Bachelors Program in Construction Technology
Highway Engineering
•
Bachelor
Sustainable, Smart and Future Highways
Introduction
Highway engineering is moving beyond the traditional objectives of geometric design, pavement strength, capacity and safety. Modern highways must also reduce greenhouse-gas emissions, use resources efficiently, withstand climate hazards, protect ecosystems, exchange data with users and vehicles, and remain adaptable to uncertain technological change. A sustainable and smart highway is therefore not simply a road containing sensors. It is an integrated socio-technical system in which infrastructure, vehicles, people, energy, materials, information and institutions interact.
The transition is important because transport infrastructure has substantial life-cycle impacts. Carbon is emitted during extraction of aggregates, manufacture of cement and bitumen, transportation, construction, lighting, operation, maintenance and eventual demolition. At the same time, digital technologies create opportunities for better decisions, although they introduce cybersecurity, privacy, employment and accountability risks. Engineers must consequently evaluate both the benefits and the unintended consequences of innovation.
Learning Objectives
After studying this chapter, a student should be able to:
Explain sustainability and life-cycle thinking in highway engineering.
Evaluate low-carbon materials and circular construction practices.
Identify major environmental impacts and appropriate mitigation measures.
Describe the roles of ITS, BIM, GIS, drones, sensors and digital twins.
Explain how connected and autonomous vehicles may change highway design and operation.
Assess responsible uses of artificial intelligence in safety, monitoring and predictive maintenance.
Analyse implementation barriers, governance requirements and professional responsibilities.
1. Sustainability as a Highway Engineering Principle
Sustainability requires that present transport needs be met without undermining the ability of future generations to meet theirs. In highway projects, this principle is commonly interpreted through three linked dimensions:
Environmental performance: carbon emissions, energy, water, air quality, noise, biodiversity, soil and waste.
Economic performance: whole-life cost, reliability, productivity, maintainability and resilience.
Social performance: accessibility, safety, equity, public health, community severance, employment and cultural heritage.
A project can be efficient in one dimension and harmful in another. For example, a high-speed road may reduce travel time but increase land consumption, induced traffic and social displacement. Sustainable engineering therefore requires multi-criteria evaluation rather than a single cost-minimisation criterion.
Life-cycle assessment and life-cycle cost
Life-cycle assessment (LCA) estimates environmental impacts from raw-material extraction through manufacture, construction, use, maintenance and end-of-life. A simplified carbon accounting relationship is:
Here, E_total is life-cycle emissions, normally expressed in kg CO₂-equivalent; each E term is the emissions from a life-cycle stage; and C_benefits represents verified carbon removals or avoided emissions. The equation is a system boundary, not a universal prediction. Results depend on functional unit, service life, traffic assumptions, energy mix, allocation rules and data quality. A suitable functional unit might be “tonnes of CO₂-equivalent per lane-kilometre providing a specified service for 40 years.”
Life-cycle cost analysis similarly considers agency and, where appropriate, user costs:
LCC = C0 + ∑[Ct ÷ (1 + r)t]
C₀ is present construction cost, C_t is a cost occurring in year t, r is the real discount rate, and the summation covers the analysis period. Costs must be expressed in consistent monetary units and discounted only when the analysis requires present-value comparison. Salvage value, residual service life, traffic disruption and risk allowances should be included where material.
LCA and LCC should be used together. A low-cost pavement with frequent closures may be inferior to a more durable alternative when user delay and maintenance emissions are considered.
2. Low-Carbon Materials and Circular Construction
Cementitious materials
Portland cement is carbon-intensive primarily because limestone calcination releases process carbon dioxide and because kilns require high-temperature energy. Emissions can be reduced through:
supplementary cementitious materials such as fly ash, ground-granulated blast-furnace slag and calcined clay;
limestone–calcined-clay cement and other blended cements;
optimized concrete mixtures that use less binder;
recycled aggregate, where quality and durability requirements permit;
improved curing and quality control, which can extend service life.
The engineering trade-off is that some supplementary materials alter early strength, setting time, workability, sulfate resistance and durability. Mixtures must therefore be verified through specifications and laboratory testing rather than selected solely by nominal carbon content.
Asphalt and pavement materials
Reclaimed asphalt pavement (RAP) can replace part of virgin aggregate and bituminous binder. Warm-mix asphalt reduces production and compaction temperatures, potentially lowering energy use and worker exposure. Recycled plastics, crumb rubber and bio-based binders may have applications, but their performance, microplastic release, recyclability and long-term ageing require evidence.
