PHMAP’23

Prognostic Discriminator under Uncertainty
Civil Engineering domain
3 sessions and 10 tittles
https://phmap.jp/submission-os-list/
Aug 10, 2023
Organizer Takato YASUNO
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PHMAP’23
Session outline
Prognostic Discriminator under Uncertainty― Recognition technology of prognostic change in civil engineering
to respond to uncertain environment ―• Our standard of living has been in the midst of weather crisis such as heavy rains anddroughts. In the construction domain, the aging and declining workforce has become
problematic. Therefore, it is essential to continuously use existing civil and architectural
structures which support daily activities in urban and rural areas.
• According to the refinement of BIM/CIM design information, and data acquisition fromdrones and surveillance cameras inspections task for diagnosis and prognosis has been
improved using computer vision and robotics. These technologies have made it possible
to recognize surface damage and detect structural deformation at a single point in time.

• However, the methodology for multi-periods of prognosis systematically andcontinuously comparing the initial and post-event states, such as capturing changes overtime, has only just begun, and it is critical for making urgent and prioritized repair andrenewal decision.
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Session outline

PHMAP’23• In this session, as an enabler of sustainable use in the civil engineering and architectural
structures, we will address monitoring the state of structures and attempt to induce
innovation in science and technology that focuses prognostic changes over time.
• For example, we should consider various structures such as buildings, bridges, tunnels,railways, dams, rivers, sewers, and landslides.
• From a manager point of view, we aim to solve problems such as predictive maintenanceand anomaly detection in daily inspections. In case of natural disaster, we would address
earthquake/flood risk assessments, business continuity management and deteriorationassessments.
• Recent digital infrastructure has been required for the prognosis of deterioration of aging
structures, inspections and forecasts.
• We will collaborate on practical methodology and share experimental empirics based on
prognosis support technology and optimization of repair and renewal.
• We discuss the deployment of digital infrastructure, practical technology that recognizes
prognostic changes and anomalous deformation, visualization of damage scoring
indicators for structural monitoring, and opportunities for research and development.
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PHMAP’23“accelerated increase”

structural damage

Perspective for Prognostics

Focus1:“surely increase”“almost constant”
Initial t

t+1 Next

Multi-period Discriminator for Prognostics

t+htime step

FutureFocus2: Prognostics for Uncertain Environment
Figure. Predictive Management for Multi-period Prognostics

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PHMAP’23
Participants/Tittle in Civil Session 1: Concrete DefectID

TargetParticipants,Affiliation

Tittle (words)

Connect
Keyword3455

BridgeTakahiro Saitoh〇,
Gunma University

Automatic Detection of Concrete Surface Defects
Using Deep Learning and Laser Ultrasonic
Visualization Testing

Concrete
Defect9292

BridgeHitoshi Ito,
YACHIYOEngineering, Co.,Ltd.

Observation Maintenance for Bridges Using Early
Detection of Deterioration Progress

Concrete
Defect6224

Dam

Akira Ishii,
YACHIYO RIIPS

Study on the Estimation of Concrete Defects
Volume on Dam Body Surface

Concrete
Defect5571

BridgeKosuke Kinoshita,
NEC, Co.Ltd.

Outlier Analysis of Bridge Deflections Using
Satellite SAR & Structural Simulation: Case Study on Collapse Accident in Water Pipe
Bridge in Japan

ConcreteDefect5PHMAP’23
Participants/Tittle in Civil Session 2: Damage Detection
IDTargetParticipants, Affiliation

Tittle (words)

Connect
Keyword3900

Railway

Takato Yasuno〇,
Masahiro Okano, Junichiro
Fujii, YACHIYO RIIPS

Wooden Sleeper Deterioration Detection forRural Railway Prognostics Using
Unsupervised Deeper FCDDs

DamageDetection

7461

Road

Masato Tsuchiya, Ken
Miyamoto, Yasushi Sugama,
Mitsubishi Electric, Co.

Road Infrastructure Maintenance throughAnomaly Detection in Drive Recorder
ImagesDamage
Detection

0131

Road

Yuta Shirakawa, Satoshi Ito,et al., TOSHIBA Co.

Automating Daily Inspection forExpressways Using Anomaly Detection
ModelDamage
Detection

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PHMAP’23
Participants/Tittle in Civil Session 3: Digital Twin
ID

Target7529

Building

2733

4399

Participants, Affiliation

Tittle (words)

Connect
Keyword

Yasutaka Narazaki〇,Zhejiang University

Digital Twin of Built Structures assistedby Computer Vision Techniques: Overview and Preliminary Results

Digital Twin

Road
Railway

Antonio J. Guillén López, Juan
Fco. Gómez Fernández, et al.
University of Seville (Spain)

Experimental Platform for Digital Twin
Development in Railway MaintenanceDigital Twin

RoadRailwayTomoya Abe, Nakasha Creative, Efficient Inspection of Civil Engineering
Co.Ltd.
Structures for Railways and Roads Using
Images and GNSS

Digital Twin

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Civil Engineering Organized Scope-I

PHMAP’23

TargetStructure

Keywords for Multi-period Discriminator

Prognostics for Uncertain Environment

City Zone

Satellite imagery, multi- / hyper-spectral band Urbanization forecast, Carbon reduction policy,mapping, Normalized Difference X(Vegetation population density, urban thermal climate, local/Water/Snow) Index, LULC (Land Use/Cover), climate zone(LCZ), world urban database,change detection, GIS layer segment analysis scenario redesign urban and rural region

Building

Drone monitoring, visual inspection,
deterioration of exterior wall, floating anduneven surface, equipment anomalies

Post-quake emergency assessment, BIM
structural analysis, safety diagnosis

Plaza
Sidewalk

Surveillance camera, changes in people flow,
congestion density visualization

Visualize living environment, safety ofgatherings, emergency response measures

Railway

Mobile on-board camera, bolt deformation
detection, early detect anomalies, signal ofequipment failureRailway track maintenance, predictive
maintenance, level of service, management of
trackside planting

TunnelSurveillance camera, prevention/removal ofdelamination/peeling, early detect ofdeterioration

Vehicle safety management, prevention of
third-party injury, groundwater influence,backside cavities in lining

Note. The abovementioned keywords are not limited, so new ideas, methodology and experimental lessons
are fine to submit to any paper for Civil Engineering prognostic.

8Civil Engineering Organized Scope-II

PHMAP’23

TargetPrognostics for Uncertain Environment

Keywords for Multi-period Discriminator

BridgeEarly deterioration, drone monitoring, safe visual Predictive maintenance, latent/extensive stage,inspection on elevated area, non-destructive
degradation factors, coastal salt damage, frost
survey, crack, exposed rebar, corrosion
damage, vehicle load, sedimentation, fatigue

Dam

Drone monitoring, autonomous flight in higharea, displacement from the initial stage, early
detect of deformation

Initial condition at completion stage, CIM, safety
diagnosis, predictive maintenanceRiver

Surveillance camera, management indicator ofwater surface condition, visualize riverenvironment

River environment preservation, water quality,odor, river landscape, chemical measure decision
supportRoad

On-board camera, road surface, asphalt damage
detection, surveillance camera, visualize
hazardous vehicle flow lines, emergency alerts

Anomaly detection and removal of falling objects,
planning repair measure, synchronization ofsection construction, accident prevention atintersections, rehabilitation design

Landslide

Condition monitoring before/after heavy rain,
Slope preservation, avoidance of third-party injury,
risk/emergency assessment of landslide, near real preliminary area hazard assessment
time satellite monitoring
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Innovative solutions for thesociety10