Reducing GeoRisks thru Data Driven Decision Making

Bauer

B-Tronic

System

Southwest Geotechnical Conference

24 June 2021

Reducing GeoRisks thru

Data Driven Decision Making

Informed > Guesstimates

Dr. Antonio Marinucci, MBA, PE

President, V2C Strategists LLC

2 ©Marinucci; V2C Strategists, LLC, 2021SWGEC 2021 (Virtual) – 24 June 2021

• Data Capture & Analysis – Small-scale

• MWD & Large-scale Construction Methods

• Take Away Points

Agenda


• It is understood

• Soil conditions → heterogeneous & variable

• Many construction methods alter the soil mass (& in-situ properties)

• Acknowledging shortfalls

• Sampled volume → very minor representation of total project volume ( < 0.000…% ? )

• Design methods & numerical constitutive models → cannot possibly account for all possibilities in soil

variability & construction-induced effects

• Addressing the unknowns

• Improve current practices → use new technologies for more accurate & better performing designs?

• Obtain reliable information for 100% of elements to accurately predict resistance & behavior?

• How → costs & time & implementation?

Decisions with Limited Data


• Can we improve current practices using new technologies?

• How is practice typically performed?

• How long does design / construction take?

• Risks & who bears them?

• What do you really get? Is there something better?

• How can we build upon each phase - develop clearer picture

• What’s out there?

• Develop bespoke systems? Adapt & evolve?

• Innovations should provide

• Robustness & reliability

• Improved quality in the data

• Economical solutions

• Faster, better, & more durable deliverables / products

Investing In Technological Advancements

Data Capture & Analysis -

Site Characterization & Lab Testing


• Prof. K.K. Phoon, National University of Singapore

• Any site characterization methodology that relies solely on measured data, both site-specific data

collected for the current project & existing data of any type collected from past stages of same project

or past projects at same site, neighboring sites, or beyond

• Ching & Phoon (2020)

• Records are weighted by similarity measures and combined with limited site-specific data to construct

a quasi-site-specific transformation model based on geotechnical characteristics relevant to the site

Data-driven Site Characterization

Ching, J.Y. & Phoon, K.K. (2020). “Measuring

similarity between site-specific data & records

in a geotechnical database.” ASCE-ASME

Journal of Risk & Uncertainty in Eng.

Systems, Part A: Civil Engineering, 6(2)


Generic Property Databases (ISSMGE TC304)

Database Reference Parameters of interest# Data points

# Sites /studies

Range of parameters

OCR PI St

CLAY/5/345Ching & Phoon

(2012)LI, su, su

re, σ’p, σ

’v 345

37Sites

1 – 4 —Sensitiveto quick

CLAY/6/535Ching et al.

(2014)

su/σ'v, OCR, (qt-σv)/σ

'v, (qt-u2)/σ

'v,

(u2-u0)/σ'v, Bq

53540

Sites1 – 6

Low tovery high

Insensitiveto quick

CLAY/7/6310Ching & Phoon (2013, 2015)

su from 7 different test procedures

6310164

Studies1 – 10

Low tovery high

Insensitiveto quick

CLAY/10/7490Ching & Phoon

(2014)LL, PI, LI, σ'

v/Pa, St, Bq, σ'p/Pa,

su/σ’v, (qt-σv)/σ

'v, (qt-u2)/σ

'v

7490251

Studies1 – 10

Low tovery high

Insensitiveto quick

CLAY/9/249D'Ignazio et al.

(2019)σ'

p/Pa, σv/Pa, σ'v/Pa, qt/Pa,

u2/Pa, u0/Pa, PI, wn, St

24918

Sites1 – 10

Low tovery high

Insensitiveto quick

FG/KSAT/4/1358Feng &

Vardanega (2019)

e, LL, wn/LL,-ln(ksat)

135833

Studies—

Low tovery high

—


1. Ugly Data

• MUSIC – 3X

2. Site recognition

• Quantify site uniqueness so big indirect databases (BIDs) can be combined

with sparse site-specific data in a manner sensitive to site differences

3. 3D Stratification

• Can we produce representation with explicit uncertainty quantification (e.g.,

range of simulated 3D profiles associated with different likelihoods) at a

reasonable cost based on measured MUSIC-3X data alone?