Material selection should consider moisture susceptibility, fatigue, rutting, thermal cracking, skid resistance and maintainability. A material is not sustainable if its lower initial emissions are offset by premature failure.
Circular construction
Circular construction keeps materials at their highest practical value and reduces waste. Highway measures include:
Design for durability and adaptability: provide maintainable drainage, replaceable components and capacity for future upgrades.
Reuse in place: cold in-place recycling, full-depth reclamation and stabilisation can reduce haulage and virgin-material demand.
Material passports: record composition, location, test results and future recovery potential.
Construction waste management: segregate, test and reuse excavated soil, pavement materials and structures.
Industrial symbiosis: use suitable by-products from other industries after environmental and performance verification.
Circularity must not become a justification for unsafe material use. Leaching, contaminants, worker exposure and end-of-life separation must be assessed.
3. Environmental Impacts and Resilience
Highways affect land, water, air, climate and communities. Construction can cause erosion, sedimentation, dust, noise, habitat fragmentation and disruption of drainage. During operation, traffic emissions, tyre and brake particles, road salt and contaminated runoff may affect receiving environments.
Environmental management follows a hierarchy:
avoid sensitive locations where feasible;
minimise unavoidable disturbance through design and construction controls;
restore affected ecosystems;
compensate for residual impacts only after avoidance and mitigation.
Typical measures include wildlife crossings, erosion and sediment controls, vegetated drainage, noise barriers, low-glare lighting, dust suppression and construction timing that avoids breeding seasons. Stormwater design should account for pollutant capture, exceedance flows and maintenance access.
Climate adaptation
Climate change can increase flooding, extreme heat, wildfire, landslides, freeze–thaw damage and coastal inundation. Resilience is the ability of a highway system to maintain or rapidly restore required service under hazards. Adaptation may include higher or larger drainage structures, scour protection, heat-resistant materials, slope stabilisation, redundancy, emergency detours and condition-based maintenance.
A useful risk framework is:
Risk = Probability of hazard × Consequence of failure
This is a screening relationship, not a complete probabilistic model. Probability and consequence should be defined consistently, and uncertainty should be reported. Critical routes may require reliability analysis, scenario modelling and consideration of cascading failures such as loss of power or communications.
4. Intelligent Transport Systems
Intelligent transport systems (ITS) apply sensing, communications, computation and control to improve mobility, safety and environmental performance. Examples include:
adaptive signal control;
variable speed limits and lane-control systems;
ramp metering;
electronic tolling and traveller information;
incident detection and emergency management;
weigh-in-motion and overload enforcement;
weather and road-condition warning systems;
connected work-zone protection.
ITS architecture should distinguish the physical field layer, communications layer, data and platform layer, and user or control applications. Interoperability is essential: equipment from different suppliers should communicate through documented standards and controlled interfaces.
The performance of an ITS project should be evaluated using baseline and post-implementation data. Indicators may include crash frequency and severity, travel-time reliability, queue length, fuel consumption, emissions, response time and user equity. A system that reduces congestion at one location but diverts traffic and risk to another has not necessarily improved the network.
5. BIM, GIS and Digital Information Management
Building Information Modelling
BIM is a managed process for creating, exchanging and maintaining structured information about physical assets. In highways, a model may contain alignments, surfaces, drainage, utilities, structures, pavements, quantities, specifications, construction status and maintenance records. BIM is more than a three-dimensional drawing: its value lies in coordinated information and reliable decision-making.
A common information workflow is:
Highway digital information workflow from survey to operation
Figure: Highway digital information workflow from survey to operation
Figure: A highway information workflow linking survey, BIM, construction and operational digital-twin functions.
GIS
Geographic information systems link asset data to location. GIS supports route selection, environmental constraints mapping, pavement-condition visualisation, crash spatial analysis, drainage catchment assessment and emergency planning. Accuracy depends on coordinate reference systems, positional quality, metadata and update procedures.
Digital twins
A digital twin is a continuously or periodically updated digital representation of a physical asset or system, connected through data and models. A static BIM model is not automatically a digital twin. A twin requires an operational purpose, data connection, model logic, version control and a defined decision process. For example, sensor data may update a bridge or pavement model, while deterioration algorithms estimate remaining service life.
