Algorithms for Automated Monitoring and Control of Fall Hazards

Most lethal accidents in construction are caused by falling from heights. Researchers point out the importance of safety control, carried out systematically and based on real-time data collection, as the most important element of accident prevention. An automated model to monitor and control fall hazard was developed. The model identifies the activities associated with risk of fall from heights and the areas where these activities are scheduled to be performed and plans the protective measures—guardrails in the present case. The model is designed to follow up the existing guardrails and constantly compare their locations and lengths to the planned ones. Based on this comparison, the model issues warnings whenever guardrails are missing, or temporarily removed. The model provides reports and warnings—the reports are used for planning the materials, or workers, needed to erect the protective measures. Warnings are given when a dangerous activity is performed without appropriate protective measures, or when the latter were removed before the dangerous activity was completed. The model’s main algorithms—dangerous activities and areas identification—were implemented and evaluated on site. Whereas the proposed model was developed to improve safety during the construction stage, it can be used as a useful tool during the design stage too. Including safety in the design stage, typically absent, can meaningfully improve safety during the actual construction.     

Field Experiments in Automated Monitoring of Road Construction

The growing need for better monitoring and control of road construction projects, together with rapid technological progress, have led to a number of interesting developments, which are reviewed below. The present paper describes the development of a real-time monitoring model capable of measuring productivity and progress automatically. The model, which uses global positioning system for on-site automated data collection, was tested and validated on site. The results of the field experiments have indicated that the expected accuracy level of the model can be assessed as ±4–5% for unstructured activities and even higher for more structured ones, such as asphalt spreading. The paper concludes that it is possible to automatically measure the performance of earthmoving operations. Based on the results, it also highlights the needs for further research.     

Performance of Model Predictive Control on ASCE Test Canal 1

Model predictive control (MPC) is a popular control algorithm in the process control industry that is particularly suited to the automatic control of irrigation water delivery systems because it explicitly accounts for the long delay times encountered in open-channel flow. In addition, a feedforward routine is easy to implement in MPC and many of the constraints that canal operators face can be directly incorporated into the MPC scheme. The ASCE Task Committee on Canal Automation Algorithms developed a series of test cases to evaluate the performance of canal control algorithms. In this paper, simulation tests were performed on ASCE test canal 1 using a remote downstream control configuration of MPC. The MPC algorithm effectively controls ASCE test canal 1, and its performance was similar to that of other proposed controllers. When there were no minimum gate movement constraints, MPC was fairly robust because the controller performance did not significantly degrade under untuned conditions. In the presence of minimum gate movement constraints, the water levels continually oscillate around the water level setpoint. Using the configuration presented in this paper, the feedforward portion of MPC does not perform as well as other proposed feedforward routines. This underperformance is related to the simplifications made by the underlying process model and not to MPC itself.     

Feasibility Study of an Automated Tool for Identifying the Implications of Changes in Construction Projects

Because of the fragmented nature of project information, decisions on changes in construction projects are usually based on project design instead of project requirements. This research proposes a new approach for coping with changes in construction projects: A change control tool (CCT) that will identify implications of a change as soon as it is proposed. The tool will ensure that the stakeholders involved in the decision process in which change proposals are evaluated will know in advance if a change could cause the project to stray from its original goals, as expressed in the requirements. The proposed CCT uses the building program as a link between client requirements and the building design and traces the different relationships that exist between the requirements in the project. The relationships are traced using requirement traceability capabilities on the level of a specific space in the project and on the level of the entire project. A preliminary CCT model was developed and pilot studies implementing the model have been conducted. The pilot studies have given positive results, indicating that the CCT could identify the scope of the proposed changes’ implications.     

Interpretation of Automatically Monitored Lifting Equipment Data for Project Control

Interpretation of the data that can be collected by automated monitoring systems on construction sites is the most significant challenge to providing useful management information. Distinct construction operations must be identified and associated with construction activities, so that they can be related to construction plans. Earlier research has indicated that construction equipment can be monitored conveniently and that individual equipment operations can be isolated and characterized. In this work, an approach has been developed for unique association of isolated equipment operations with planned construction activities. The approach is based on comparison of the values of various characteristics, calculated for each equipment operation, against preset filters of characteristic values for all expected basic construction activities. The composition of the set of characteristics is different for each data stream monitored and is dependent on the nature of the construction activities. The method has the distinct advantage of ensuring the uniqueness of each filter within the collection of filters when the system is calibrated at the start of any project, rather than during online data processing. In this way, rapid and accurate interpretation of monitored data can be guaranteed. The method was tested using data collected during construction of a high-rise office tower.     

