesis: With some prediction methods of drilled shafts resistance that accept averaged estimated error of about 50% (=1/FS, FS=2: safety factor) with reliable interval of 0,95 (i.e., α=0,05) and θ = 0,2. Previous studies indicate standard deviations of the resistance bias factor from 0,27 to 0,74. Thus, the effect factor is: ES = 0,5/0,74 = 0,456 and C=7,85. By applying the formula (2.1) to estimate the required sample size for the study: 7,85 = n = 17, 2 > 17( samples ) ( 0,5 / 0, 74 )2 To compare with recommendation of Murad (2013), the number of test piles for the study area at least is ≥ 20 piles. Thus, with 24 results of static axial compressive load tests for drilled shafts in Ho Chi Minh City area can be considered reliable enough for analysis in order to meet the research objectives of the thesis. 2.1.2 Testing method of suitable probability distribution for the random bias factor Through analysis, the Shapiro-Wilk method or the Pearson chi-square (when the sample size is less than 50) is recommended with the following principles: the empirical distribution consists with assumed theoretical distribution (standard or logarithmic, ... ) when the match probability (P) is greater than 0.05. 2.1.3 Correction method for statistical characteristics of random bias factor For foundation structures, the laws of probability distributions of random bias factor often match or nearly match the normal standard distribution or standard logarithm. Through research, the authod proposes two correction methods of statistical characteristics for logarithmic distribution form according to the the principle (Allen, 2005): Based on the graph of the 1 cumulative probability function to 2 examine the conformity with one of 3 the two cases, 1) consistent with the entire collection data (FTAD method -fit to All data) or 2) only consistent with the area of small values at Figure 2.1. Cumulative probability distribution tail (BFTT-Best method density function of resistance bias fit to tail) (Figure 2.1) factor
7 2.2 Reliability Analysis Method When analyzing the reliability, the incident probability is the condition that the limited state has been reached. The adjustment factors are selected to ensure that incident probability of each limited state is very small and acceptable. The probability density functions of load effects (Q) and resistance (R) with the assumption of two independent normally distributed variables (Figure 2.2). Safety range or the safety factor is the difference between R and Q, the quantitative quantity for the safety is reliability or safety probability, Ps: Ps = R > Q ) = G =- Q > 0) =( β ) P( P( R Φ (2.2) Incident probability: Pf is calculated as: Pf = P ( G < 0 ) = 1- Ps = 1 − Φ ( β ) (2.3) In which: Φ(.): normalized distribution functions; β: index of reliability. Index of reliability is determined based on averaged number and standard deviation as follows: µG µ R - µQ (2.4) β = = σG σ R + σQ 2 2 Figure 2.3. Normal Logarithm Figure 2.2. Normalized distribution probability density distribution probability density functions function If R and Q follows the normal logarithm distribution, safety range, G, is determined as follows: (Figure 2.3): G=ln(R)-ln(Q)=ln(R/Q) (2.5) Here, β is determined as the ratio of logarithm averaged number G and logarithm standard deviation, ξG. β= G (2.6) ξG 2.3 Methods to determine pile body resistances The thesis has researched four methods to determine the pile body resistance: Method in accordance with the safety factor of the design philosophy of allowable stress (ASD); first-order secondary moment