6. Drones, Sensors and Predictive Maintenance
Uncrewed aerial systems can collect high-resolution imagery and photogrammetric point clouds for slope inspection, earthwork measurement, bridge documentation and post-disaster assessment. They reduce exposure of inspectors to traffic and unstable terrain, but require airspace approval, competent pilots, ground control, image quality checks and privacy management.
Sensors may measure strain, vibration, temperature, moisture, axle loads, traffic volume, pavement response, visibility and water level. Sensor deployment should begin with a decision question: what action will the measurement support? Unnecessary sensors create cost, data overload and cybersecurity exposure.
Predictive maintenance estimates the probability or timing of deterioration and recommends intervention before failure. A simple condition model can be expressed as:
P(Failure by t) = f(condition, loading, environment, age, maintenance history)
Here, f is a statistical or machine-learning model and t is the analysis time. This notation describes a modelling objective, not a universal equation. Training data must represent the asset population, failure definitions and inspection practices. Predictions should include uncertainty and be validated on data not used for training.
A practical maintenance decision compares expected intervention cost with risk and service consequences. Automated recommendations should be reviewed by engineers, especially for safety-critical assets.
7. Connected and Autonomous Vehicles
Connected vehicles exchange information with infrastructure, other vehicles, networks and pedestrians. Applications include signal phase and timing information, collision warnings, cooperative merging, work-zone alerts, weather warnings and platoon coordination. Autonomous vehicles use combinations of cameras, radar, lidar, satellite positioning, maps and onboard computing to perceive and control movement.
Future highway design may need to address:
machine-readable lane markings and signs;
reliable digital maps and positioning;
roadside communication units;
secure interfaces for work zones;
safe fallback behaviour during communication or sensor failure;
charging infrastructure and energy demand;
accessibility for users who do not own automated vehicles.
Autonomy does not eliminate conventional engineering. Lane geometry, friction, drainage, visibility, roadside safety and human interaction remain important. Mixed traffic between automated and human-driven vehicles may persist for decades. Claims of safety improvement must be based on comparable exposure, reliable incident definitions and transparent evidence.
8. Responsible Artificial Intelligence
AI can classify pavement cracks, identify damaged signs, estimate traffic, detect stopped vehicles, forecast deterioration and optimise maintenance scheduling. Computer vision can process images from vehicles, drones or fixed cameras; time-series models can use traffic, weather and condition data.
Responsible AI requires:
Purpose limitation: use AI for a defined engineering decision.
Data governance: document provenance, quality, representativeness, retention and access.
Validation: test against independent field observations and relevant conditions.
Explainability: provide reasons, confidence and evidence appropriate to the decision.
Human oversight: retain accountable professional approval for high-consequence decisions.
Fairness: check whether performance differs by location, lighting, weather, road user or community.
Security and privacy: protect systems from manipulation and minimise personal data.
Auditability: retain model versions, inputs, outputs and decision records.
An AI model that detects cracks accurately on dry daytime imagery may fail at night, in rain or on unusual pavements. Accuracy alone is insufficient; false negatives may be more costly than false positives. Procurement documents should specify performance thresholds, test datasets, retraining responsibilities, ownership of derived data and procedures for system failure.
9. Illustrative Case: Smart and Circular Renewal of a Flood-Prone Highway
Background
A regional two-lane highway serves hospitals, farms and a logistics centre. It experiences periodic flooding, pavement rutting and lengthy closures. The road authority proposes a conventional resurfacing project, but the route is also a critical emergency link.
Problem and analysis
A multidisciplinary team maps flood extents in GIS, reviews five years of maintenance records, surveys pavement condition with a vehicle-mounted camera, and installs temporary water-level and temperature sensors. LCA indicates that repeated overlays would require substantial virgin aggregate and haulage. Risk analysis shows that a short closure during an extreme flood has high social consequences even though its annual probability is low.
Approach
The selected scheme combines full-depth reclamation of suitable pavement layers, a verified low-carbon binder, improved side drainage, enlarged culverts, scour protection and a raised section at the most vulnerable low point. A BIM model records materials, drainage assets and maintenance access. Cameras provide work-zone and flood alerts. A digital dashboard combines rainfall forecasts, water levels and road status. AI assists image screening, but engineers verify defects and trigger closures.
Outcome
The project reduces virgin-material demand, improves flood resilience and provides an auditable asset record. Its success is measured not only by construction cost but also by avoided closure hours, material emissions, crash risk, maintenance response time and community access. The illustrative case demonstrates that sustainability, resilience and digitalisation are most effective when designed as one asset-management problem.
Learning points
Start with service outcomes rather than technology procurement.