3 Challenges (Phoon, 2021)

MUSIC - 3X

MULTIVARIATE

UNCERTAIN

& UNIQUE

SPARSE

INCOMPLETE 3D SPATIAL

VARIATION

(potentially)

CORRUPTED


Data-driven Site Characterization (Ching & Phoon, 2019)

• Bayesian machine learning

• Standard normal distribution (, C)

• Mean vector (); Covariance matrix (C)

• Standard normal distribution (, C, Xu)

• Missing data (Xu)

• Gibbs sampler + conjugate prior

• f(|C, Xu, DATA) multivariate normal if f() multivariate normal

• f(C|, Xu, DATA) inverse wishart if f(C) inverse wishart

• f(Xu|,C, DATA) multivariate normal

• Sample iteratively from f(|C,Xu,DATA), f(C|,Xu,DATA), f(Xu|,C,DATA)

Ching, J.Y. & Phoon, K.K. (2019). “Constructing site-specific

probabilistic transformation model by Bayesian machine

learning.” J. of Engineering Mechanics, ASCE, 145(1)


Data or Information? (ASCE GeoInstitute)

• Facilitates exchange of data

• Current practice

• Manual recording, handling, & input

• Time consuming & costly

• Rife for error (data entry & use)

• What is it? Common Data Structure

• Capture information from site investigation

through production works & load testing

• Data entered once & not manipulated again

• Reduce time & expenses & errors

• Improve quality & usability

• Data management

• Data obtained to level of quality & structure

• Readable & usable from system to system

Large-scale Construction Methods –

Installation of Deep Foundations


• More locations = more data = better understanding = less uncertainty

• i.e., lower risk = fewer consequences = higher ROI

• Full-scale installation

• Can we use the data being generated? In real-time? How?

• How do we implement? Now or in future?

• Modified observational method - verification or progressive design?

• Benefits include

• Real-time & recorded logs of productivity & progress

• Proof of performance & measurement of quality

• Assurance that required metric and/or resistance is achieved

• Display & analysis of production & performance

• Calculation and/or measurement of quantities

• QA - Satisfies project reporting requirements for submittals

What Can We Do? Why?


• Integrated onboard computer systems (& sensors)

• Monitor operating parameters of the machine (fleet

management & maintenance)

• Monitor various drilling parameters

• Optimize drilling performance & production rates

• Automate control of some operations

• Address drilling process, spatial uncertainty, & material

property assessment

• Record data & prepare detailed reports

• Make data available for download and/or transmission

• Real-time monitoring

• Permits observation & documentation of operational

conditions of equipment

• Permits real-time QA & QC during construction

Real-time Monitoring during Installation (MWD)

Bauer BG39 in

Cased CFA (CCFA)

piling mode

Courtesy of BAUER

Maschinen GmbH


Full-scale Production Installation Courtesy of BAUER

Maschinen GmbH

Bauer BG39 in

Cased CFA (CCFA)

piling mode

View inside operator’s cab

Parameters & records

can be visualized using

displays, downloaded,

and/or transmitted to

other devices


• Select parameters for typical installation methods include:

• Some parameters can be controlled automatically

• Spatial positioning & as-builts

• Some systems use conventional 2D or satellite map view

• Geo-tracking & location of equipment and drilling operations

• Monitor in cab displays positioning of piles using GPS along with project information

• Coordinate-based positioning to minimize out-of-tolerance piles

• Using input coordinates

• Target and as-built locations recorded and included in the report log (e.g., for BIM)

MWD - Production Parameters

• Depth of tooling / hole / casing • Penetration rate • Concrete pressure

• Rotation speed • Rotary torque (pressure) • Concrete volume

• Crowd pressure/force • Mast alignment • Lifting speed

• Specific energy used • Diameter of hole • Overbreak


Impact Pile DrivingWith accelerated hydraulic

hammer, trend indicator

alerts when the pile has

reached its max. resistance

Vibratory Pile DrivingDifferent modes adjust

speed & amplitude for

specific soil conditions &

types of driving elements

CFA Pile DrillingAutomated controls adjust

crowd force & rotational

speed (penetration rate)