8 method (FOSM); First-order reliability method (FOSM); Monte Carlo method (MCS). After analyzing the advantages and disadvantages of these four method, the author proposes to select Monte Carlo analysis method to determine the resistance factorss. Safety range, G, is applied to determine resistance factors as R and Q follow the normal logarithm distribution: QD λR (γ D+ γ L) (2.7) QL f(R, Q= G ln ) = Q ϕ ( λD D + λL ) QL 2.4 Propose a procedure and pattern to determine the resistance factors The procedure and pattern to determine the pile resistance factors comply with the ensurement basis of target reliability as follows: 1. To determine limited state according to soil base strength conditions for drilled shafts (22TCN272-05, AASHTO LRFD), strength state function: g(R,Q)=ϕR – (γDQD+γLQL)= λR(γDk+γL)/ϕ - (λDk+ λL); 2. To select statistical parameters of design load effect (Q) and load factors: the representive is static load bias factor (λD) and live load effect bias factor (λL) complied with the standard AASHTO LRFD. 3. To analyze the statistical characteristics of resistance (R): the representive is resistance bias factor, λR, which is the ratio of measured ultimate resistance (Rtd) and predicted nominal resistance (Rdt): a. To determine the measured ultimate resistance Rtd from results of pile static load tests according to soil base condition, this is the trial load value at a settlement of 5% of pile diameter or merged settlement pile (AASHTO LRFD 2012, TCVN 9393-2012); b. To predict the nominal resistance (Rdt) based on calculation theory; c. To determine the resistance bias factor, λR=Rtd/Rdt; d. To analize, calculate the statistical parameters (μ, σ) and to verify the form of distribution density function (standard, logarithm,..) suitable for λR; 4. To analyze and to determine the resistance factors of drilled shafts (ϕ) on the basis of analyzing reliability follwing Monte Carlo method with the target reliability index satisfied, βt; 5. To recommend to correct the resistance factors for calculation method. The above procedure is shown in Figure 2.4.
9  Define the failure condtion of drilled shafts Determine limit state based on soil base for piles based on soil base (AASHTO LRFD, 5% pile drilled shafts piles (strength, service states) diameter of merged) Strength state function: g(R,Q)=ϕR – (γDQD+γLQL)  Determine statistical characteristics for 2 random variables (R: resistance, Q: load effect): Representive of R is resistance bias factor, λR=Rtd/Rdt Representive of Q is load effect bias factor, (λD, λL)  Determine λR, is the ratio of measure ultimate  Apply the statistical characteristics to resistance, Rtd and predicted nominal resistance, Rdt deadload and live load effect bias factor (λD, λL) according to AASHTO LRFD  Analysis and calculate the statistical characteristics (μ, σ, V) and verify distribution density function (standard, loga…) suitable for λR  Evaluate the reliability index Select target reliability index βt (refered to AASHTO LRFD: βt=3,0)  Determine resistance factors ϕ based on Monte Determine reliability index, β and Carlo (MCS) method or fisrt-order reliability method incident probability, Pf (FORM) 11 Propose to correct resistance factors for  Compare and evaluate the study results with estimated axial resistance method following other literature soil base strength condition Figure 2.4. Analysis model to determine pile resistance factors on the basis of ensuring the target reliability index Results obtained in Chapter 2 - Recommend to use relative random resistance bias factor (λR) with a minimum sample size of 20 to analyze statistical characteristics. When choosing a probability distribution function (cumulative), it is needed to consider between 2 cumulative distribution functions which fit to the entire real values (FTAD) and cumulative distribution function calibrated in accordance with the actual value area at the tail of distribution (BFTT). - Recommend to use Monte Carlo method to analysis the reliability as a basis for determining pile resistance factors and to use the first-order reliability method (FORM) for validation. - Propose a procedure and a pattern to determine pile resistance factors as shown in item 2.4. Chapter 3. ANALYZING THE PARAMETERS INFLUENCING TO RESISTANCE FACTORS OF DRILLED SHAFTS USED IN BRIDGE SUBSTRUCTURES IN HO CHI MINH CITY