Combine field evidence, engineering judgement and digital models.
Treat carbon, flood risk and social access as linked decisions.
Specify ownership, maintenance and governance before installing technology.
10. Implementation, Governance and Professional Practice
Digital and sustainable projects often fail because institutional processes lag behind technology. Effective implementation requires a business case, baseline measurements, data standards, cybersecurity controls, staff training, lifecycle budgets and responsibility matrices. Pilot projects should have clear success criteria and an exit plan if benefits are not demonstrated.
Important governance questions include: Who owns sensor data? Who may access vehicle or camera information? Who is liable when an automated warning is wrong? How are procurement decisions protected from vendor lock-in? How are rural and low-income communities included? How will accessibility be maintained for pedestrians, cyclists and public transport users?
Engineers remain responsible for professional judgement even when software produces a recommendation. Safety-critical decisions should be traceable to competent review, approved standards and documented assumptions. Regulatory requirements vary by jurisdiction, but commonly concern environmental assessment, construction safety, traffic control, data protection, aviation, radio communications, accessibility and automated-vehicle testing.
Challenges and Limitations
Sustainable and smart highways face several limitations:
uncertain carbon factors and inconsistent LCA boundaries;
high initial costs and uncertain payback;
fragmented data ownership and incompatible systems;
sensor drift, missing data and poor connectivity;
cybersecurity threats to operational technology;
biased or non-transferable AI models;
rebound effects, such as induced traffic after capacity improvement;
electronic waste and energy consumption from digital infrastructure;
skills shortages and resistance to changed workflows;
unequal access to benefits and possible surveillance harms.
Technology should therefore be selected through transparent options analysis. Sometimes a simple drainage improvement, better inspection practice or safer road layout produces more benefit than an elaborate platform.
Best-Practice Framework
A robust project sequence is:
Define service, safety, environmental and equity outcomes.
Establish baseline asset, traffic, carbon and community data.
Compare alternatives using life-cycle cost, LCA, risk and resilience.
Design for durability, repairability, reuse and future adaptation.
Specify open data structures, interoperability and cybersecurity.
Pilot sensors, analytics or automation in a controlled setting.
Validate performance independently and report uncertainty.
Integrate construction records into the operational asset model.
Monitor outcomes and update models through the asset life.
Retire technologies responsibly and preserve essential service continuity.
Chapter Summary
Sustainable, smart and future highways combine low-carbon materials, circular construction, environmental stewardship, resilience and digital intelligence. Life-cycle assessment captures emissions across material, construction, operation, maintenance and end-of-life stages, while life-cycle costing compares long-term economic consequences. Circular methods such as recycling, reuse, design for durability and material passports reduce resource demand but require performance and environmental verification.
ITS improves traffic management, safety and information exchange. BIM and GIS organise spatial and asset information; drones and sensors improve inspection; digital twins connect operational data to models and decisions. Connected and autonomous vehicles may change roadway communication, markings, control and energy systems, but conventional safety engineering remains essential. AI can support detection and prediction, but responsible use requires representative data, validation, explainability, human oversight, cybersecurity and accountability. The central professional lesson is that technology is a means to achieve safe, resilient, equitable and low-impact transport—not an objective in itself.
Key Terms
Life-cycle assessment: Evaluation of environmental impacts across an asset’s life.
Life-cycle cost: Present-value assessment of costs over an asset’s analysis period.
Low-carbon material: Material designed or selected to reduce embodied greenhouse-gas emissions.
Circular construction: Construction that reduces waste and keeps materials in productive use.
Reclaimed asphalt pavement: Processed asphalt pavement reused in new pavement mixtures.
Resilience: Capacity to withstand, adapt to and recover from disruptive hazards.
Intelligent transport systems: Technologies that use sensing, communication and computation in transport operations.
BIM: Managed creation and exchange of structured information about built assets.
GIS: System for storing, analysing and displaying geographically referenced data.
Digital twin: Operational digital representation connected to a physical asset through data and models.
Internet of Things: Network of connected devices that sense, communicate and sometimes act.
Predictive maintenance: Maintenance planning based on predicted condition or failure risk.
Connected vehicle: Vehicle capable of exchanging information with infrastructure, networks or other vehicles.
Autonomous vehicle: Vehicle able to perform driving tasks with limited or no continuous human control.
Artificial intelligence: Computational methods that perform tasks associated with human cognitive functions.
Explainability: Ability to communicate how or why a model produced an output.