Kelly DrillingDisplay of relevant

parameters & features

for safe operation

QA - Reporting & Documentation

• Display of production data for assessment of productivity

• Produce a daily report of productivity

• Standard reports – documenting construction site progress

& proof of performance and quality

Visualization & Reporting Courtesy of BAUER

Maschinen GmbH


• Automated Monitoring Equipment (AME)• Depth sensor

• Pile depth & rates of penetration / withdrawal

• Rotation & rotary head pressure sensors

• Rotation rate & torque

• Flow meter

• Volume of grout / concrete pumped

• Pressure sensors

• Injection pressure of grout / concrete

• Computer system assists to ensure• Automated control of drilling and/or extraction

• Adjust crowd force & rotational speed to control

penetration rate

• Optimal filling level of auger

• Prevent corkscrew effect, overmining of soil,

& blockages in lines

• Optimize concrete consumption

• High quality pile

Onboard Sensing & AME - CFA / ACIP Piles

Courtesy of Berkel & Company

Typ ACIP

pile rig set up


MWD & AME - CFA / ACIP Piles (Marinucci et al, 2021

DFI Journal)


• Automate different processes

• Drilling - optimization

• Rate of penetration & crowd force → based on rotational speed of tool

• Concreting - control

• Speed of extraction controlled based on flow rate of concrete

(minimize necking potential)

• α-value

• Based on measured rate of penetration & torque during drilling / penetration

• Real-time indication of strength, density, and/or soil’s load bearing

characteristics of soil (at tip)

BUT• Calibrate drilling parameters (& 𝛼-value) based on site characterization

program & load tests

Drilled, Full-Displacement Piles (FDPs) Courtesy of BAUER

Maschinen GmbH

Bauer BG39

in FDP mode


Drilled, Full-Displacement Piles (FDPs) Courtesy of BAUER

Maschinen GmbH

Bauer BG39 with FDP tool


• MWD Drilling Resistance vs. Rock Strength (BDV31-977-20; BDV31-977-91)

• Objectives:• Correlations - Side Resistance vs. MWD Specific Energy in laboratory & field

• Validate MWD correlations

• Reassess LRFD ϕ factors

• Develop MWD QA/QC tool for drilled shaft construction

• Drilling parameters & AME• Torque, crowd force, penetration rate, rotational speed, bit diameter, & tooling type

• Must validate with results from site characterization, lab testing, & load testing

Research Efforts – Univ of FL (McVay, Rodgers, et al)

Take-away Points


• Getting to full implementation more than just drilling & technology concerns

• Hurdles that need to be addressed include

• How much research required to develop valid relationships among different key parameters?

• Are the measurements & processes repeatable?

• What key parameters must be monitored?

• Who will pay for this research? Cooperation & synergies with other researchers/countries?

• Can reliable design methods be gleaned from results of MWD?

• Can data generated during drilling be integrated into DIGGS (other?) & how?

• Contracting mechanism & incorporation of MWD into the contract documents?

• Protecting contractor’s privileged & competitive information?

• How will design be addressed & by whom?

• Who owns risk to design & performance?

Key Take-away Points - Concerns


• Potential benefits to implementation of MWD, including

• Enhanced knowledge of subsurface conditions

• Comprehensive records for each hole drilled (& concreted)

• Reduced uncertainty due to spatial variability

• Design optimization

• Cost-efficient construction

• Accurate delineation of top of rock

• Reduced risk & contingency (remuneration and/or time extensions for true DSCs)

• “Real time" evaluation (QC/QA) & improved quality of each pile on a project

• Documentation to ensure achievement of project specifications

Key Take-away Points - Benefits


Thank You For Your Time & Attention!

Dr. Antonio Marinucci, MBA, PEPresident

Mobile: +1 (347) 670.2006

Email: [email protected]

Documents

Reducing GeoRisks thru Data Driven Decision Making