A crash avoidance framework for heavy equipment control systems using 3D imaging sensors

This paper presents a preliminary crash avoidance framework for heavy equipment control systems. Safe equipment operation is a major concern on construction sites since fatal on-site injuries are an industry-wide problem. The proposed framework has potential for effecting active safety for equipment operation. The framework contains algorithms for spatial modeling, object tracking, and path planning. Beyond generating spatial models in fractions of seconds, these algorithms can successfully track objects in an environment and produce a collision-free 3D motion trajectory for equipment.

A methodology for object identification and tracking in construction based on spatial modeling and image matching techniques

Understanding the motion characteristics of on-site objects is desirable for the analysis of construction work zones, especially in problems related to safety and productivity studies. This article presents a methodology for rapid object identification and tracking. The proposed methodology contains algorithms for spatial modeling and image matching. A high-frame-rate range sensor was utilized for spatial data acquisition. The experimental results indicated that an occupancy grid spatial modeling algorithm could quickly build a suitable work zone model from the acquired data. The results also showed that an image matching algorithm is able to find the most similar object from a model database and from spatial models obtained from previous scans. It is then possible to use the matched information to successfully identify and track objects.

Object identification based on 3D spatial models of construction sites

On obstacle-cluttered construction sites, understanding the motion characteristics of objects is important for anticipating collisions and preventing accidents. This study investigates algorithms for object identification applications that can be used by heavy equipment operators to effectively monitor congested local environment. The proposed framework contains algorithms for three-dimensional spatial modeling and image matching that are based on 3D images scanned by a high-frame rate range sensor. The preliminary results show that an occupancy grid spatial modeling algorithm can successfully build the most pertinent spatial information, and that an image matching algorithm is best able to identify which objects are in the scanned scene.

Development of an automated safety assessment framework for construction activities

This paper presents an ongoing research project concerning the development of an automated safety assessment framework for earthmoving and surface mining activities. This research seeks to determine data needs for safety assessment and investigates how to utilize collected data to promote more informed and efficient safety decision-making. The research first examined accidents and fatalities involved with earthmoving and surface mining activities—more specifically, those involving loading, hauling, and dumping operations,—investigated risk factors involved with the accidents, and finally identified data needs for safety assessment based on safety regulations and practices. An automated safety assessment method was then developed using the data needs that had been identified. This research is expected to contribute to the introduction of a fundamental framework for automated safety assessment and the systematic collection of safety-related data from construction activities. Implementation of the entire safety assessment process on actual construction sites remains a task for future research.

Modeling object identification and tracking errors on image-based safety assessment of earthmoving operations

Object identification and tracking have become critical for automated on-site construction safety assessment. The primary objective of this paper is to present the development of a testbed to analyze the impact of object identification and tracking errors caused by data collection devices and algorithms used for safety assessment. The testbed models workspaces for earthmoving operations and simulates safety-related violations, including speed limit violations, access violations to dangerous areas, and close proximity violations between heavy machinery. Three different cases were analyzed based on actual earthmoving operations conducted at a limestone quarry. Using the testbed, the impacts of device and algorithm errors were investigated for safety planning purposes.

Automated object identification using optical video cameras on construction sites

Visual recording devices such as video cameras, CCTVs, or webcams have been broadly used to facilitate work progress or safety monitoring on construction sites. Without human intervention, however, both real-time reasoning about captured scenes and interpretation of recorded images are challenging tasks. This article presents an exploratory method for automated object identification using standard video cameras on construction sites. The proposed method supports real-time detection and classification of mobile heavy equipment and workers. The background subtraction algorithm extracts motion pixels from an image sequence, the pixels are then grouped into regions to represent moving objects, and finally the regions are identified as a certain object using classifiers. For evaluating the method, the formulated computer-aided process was implemented on actual construction sites, and promising results were obtained. This article is expected to contribute to future applications of automated monitoring systems of work zone safety or productivity.

Image-based safety assessment : automated spatial safety risk identification of earthmoving and surface mining activities

This paper presents an automated image‐based safety assessment method for earthmoving and surface mining activities. The literature review revealed the possible causes of accidents on earthmoving operations, investigated the spatial risk factors of these types of accident, and identified spatial data needs for automated safety assessment based on current safety regulations. Image‐based data collection devices and algorithms for safety assessment were then evaluated. Analysis methods and rules for monitoring safety violations were also discussed. The experimental results showed that the safety assessment method collected spatial data using stereo vision cameras, applied object identification and tracking algorithms, and finally utilized identified and tracked object information for safety decision making.

Design of a preliminary error impact analysis model for spatial safety assessment of earthmoving operations

Regardless of technology benefits, safety planners still face difficulties explaining errors related to the use of different technologies and evaluating how the errors impact the performance of safety decision making. This paper presents a preliminary error impact analysis testbed to model object identification and tracking errors caused by image-based devices and algorithms and to analyze the impact of the errors for spatial safety assessment of earthmoving and surface mining activities. More specifically, this research designed a testbed to model workspaces for earthmoving operations, to simulate safety-related violations, and to apply different object identification and tracking errors on the data collected and processed for spatial safety assessment. Three different cases were analyzed based on actual earthmoving operations conducted at a limestone quarry. Using the testbed, the impacts of the errors were investigated for the safety planning purpose.