10 The parameters that influence the results of determining of pile resistance factors described in Figure 3.1. Target reliability index (βt) Real geological Model of (MH) soil Model MH applied Result in (φ) layer profile base for design CKN Abnormal profile + Statistical error Error due to MH: MH predicts uncertain Q measurement error discrebing factos: (khả φo, N,…) γ (ϲ, át ) γ (ϲ, φo, N,…) Su (qu,…) MH predict uncertain R μ±σ μ±σ Quality of construction organization, management and operation based on reliability analysis Figure 3.1. Parameters influencing to determinging of resistance factors (φ) 3.1 Uncertainty factors and statistical characteristics of load effect In Vietnam, there is no research conditions to determine the rules of distribution of load effects, the author proposes to apply the statistical characteristics and other factors regulated by the AASHTO LRFD design as:γL=1,75, λL=1,15, VL = 0,18; γD = 1,25, λD=1,08, VD = 0,13, QD/QL =3. where: λD and λL are deadload and live load effect bias factor. VD and VL are variation coefficients of dead load and live load; the ratio QD/QL is of dead load and live load. 3.2 Uncertainties affecting to drilled shafts resistance The uncertainties affecting the predicted pile resistance should be analyzed to determine the resistance factors for methods to ensure required reliability and they are divided into four main groups: 1). The diversity, the unusual geological structure; 2). The error of measurement (measuring, surveying, testing of characteristic parameters of the material, structure or soil base); 3). The model error and 4). Quality of project administration and construction experience (According to Phoon and Kulhawy (1999), Paikowsky (2004)). To describe the general characteristics of these uncertainties, relative random resistance bias factor (λR) as outlined in Chapter 2 can be used. 3.3 Analyzing selection of methods to predict drilled shafts resistance On the basis of several popular methods of pile resistance prediction in Vietnam and overseas, the author selected four methods according to soil base condition as mentioned in the research scope.
11 The formula to determine the unit resistance at the pile tip and pile shaft according to the two standards are briefly introduced in Table 3.1 and Table 3.2. Table 3.1. Summary of formula to determine nominal unit resistance of drilled shafts according to 22TCN 272-05 and AASHTO LRFD 2012 22TCN 272-05 (brief RO88-272) AASHTO LRFD 2012 ( brief OR99-AL12) Unit shaft Unit tip resistance, qp Unit shaft resistance, qs Unit tip resistance, qp resistance, qs 1. Cohesive soil (clay, soil with clay dust content higher 50%) qp=Nc Su ≤4 (MPa), qp=Nc Su ≤4 (MPa), qs= α Su (MPa) qs= α Su (MPa), where: α where: where: Su(MPa) α =0,55, với Su / pa ≤ 1,5 0,9 - with Su 50 with N60
12 To ensure the consistency with the design philosophy of drilled shafts in LRFD method, the author proposes to select actual measured resistance value in accordance with the AASHTO LRFD standards as outlined (referred to as AASHTO method) when analyzing to determine resistance factors. In AASHTO LRFD 2007, actual measured pile body resistance is the load at which settlement of pile top equals 5% of pile diameter or pile is Figure 3.2. Trial loading and settlement relationship merged (Figure 3.2). 3.5 Analyzing the statistical characteristics for resistance bias factor of drilled shafts based on soil base strength in Ho Chi Minh City 3.5.1 Survey to collect data base of static axial compressive load tests to serve for current research. The survey collected 24 profiles of static axial compressive load tests for drilled shafts (including geological survey reports, topographical, design dossiers and dossiers of pile construction quality management) which meet the requirements of statistical studies in Figure 3.3, Table 3.3 and Table 3.4 (see details in Appendix 1). Characteristics of this data set is the same method of construction in bentonite mortar (wet technology); geological conditions are similar mixture soil (cohesive and discrete): mud clay, silt, clay, loam, sand, clay sand (mainly forming pile skin resistance ); but different in size (diameter from 1m-2m, length from 25m-85m) and location (Table 3.3).