Model drift: Reduction in model performance as operating conditions or data change.
Interoperability: Ability of systems and organisations to exchange and use information effectively.
Embodied carbon: Emissions associated with material production, construction, maintenance and end-of-life.
Whole-life performance: Combined environmental, economic, technical and social performance over an asset’s life.
Review Questions
Short Answer Questions
What are the three dimensions of highway sustainability?
Why is life-cycle assessment preferable to considering only construction emissions?
Distinguish BIM from a digital twin.
State two uses of drones in highway engineering.
Why is human oversight important in safety-related AI applications?
Long Answer Questions
Explain how circular construction can reduce the environmental impact of highway projects, including its technical limitations.
Describe an integrated ITS architecture and discuss suitable performance indicators.
Discuss the roles of GIS, BIM, sensors and digital twins across the highway life cycle.
Evaluate the opportunities and risks associated with connected and autonomous vehicles.
Explain a responsible-AI framework for pavement-condition assessment and predictive maintenance.
Analytical / Application Questions
A highway agency is choosing between frequent resurfacing and a more durable recycled-material pavement. Develop a comparison framework using LCA, life-cycle cost, traffic disruption, risk and social access.
Design a monitoring and emergency-response system for a flood-prone highway. Identify sensors, communications, data governance, engineering actions and validation requirements.
An AI crack-detection system performs well in dry daylight tests but poorly during rain and at night. Analyse the risks and propose a responsible deployment and improvement plan.
Multiple Choice Questions with Answers
Question 1
Which statement best describes life-cycle assessment for a highway?
A. It considers only the initial construction cost.
B. It evaluates environmental impacts across defined life-cycle stages.
C. It measures only traffic congestion after opening.
D. It replaces all engineering performance tests.
Correct Answer: B. It evaluates environmental impacts across defined life-cycle stages.
Question 2
Which practice most directly supports circular highway construction?
A. Increasing single-use packaging on site
B. Using only newly quarried aggregates
C. Reusing suitable pavement materials in place
D. Removing all maintenance access points
Correct Answer: C. Reusing suitable pavement materials in place
Question 3
What distinguishes a digital twin from a static BIM model?
A. A digital twin must be printed before construction.
B. A digital twin is connected to operational data and models.
C. A digital twin contains no geographic information.
D. A digital twin is used only for architectural rendering.
Correct Answer: B. A digital twin is connected to operational data and models.
Question 4
Which is an appropriate application of GIS in highway engineering?
A. Mapping flood exposure and environmental constraints
B. Increasing cement hydration temperature automatically
C. Replacing all field inspections without validation
D. Determining legal liability without evidence
Correct Answer: A. Mapping flood exposure and environmental constraints
Question 5
Why should an AI pavement model be tested on independent data?
A. To make the model’s training data larger by duplication
B. To assess performance on data not used for training
C. To eliminate the need for engineering review
D. To guarantee that no prediction is uncertain
Correct Answer: B. To assess performance on data not used for training
Question 6
Which measure is most directly associated with climate adaptation?
A. Ignoring future rainfall changes
B. Reducing drainage capacity
C. Providing larger culverts where flood risk justifies them
D. Removing emergency detour routes
Correct Answer: C. Providing larger culverts where flood risk justifies them
Question 7
What is a principal purpose of connected-vehicle infrastructure?
A. To prevent every vehicle from using onboard sensors
B. To exchange information such as work-zone warnings
C. To eliminate the need for road markings
D. To guarantee autonomous operation in all conditions
Correct Answer: B. To exchange information such as work-zone warnings
Question 8
Which item is an example of responsible AI governance?
A. Using an unvalidated model for automatic road closure
B. Concealing model errors from operators
C. Documenting data provenance, validation and human oversight
D. Allowing unrestricted access to personal camera data
Correct Answer: C. Documenting data provenance, validation and human oversight
Question 9
In the simplified relationship Risk = Probability × Consequence, what does consequence represent?
A. The likelihood that a hazard occurs
B. The severity or impact if the failure occurs
C. The age of the pavement only
D. The number of sensors installed
Correct Answer: B. The severity or impact if the failure occurs
Question 10
Which statement about ITS evaluation is most appropriate?
A. Only equipment purchase price should be measured.
B. Performance should be compared with a baseline using relevant indicators.
C. A reduction in congestion at one site always proves network benefit.
D. User equity is unrelated to transport-system evaluation.
Correct Answer: B. Performance should be compared with a baseline using relevant indicators.