AquaTwinX

EDUCATION & CONTINUOUS LEARNING

Applied Curriculum for
Intelligent Infrastructure.

06 learning pathways36 structured modulesPractice-oriented outcomes
01 / 06 · PROFESSIONAL LEARNING PATHWAY

PE Civil Exam Preparation

Intended audience: Civil engineering graduates and examination candidates

6
MODULES

Learning objectives

  • Interpret the PE Civil examination structure and establish a diagnostic baseline
  • Strengthen breadth and discipline-specific technical competency
  • Use performance evidence to prioritize study time and improve examination readiness

Expected outcome

A documented preparation pathway informed by topic-level evidence and timed practice performance.

MODULE 01

Orientation & Diagnostic Baseline

Exam structure, calculator and reference-handbook use, initial mixed-topic diagnostic.

MODULE 02

Civil Engineering Fundamentals

Mathematics, probability, ethics, economics, project planning, mechanics, materials, and site development.

MODULE 03

Water Resources & Environmental

Hydrology, open-channel flow, pressure systems, groundwater, treatment, conveyance, and environmental processes.

MODULE 04

Discipline-Focused Practice

Targeted practice in structural, geotechnical, transportation, construction, or water-resources depth topics.

MODULE 05

Timed Simulation & Remediation

Timed mixed sets, error classification, weak-topic intervention, and repeat assessment.

MODULE 06

Readiness Review

Trend interpretation, predicted performance, final study priorities, and examination strategy.

APPLIED WORK

Complete a diagnostic test, four targeted practice cycles, and a final timed simulation with a documented remediation plan.

Enter PE learning portal →
02 / 06 · PROFESSIONAL LEARNING PATHWAY

GIS for Infrastructure

Intended audience: Engineers, planners, analysts, field personnel, and asset managers

6
MODULES

Learning objectives

  • Design and maintain fit-for-purpose utility spatial data
  • Apply network, proximity, overlay, and risk-analysis methods
  • Publish decision-ready maps, dashboards, and repeatable geoprocessing workflows

Expected outcome

A governed infrastructure GIS workflow that supports reliable mapping, analysis, prioritization, and communication.

MODULE 01

Utility GIS Foundations

Coordinate systems, data models, topology, metadata, domains, subtypes, and asset identifiers.

MODULE 02

Data Development & Quality

Field collection, editing controls, completeness, positional accuracy, lineage, and QA/QC.

MODULE 03

Spatial & Network Analysis

Buffers, overlays, service areas, tracing concepts, accessibility, exposure, and impact analysis.

MODULE 04

Asset Risk & Criticality

Failure, consequence, vulnerability, equity, and service-impact layers for prioritization.

MODULE 05

Web GIS & Dashboards

Operational maps, dashboard design, mobile workflows, permissions, and stakeholder communication.

MODULE 06

Automation & Capstone

Python/ArcPy concepts, repeatable geoprocessing, scheduled updates, and an infrastructure decision-support product.

APPLIED WORK

Develop an asset-risk map and web dashboard using a documented data model and reproducible analysis workflow.

03 / 06 · PROFESSIONAL LEARNING PATHWAY

Hydraulic Modeling

Intended audience: Water, wastewater, stormwater, and planning engineers

6
MODULES

Learning objectives

  • Develop reliable models from GIS, demand, asset, and operational information
  • Evaluate calibration quality, assumptions, sensitivity, and uncertainty
  • Translate model results into clear engineering recommendations

Expected outcome

A calibrated model and defensible scenario analysis that can inform planning, design, and operational decisions.

MODULE 01

Hydraulic Principles & Model Governance

Continuity, energy, losses, network representation, model purpose, documentation, and version control.

MODULE 02

WaterGEMS for Distribution Systems

Model construction, demands, controls, fire flow, pressure, water age, scenarios, and alternatives.

MODULE 03

EPANET Modeling & Automation

Core EPANET workflow, extended-period simulation, water quality, Python integration, and batch analysis.

MODULE 04

InfoWorks ICM for Integrated Systems

Collection-system representation, rainfall-runoff, 1D/2D concepts, controls, flooding, and integrated scenarios.

MODULE 05

Calibration & Validation

SCADA and field-data preparation, residual analysis, parameter adjustment, performance criteria, and uncertainty.

MODULE 06

Planning Capstone

Existing-condition assessment, future demands, failure scenarios, improvement alternatives, and technical reporting.