13 Huyện Củ Chi Geological characteristics at the KÝ HIỆU TÊN CỌC TỈNH BÌNH DƯƠNG CT1 TP1NL PT22-PT24 testing place can be considered as CT2 TPRC CT3 TP02LG the representative for the type of CT4 PT23 TPCY PT15-PT17 PT19-PT21 the cohesive and discrete mixture TP.HỒ CHÍ MINH TP HỒ CHÍ MINH CT5 CT6 TPCTL PT12-PT14 PT16-PT18 TPCTN PT7-PT9 soil in HCM City in particular, the PT18-PT19 PT24-PT25 CT7 CT8 TPABCL TPB1CL layer profile is formed from river PT20-PT21 PT26-PT27CT9 CT10 TPB3CL C1SG2 PT10 PT11 PT12 PT6 sediments, sea (clay mud, muddy PT2 PT3 CT11 T96CC CT12 TPB-1MT1 sand, sandy loam, sandy clay and CT13 PT22 PT5 TPB-2MT1 CT14 TPB-3MT1 TỈNH ĐỒNG NAI sand). Stratigraphic distribution: CT15 PT1 PT4 TPB-4MT1 CT16 TPB-5MT1 the top layer is soft soil (clay mud, 1CT17 TPB-6MT1 PT1 sand mud) with up to 35m in CT18 CT19 DP55-CO152 DP143-CO152 Huyện Cần Giờ thickness, the SPT index (N 50 (Table 3.3, compressive load tests in Ho Chi Minh Appendix 2, 4). city Table 3.3. Characteristics statistics of 24 drilled shafts under static axial compression testing Length/ Measured Geological characteristics Pile Construction Location Diameter, resistance name Soil Type of soil material (body/toe) method L(m)/D(m) (kN) East-West Avenue project – Ho Chi Minh City, District 6, 8, 1 and 2: From CT1-CT9 Nuoc Len bridge, Clay mud, sandy mud, clay sand, CT1 54,9/1,2 7.554 Km0+800 clay/Clay sand Rach Cay Bridge, Clay mud, clay sand, clay, sandy CT2 59,5/1,2 10.440 KM3+700 clay/Fine sand Clay mud, clay sand, sandy CT3 Lo Gom Bridge, Km4+725 71,8/1,5 14.712 clay/Clay sand Y-Shaped Bridge, CT4 25,7/1,0 5.542 Sandy clay, Grevel dust sand/ Clay Km10+680 wet Cohesive Ca Tre Lon Bridge, (Bentonite) CT5 39,1/1,2 8.041 and Clay, clay sand/Dust sand Km17+017 discrete Ca Tre Nho Bridge, Clay mud, sand clay, clay CT6 54,4/1,2 11.673 Km17+677 sand/Gravel clay sand Clay mud, sand clay, clay sand/ CT7 A&B Bridge, Cat Lai 38,1/1,0 5.572 Gravel clay sand Intersection Over-Passing CT8 67,0/1,0 12.000 Bridge, Km21+300 Organic clay, clay/clay sand CT9 58,8/1,2 14.760 Sai Gon 2 Bridge, Q.BT- Mud, clay sand, clay, clay sand, CT10 74,0/1,2 40.810 wet Q2, sand clay/Sand clay Cohesive Can Bridge, Km7+958, CT11 79,3/2,0 16.346 and Organic clay, clay/clay sand wet HCM-LT-DG Express discrete
14 Length/ Measured Geological characteristics Pile Construction Location Diameter, resistance name Soil Type of soil material (body/toe) method L(m)/D(m) (kN) Clay mud, clay sand, clay, sand dust CT12 40,2/1,0 7.070 /dust sand Can Bridge, LT: P7-17- Clay mud, clay sand, average sand, CT13 _P7-22, Metro No.1, Ben 77,5/1,5 27.727 wet dust clay /dust sand Thanh-Suoi Tien, HCM Clay mud, average sand, dust clay / CT14 75,4/1,2 19.672 Cohesive average sand and Clay mud, average sand, dust clay / CT15 26,7/1,0 6.428 discrete average sand Can Bridge, LT: P13-39 Gravel fine sand, gravel clay, sandy CT16 _P13-41, Metro No.1, Ben 55,4/1,5 27.727 wet clay/dust sand Thanh-Suoi Tien, HCM Gravel fine sand, gravel CT17 46,8/1,2 17.942 clay/average dust sand CT18 85,0/1,5 22.171 Cohesive Office Building, 152 Đien and Mud, clay, clay sand/Clay sand wet CT19 Bien Phu, BT, HCM 83,0/1,0 13.538 discrete CT20 Ben Thanh Tower, 48-50 76,0/1,2 30.970 Cohesive Clay mud, sandy clay, clay Le T. Hong Gam, D.1, and wet CT21 74,0/1,5 30.656 sand/Clay sand HCM discrete CT22 Lotte Mart Binh Duong, 49,4/1,5 16.554 Cohesive Organic clay, clay, sand clay, coarse CT23 D.Thuan An, Binh Duong 