Geotechnical and safety protective equipment planning using range point cloud data and rule checking in building information modeling

Abstract After experiencing 806 fatalities in 2012, safety continues to be among the top concerns in the US construction industry. Among all construction operations, excavation is one of the most hazardous because of its inherent consequences from potential cave-ins, falls, and contacts of workers-on-foot with equipment or unknown objects. Current design, planning, and inspection of safety equipment at excavation sites is insufficient as it is still done manually, infrequently, time-consuming, and prone to human error. A new method is presented that semi-automatically identifies fall and cave-in hazards related to excavation pits and models, among other temporary geotechnical excavation objects, the required fall protection equipment. The approach first extracts relevant fall risk criteria from safety rules and regulations published by the Occupational Safety and Health Administration (OSHA) and applied in industry best practices. Three-dimensional (3D) range point clouds from the excavated pits are then collected to measure the geometrical properties of the pit. An algorithm extracts height information automatically to identify and locate fall hazards. The integration of geometric parameters with geotechnical and safety regulations finally results in a building information model (BIM) that includes the installation of safety equipment. An experimental field trial demonstrates the applicability of the developed method for successful use by practitioners in the industry.

[1]  Frédéric Bosché,et al.  Automated recognition of 3D CAD model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction , 2010, Adv. Eng. Informatics.

[2]  Roger V. Bostelman,et al.  Methods for improving visibility measurement standards of powered industrial vehicles , 2014 .

[3]  Jochen Teizer,et al.  Visibility-related fatalities related to construction equipment , 2011 .

[4]  Burcu Akinci,et al.  TECHNOLOGY AND PROCESS ASSESSMENT OF USING LADAR AND EMBEDDED SENSING FOR CONSTRUCTION QUALITY CONTROL , 2005 .

[5]  J Harold Deatherage,et al.  Neglecting safety precautions may lead to trenching fatalities. , 2004, American journal of industrial medicine.

[6]  Thomas K. Peucker,et al.  2. Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature , 2011 .

[7]  Jochen Teizer,et al.  Automating the blind spot measurement of construction equipment , 2010 .

[8]  Roger V. Bostelman,et al.  Improved Methods for Evaluation of Visibility for Industrial Vehicles Towards Safety Standards , 2013 .

[9]  Charles M. Eastman,et al.  Building Information Modeling (BIM) and Safety: Automatic Safety Checking of Construction Models and Schedules , 2013 .

[10]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .

[11]  Silvio Savarese,et al.  Application of D4AR - A 4-Dimensional augmented reality model for automating construction progress monitoring data collection, processing and communication , 2009, J. Inf. Technol. Constr..

[12]  Jimmie Hinze,et al.  Autonomous pro-active real-time construction worker and equipment operator proximity safety alert system , 2010 .

[13]  Jochen Teizer 3D range imaging camera sensing for active safety in construction , 2008, J. Inf. Technol. Constr..

[14]  Carlos H. Caldas,et al.  Real-Time Three-Dimensional Occupancy Grid Modeling for the Detection and Tracking of Construction Resources , 2007 .

[15]  Martial Hebert,et al.  Fully automatic registration of multiple 3D data sets , 2003, Image Vis. Comput..

[16]  Jochen Teizer,et al.  Computing 3D blind spots of construction equipment: Implementation and evaluation of an automated measurement and visualization method utilizing range point cloud data , 2013 .

[17]  Jochen Teizer,et al.  Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system , 2014 .

[18]  Patricio A. Vela,et al.  Performance evaluation of ultra wideband technology for construction resource location tracking in harsh environments , 2011 .

[19]  Burcu Akinci,et al.  Quantification of edge loss of laser scanned data at spatial discontinuities , 2009 .

[20]  Osama Moselhi,et al.  Integrating 3D laser scanning and photogrammetry for progress measurement of construction work , 2008 .

[21]  Stephanie G. Pratt,et al.  BUILDING SAFER HIGHWAY WORK ZONES: MEASURES TO PREVENT WORKER INJURIES FROM VEHICLES AND EQUIPMENT , 2001 .

[22]  Dulcy M. Abraham,et al.  Fatalities in Trenching Operations—Analysis Using Models of Accident Causation , 2004 .

[23]  Burcu Akinci,et al.  Automatic Reconstruction of As-Built Building Information Models from Laser-Scanned Point Clouds: A Review of Related Techniques | NIST , 2010 .

[24]  Mani Golparvar-Fard,et al.  Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques , 2011 .

[25]  Carlos H. Caldas,et al.  Human-Assisted Obstacle Avoidance System using 3D Workspace Modeling for Construction Equipment Operation , 2006 .

[26]  Tao Cheng,et al.  Modeling Tower Crane Operator Visibility to Minimize the Risk of Limited Situational Awareness , 2014 .

[27]  Ernst Rank,et al.  Integration of Geotechnical Design and Analysis Processes Using a Parametric and 3D-Model Based Approach , 2011 .

[28]  Patricio A. Vela,et al.  A Sparsity‐Inducing Optimization‐Based Algorithm for Planar Patches Extraction from Noisy Point‐Cloud Data , 2015, Comput. Aided Civ. Infrastructure Eng..

[29]  Seokho Chi,et al.  Image-Based Safety Assessment: Automated Spatial Safety Risk Identification of Earthmoving and Surface Mining Activities , 2012 .