Intelligent computing and sensing for active safety on construction sites

On obstacle-cluttered construction sites where heavy equipment is in use, safety issues are of major concern. The main objective of this paper is to develop a framework with algorithms for obstacle avoidance and path planning based on real-time three-dimensional job site models to improve safety during equipment operation. These algorithms have the potential to prevent collisions between heavy equipment vehicles and other on-site objects. In this study, algorithms were developed for image data acquisition, real-time 3D spatial modeling, obstacle avoidance, and shortest path finding and were all integrated to construct a comprehensive collision-free path. Preliminary research results show that the proposed approach is feasible and has the potential to be used as an active safety feature for heavy equipment.

Raman spectroscopy of the borate mineral ameghinite NaB3O3(OH)4

The molecular structure of the sodium borate mineral ameghinite NaB3O3(OH)4 has been determined by the use of vibrational spectroscopy. The crystal structure consists of isolated [B3O3(OH)4]- units formed by one tetrahedron and two triangles. H bonds and Na atoms link these polyanions to form a 3-dimensional framework. The Raman spectrum is dominated by an intense band at 1027 cm-1, attributed to BO stretching vibrations of both the trigonal and tetrahedral boron. A series of Raman bands at 1213, 1245 and 1281cm-1 are ascribed to BOH in-plane bending modes. The infrared spectra are characterized by strong overlap of broad multiple bands. An intense Raman band found at 620 cm-1 is attributed to the bending modes of trigonal and tetrahedral boron. Multiple Raman bands in the OH stretching region are observed at 3206, 3249 and 3385 cm-1. Raman spectroscopy coupled with infrared spectroscopy has enabled aspects about the molecular structure of the borate mineral ameghinite to be assessed.

The borate mineral jeremejevite Al6(BO3)5(F,OH)3 – A vibrational spectroscopic study

Jeremejevite is a borate mineral of aluminium and is of variable colour, making the mineral and important inexpensive jewel. The mineral contains variable amounts of F and OH, depending on origin. A comparison of the vibrational spectroscopic data is made with the published data of borate minerals. Raman spectra were averaged over a range of crystal orientations. Two intense Raman bands observed at 961 and 1067 cm−1 are assigned to the symmetric stretching and antisymmetric stretching modes of trigonal boron. Infrared spectrum, bands observed at 1229, 1304, 1350, 1388 and 1448 cm−1 are attributed to BOH in-plane bending modes. Intense Raman band found at 372 cm−1 with other bands of significant intensity at 327 and 417 cm−1 is assigned to trigonal borate bending modes. A quite intense Raman band is found at 3673 cm−1 with other sharp Raman bands found at 3521, 3625 and 3703 cm−1 are assigned to the stretching modes of OH. Raman and infrared spectroscopy has been used to assess the molecular structure of the mineral jeremejevite. Such research is important in the study of borate based nanomaterials.

Vibrational spectroscopy of the mineral meyerhofferite CaB3O3(OH)5·H2O – An assessment of the molecular structure

Meyerhofferite is a calcium hydrated borate mineral with ideal formula: CaB3O3(OH)5�H2O and occurs as white complex acicular to crude crystals with length up to �4 cm, in fibrous divergent, radiating aggregates or reticulated and is often found in sedimentary or lake-bed borate deposits. The Raman spectrum of meyerhofferite is dominated by intense sharp band at 880 cm�1 assigned to the symmetric stretching mode of trigonal boron. Broad Raman bands at 1046, 1110, 1135 and 1201 cm�1 are attributed to BOH in-plane bending modes. Raman bands in the 900–1000 cm�1 spectral region are assigned to the antisymmetric stretching of tetrahedral boron. Distinct OH stretching Raman bands are observed at 3400, 3483 and 3608 cm�1. The mineral meyerhofferite has a distinct Raman spectrum which is different from the spectrum of other borate minerals, making Raman spectroscopy a very useful tool for the detection of meyerhofferite in sedimentary and lake bed deposits.

Thermal analysis and vibrational spectroscopic characterization of the boro silicate mineral datolite – CaBSiO4(OH)

The objective of this work is to determine the thermal stability and vibrational spectra of datolite CaBSiO4(OH) and relate these properties to the structure of the mineral. The thermal analysis of datolite shows a mass loss of 5.83% over a 700–775 °C temperature range. This mass loss corresponds to 1 water (H2O) molecules pfu. A quantitative chemical analysis using electron probe was undertaken. The Raman spectrum of datolite is characterized by bands at 917 and 1077 cm−1 assigned to the symmetric stretching modes of BO and SiO tetrahedra. A very intense Raman band is observed at 3498 cm−1 assigned to the stretching vibration of the OH units in the structure of datolite. BOH out-of-plane vibrations are characterized by the infrared band at 782 cm−1. The vibrational spectra are based upon the structure of datolite based on sheets of four- and eight-membered rings of alternating SiO4 and BO3(OH) tetrahedra with the sheets bonded together by calcium atoms.