49,2/1,2 14.041 and wet – fine sand/ Coarse-fine sand CT24 (near Sai Gon river) 50,0/1,0 11.289 discrete Table 3.4. Synthetic table of survey data of experimental results of drilled shafts under static load test in HCMC area and comparision with a number of research works of foreign authors Data Characteristics collected from static loading pile tests Work of Construction Geology/Location n (pile) L(m) D(m) Rtd (kN) method/static loading Cohesive and Present discrete mixture 24 25-85 1-2 5.542-40.810 wet/static loading thesis soil/HCM city Clay/America 15 4,91-31,32 0,46-0,91 1.373-4.903 Combined (dry, wet, Liang wall tube)/Static (2009) Clay/America 18 4,91-30,5 0,36-0,91 113-7.551 loading&Osterberg- Cell Cohesive and Combined (dry, wet, Murad discrete mixture soil 2.108- wall tube)/Static 32 10,7-42,1 0,61-1,83 (2013) / Louisiana& 27.125 loading&Osterberg- Mississippi(America) Cell Notation: n-number of piles; D-diameter; L-length, Rtd-actual measured resistance Comment: From table 3.3 and 3.4, it can be found that: 24 document sets mentioned above are similar to data from studies of some foreign authors on the general nature of the survey data collected. Thus, the 24 sets of data are sufficiently reliable to carry out a study to identify the resistance factors of foundation piles for bridge substructures in HCMC area.
15 3.5.2 Analysis of data statistic characteristics Statistical analysis data includes: 1. Estimated nominal resistance (Rdti) according to the four methods mentioned above with the geological survey data and the actual size of the pile; 2. Actual measured resistance (Rtdi) which is testing load value corresponding to the settlement by 5% of pile diameter or the load causes the pile merged. The analyzed results were listed in Table 3.5. Use R-software to analyze the statistical characteristics for this resistance bias factor (mean, λ R , standard deviation, σλR, coefficient of variation, VλR) and appropriate distribution rules. Analytical results are presented in Table 3.5 and Figure 3.4-3.7. The study results summarized for a comparison with some research results abroad are presented in Table 3.6. Table 3.5. Actual measured and predicted nominal resistances, statistical characteristics of resistance bias factor (λR) of drilled shafts according to 4 methods for 24 piles under static load tests Length/ Measured Predicted nominal resistance, Rdt(kN) and resistance bias factor (λRi) based on: Pile Diameter, resistance RO88-272 OR99-AL12 SNIP-205 SHB4-JRA02 name L(m)/D(m) Rtdi(kN) Rdti λRi Rdti λRi Rdti λRi Rdti λRi CT1 54,9/1,2 7.554 9.253 0,820 8.836 0,850 7.127 1,060 5.868 1,290 . . . . . . . . . . . CT24 50,0/1,0 11.289 7.806 1,450 7.372 1,530 9.398 1,200 7.615 1,480 Averaged number of bias factor λR, λ R 1,066 1,153 1,215 1,203 Standard deviation of λR, σλR 0,308 0,351 0,246 0,368 Variation coefficient of λR, VλR 0,289 0,304 0,202 0,306 Most suitable distribution (Standard or loga loga loga loga logarithm distribution) Ps=0,80 Ps=0,56 Ps=0,99 Ps=0,39 (Notation: Ps: Appropriate probability of aussumed distribution (Standard or logarithm) compared to standardization distribution, determined based on Shapiro-Wilk method (appropriate condition: PS≥0,05)) Standard Distri.: λ R =1,066;σR = 0,308 Stand.Distri.Validation (Shapiro-Wilk): Standard logarit PS= 0.13>0.05 distribution suitable to standard μlnλ=0,026 distribution σlnλ=0,278 Loga.Distri.Validation (Shapiro-Wilk): Ps= 0.80>0.05 suitable to logarithm distribution Hình 3.4. Distribution density vs.distribution inspection for resistance bias factor, λR (Rtd/RdtRO88-272), (RO88-272: Resee&O’Neill(1988) method)