APPLIED WORK

Construct or refine a network model, calibrate it against observations, and present a scenario-based capital or operational recommendation.

04 / 06 · PROFESSIONAL LEARNING PATHWAY

AI & Machine Learning

Intended audience: Engineers, analysts, researchers, and technical managers

6
MODULES

Learning objectives

  • Prepare infrastructure data for statistically defensible modeling
  • Develop, compare, and interpret supervised machine-learning models
  • Evaluate performance, calibration, uncertainty, fairness, and operational usefulness

Expected outcome

An explainable predictive workflow with appropriate validation, performance measures, and limitations.

MODULE 01

Analytical Foundations

Problem definition, target design, leakage prevention, sampling, bias, and reproducibility.

MODULE 02

Data Preparation & Feature Engineering

Missingness, encoding, scaling, outliers, temporal variables, spatial features, and class imbalance.

MODULE 03

Predictive Modeling

Regression, decision trees, random forests, gradient boosting, and survival-analysis concepts.

MODULE 04

Validation & Performance

Train-test design, cross-validation, ROC/PR analysis, calibration, top-k capture, and threshold selection.

MODULE 05

Explainability & Prescriptive Analytics

Feature importance, SHAP concepts, scenario testing, optimization, and decision rules.

MODULE 06

Responsible AI Capstone

Documentation, governance, human oversight, monitoring, limitations, and communication to decision-makers.

APPLIED WORK

Develop and evaluate an explainable infrastructure-risk model, then translate its output into a prioritized intervention strategy.

05 / 06 · PROFESSIONAL LEARNING PATHWAY

AI in Water Utilities

Intended audience: Utility leaders, engineers, operators, planners, and technology teams

6
MODULES

Learning objectives

  • Identify high-value and feasible AI applications across the utility lifecycle
  • Design trustworthy knowledge, prediction, and automation workflows
  • Establish governance, cybersecurity, validation, and human-accountability controls

Expected outcome

A governed AI use case with defined data requirements, human controls, performance measures, and implementation value.

MODULE 01

AI Opportunity Framework

Utility challenges, use-case screening, value, feasibility, risk, maturity, and prioritization.

MODULE 02

Generative AI & Utility Knowledge

Retrieval-augmented generation, source grounding, technical documents, citations, and KnowledgeGPT concepts.

MODULE 03

Predictive Utility Analytics

Demand, failures, water quality, energy, maintenance, anomaly detection, and forecast evaluation.

MODULE 04

AI-Driven Asset Management

Horizontal and vertical assets, condition, failure risk, intervention timing, and prescriptive strategies.

MODULE 05

Agents & Intelligent Workflows

Data QA/QC, model updates, engineering review, workflow orchestration, and escalation controls.

MODULE 06

Governance & Implementation Capstone

Data governance, privacy, cybersecurity, model risk, adoption, monitoring, and an implementation roadmap.

APPLIED WORK

Prepare a utility AI implementation charter defining the problem, data, architecture, controls, performance indicators, and adoption plan.

06 / 06 · PROFESSIONAL LEARNING PATHWAY

Digital Transformation

Intended audience: Utility executives, program leaders, engineers, and digital teams

6
MODULES

Learning objectives

  • Assess organizational, data, technology, and governance maturity
  • Align digital initiatives with service, resilience, financial, and workforce outcomes
  • Develop a practical implementation and change-management roadmap

Expected outcome

A phased transformation roadmap aligned with utility priorities, organizational readiness, governance, and measurable public value.

MODULE 01

Strategy & Readiness

Vision, operating challenges, maturity assessment, stakeholder alignment, and target outcomes.

MODULE 02

Data & Integration Foundation

Governance, architecture, interoperability, APIs, quality, ownership, and lifecycle management.

MODULE 03

Digital Twins & Dynamic Planning

Twin maturity, system context, dynamic master planning, scenario analysis, and decision feedback.

MODULE 04

Digital Operations & Capital Delivery

Connected workflows, field mobility, smart CIP, performance management, and portfolio integration.

MODULE 05

People, Governance & Adoption

Roles, skills, leadership, operating model, policy, change management, and institutional learning.

MODULE 06

Value Realization Capstone

Benefits, cost, risk, KPIs, phased delivery, investment logic, and continuous improvement.

APPLIED WORK

Create a three-horizon digital-transformation roadmap with prioritized initiatives, governance responsibilities, KPIs, and implementation dependencies.