16 Stand.Distri. Stand.loga. — - Expected line of Stand.Distri.Validation λ R =1,153 μ Distri. standard distribution (Shapiro-Wilk): lnλ=0,099 σR=0,351 o – Actual measured Ps=0.18>0.05 suitable σlnλ=0,301 value (Lnλ) to standard distribution Loga.Distri.Validation (Shapiro-Wilk): Ps= 0.56>0.05 suitable to logarithm distribution Figure 3.5. Distribution density vs.distribution inspection for resistance bias factor, λR (Rtd/RdtOR99-AL12), Standard distribution — - Expected line of standard Stand.Distri.Validatio λ R =1,215; σR =0,246 n (Shapiro-Wilk): distribution o – Actual measured value Ps= 0.55>0.05p Stand.loga. Distri. (Lnλ) μlnλ=0,176 Loga.Distri.Validatio σlnλ=0,198 n (Shapiro-Wilk): Ps= 0.997>0.05 suitable to logarithm distribution Figure 3.6. Distribution density vs.distribution inspection for resistaonce bias factor, λR (Rtd/RdtSNIP-205) Standard distribution Stand.loga. — - Expected line of Stand.Distri.Validation λ R =1,203;σR =0,368 Distri. standard (Shapiro-Wilk): μlnλ=0,146 distribution Ps= 0.010.05 suitable to logarithm distribution Figure 3.7. Distribution density vs.distribution inspection for resistance bias factor, λR (Rtd/RdtSHB4-JRA02) Table 3.6. Comparison of analytical results of statistical characteristics in literature Statistical characteristics of resistance bias Prediction Construction factor, λR Soil Note method/Specification method Pile λR σλR VλR Distribution number RO88-272: Reese& Cohesive 1,067 0,302 0,283 loga Results of O’Neill (1988)/ and Wet (Bentonite) 24 loga* this thesis 22TCN272-05 discrete 1,029 0,276 0,268 (AASHTO LRFD Clay Wet 10 1,290 0,348 0,270 Paikowsky
17 Statistical characteristics of resistance bias Prediction Construction factor, λR Soil Note method/Specification method Pile λR σλR VλR Distribution number 1998)/ and sand Wall tube 21 1,040 0,302 0,290 loga (2004) (Cohesive, discrete Combined 44 1,190 0,357 0,300 loga soil) Clay Combined (dry, 53 0,90 0,423 0,47 loga Sand wet, wall tube) 32 1,71 1,026 0,60 loga Cohesive 1,155 0,356 0,308 loga Results of and Wet 24 1,076 0,316 0,294 loga* this thesis OR99-AL12: O’Neill& discrete Resee (1999)/ Cohesive 1,270 0,381 0,300 loga Murad AASHTO LRFD and Combined 34 1,330 0,52 0,391 loga* (2013) 2012/ discrete (Cohesive, discrete 1,122 0,302 0,269 loga Clay Combined 15 soil) 0,902 0,107 0,118 loga* Liang (2009) 2,262 1,004 0,444 loga Sand Combined 18 1,482 0,453 0,306 loga* Comment: From Tables 3.5&3.6 and Figures 3.4 to 3.7, it can be seen that: The dispersion of predicted resistance values or resistance bias factor of SNIP-205 method is at least, the 3 remaining methods have more dispersion and nearly equal (Fig. 3.4-3.7); Resistance bias factor (λR) of the four methods as mentioned above follows the standard logarithmic distribution law (Probability testing in accordance with logarithms distribution of Shapiro-Wilk is Ps > 0.05). In which, SNIP-205 method is the most consistent with the logarithmic distribution (because most consistent probability: Ps = 0.997), followed by RO88-272 method (Ps = 0.8) and last is SHB4-JRA02 method (Ps = 0.39) (Table 3.5 and Figures 3.4-3.7); Averaged value ( λ R ) of resistance bias factor in SNIP-205 method is maximum ( λ R =1,215), followed by SHB4-JRA02 method ( λ R =1,203) and minimum value is of RO88-272 method ( λ R =1,066); Variation coefficient (VλR) of resistance bias factor of SNIP-205 method is the smallest (VλR=0,202 dispersion of at least λRSNIP-205), followed by RO88-272 method (VλR =0,289) and of the method SHB4-JRA02 is maximum (VλR =0,306); The study results of statistical characteristics of resistance bias factor of drilled shafts for the four methods are reliable, quite similar, and consistent with some studies in literature (Table 3.6). 3.6 Determining statistical characteristics of parameters that affect to determination of resistance factors of drilled shafts
18 Through the selection and research outcome as above, the author recommends statistical characteristics of the parameters effecting to the determination of pile resistance under cohesive and discrete mixture soil base condition in Ho Chi Minh City area, as summarized in Table 3.7. Table 3.7. Summary of proposed statistical characteristics of parameters effecting to pile resistance factors according to soil base strength Name of statistical variable Statistical characteristics Note (Resistance bias factor, λ) Distribution λ ( ln λ ) σλ (σlnλ) Vλ 1. Representive for resistance: Resistance bias factor, (λR:actual measured * as a logarithm resistance/predicted resistance) distribution corrected to RO88-272 (Reese&O’Neill loga 1,067 (0,026) 0,302 (0,278) 0,283 be consistent with values (1988)) loga* 1,029 (-0,006) 0,276 (0,263) 0,268 at the tail area of the OR99-AL12 (O’Neill&Reese loga 1,155 (0,099) 0,356 (0,301) 0,308 distribution method “Best (1999)) loga* 1,076 (0,032) 0,316 (0,288) 0,294 fit to tail (Allen, 2005)”; SNIP-205 (TC Nga trong loga 1,216 (0,176) 0,243 (0,198) 0,200 Values inside the TCXDVN205-98) loga* 1,215 (0,171) 0,270 (0,219) 0,222 bracket (.) are averaged SHB4-JRA02 (JRA2002- loga 1,203 (0.146) 0,343 (0279) 0,285 ones ( ln λ ) and standard deviation (σlnλ) of SHB_Part IV) loga* 1,127 (0,089) 0,282 (0,246) 0,250 logarithm distribution. 2. Representive for load effect: bias factor of deadload (λD) and liveload (λL) effects Deadload effect, λD loga 1,080 (0,069) 0,140 (0,129) 0,130 According to 22TCN Liveload effect, λL loga 1,150 (0,124) 0,210 (0,179) 0,180 272-05 (AASHTO LRFD) Deadload coefficient, γD=1,25; liveload coefficient, γL=1,75; ratio of deadload (D) over liveload (L), D/L=3. Results obtained from Chapter 3 In the framework, the obtained results quantified parameters influencing the resistan factors of drilled shafts through statistical characteristics of relative random resistance bias factor. Based on the result of the analysis, evaluate and quantify statistical characteristics of parameters effecting to the resistance factors of drilled shafts according to soil base strength condition for four above methods (RO88-272, OR99-AL12, snip-205, SHB4-JRA02), the following conclusions can be made: - Statistical characteristics of the resistance bias factor (λR, the ratio of the measured resistance/predicted resistance) have fully reflected all uncertainty properties of parameters affecting to predicted results of pile resistance under soil base condition. With each method as well as each form of geology, there will be different statistical characteristics; - Results of research on statistical characteristics of the resistance bias factor of drilled shafts under soil base condition initially contribute to the basics of determining the resistance factors for the pile under geological