Feasibility of Automated Monitoring of Lifting Equipment in Support of Project Control

One of the differences between industrial manufacturing or processing plants and construction sites is the temporary nature of the construction site, which has traditionally precluded installation of sophisticated production monitoring systems. Monitoring of production progress, cost, and quality is performed almost exclusively manually, with the result that it is expensive and approximate, and is commonly delivered with a time lag that does not allow for an effectively closed control loop. Automated monitoring of construction lifting equipment to provide useful feedback information for project management is a strong potential candidate; almost all components and materials must be transported by machines, and monitoring of machines is relatively straightforward. A system concept, employing a “black box” monitor and an electronic building information model, was developed. A field study was conducted to test the feasibility of the concept. The results indicate that the system is technically feasible, and offers the potential to deliver real-time, accurate project control information at very low cost.     

Computer Vision-Based Video Interpretation Model for Automated Productivity Analysis of Construction Operations

Videotaping is an effective and inexpensive technique that has long been used in construction to conduct productivity analyzes. However, as schedules of modern construction projects become more and more compressed, the limitation of video-based analysis—intensive manual reviewing process—contrasts sharply with the need for effortless data analysis methods. This paper presents a study on developing a video interpretation model to interpret videos of construction operations automatically into productivity information. More specifically, this research formalizes key concepts and procedures of video interpretation within the construction domain. It focuses on designing a mechanism for furthering the crosstalk between the prior knowledge of construction operations and computer vision techniques. It uses this mechanism to guide the detection and tracking of project resources as well as work state classifications and abnormal production scenario identifications. The resulting approach has the potential to provide a common base for developing automated video interpretation procedures that can greatly improve current data collection and analyzes practices in construction. Experimental results from preliminary studies have shown the potential of the proposed video interpretation method as an improved productivity data analysis method.     

Performance of Historic Downstream Canal Control Algorithms on ASCE Test Canal 1

Researchers have developed several algorithms to automatically control water levels in irrigation canals. Proportional-integral (PI) control logic has been used for downstream water-level control, but its performance has not always been satisfactory. Heuristic downstream water-level controllers (e.g., canal automation for rapid demand deliveries, or CARDD) have also been proposed but not rigorously tested. The ASCE Task Committee on Canal Automation Algorithms developed a series of test cases to evaluate the performance of canal control algorithms. In this paper, simulation tests were performed on the ASCE Test Canal 1 using three downstream control algorithms: (1) The standard PI control logic; (2) The PI control logic with hydraulic decouplers; and (3) The heuristic CARDD control logic. These controllers were tuned manually using trial-and-error techniques. Performance of the PI control logic improved with the addition of hydraulic decouplers. CARDD did not perform as well as the PI controllers under the conditions imposed on ASCE Test Canal 1. Robustness of these controllers depends on the aggressiveness of the controller as well as the initial flow rate.     

Automation and Robotics for Construction

The potential for automated real‐time data acquisition, process control and robotics for remote, large‐scale field operations, such as those on construction engineering projects, is addressed. Classifications of technologies for automation and robotics in such operations include hard‐wired instrumentation, remote sensing, analog and digital telecommunications, optical (laser, infrared and fiber‐optic) data transmission, monitoring via microcomputer‐based instrument control and data recording, on‐site process control for fixed plants, partial or fully automatic control of mobile equipment, fixed‐based manipulators, mobile robots, communications between on‐site computers and automated machinery, electronic ranging and detection, and video‐image pattern recognition. Combining selected technologies with microcomputer‐based software could facilitate analysis, design and control decision‐making, and could provide a means of coordinating various discrete automated components or machines that must work together to perform field tasks. This paper also mentions categories of needs for such technologies on field operations, and potential barriers to implementation. Progress will depend on the interest and support of researchers qualified to advance this field.     

Automated Generation of Customized Field Data Collection Templates to Support Information Needs of Cost Estimators

While estimating activity production rates, cost estimators rely on historical production rates. To have realistic and useful cost estimates based on historical production rates, such production rate data should be augmented with historical contextual information that depict conditions under which activity production rates were achieved in past projects. This information is needed in determining which production rate to use among alternates for a similar activity existing in a new bid. Estimators need contextual information especially when they are unfamiliar with the work being estimated. Hence, such information items need to be identified, collected, and stored for estimators’ use in new projects. This paper details a construction-method specific and an extensible approach that is developed for enabling cost estimators to define contextual information items that need to be collected on job sites and stored as part of project histories. Based on this approach, the writers implemented a prototype system, called as ContextGen, and performed user-tests with estimators with different experience levels. Results showed that the developed approach captures method-specific information needs of estimators and is extensible to incorporate new contextual information items that can have different data representations. The developed approach is also precise in retrieving contextual information items specific to a construction method from a set of predefined contextual information items available in a library.     

Design of a Single-Pool Downstream Controller Using Quantitative Feedback Control Theory

A downstream controller is designed for an irrigation canal reach using a design technique called quantitative feedback control theory (QFT). The performance of this controller is compared to a proportional, integral, derivative (PID) controller and a linear quadratic regulator (LQR) controller. In this study, the QFT controller is designed for a single canal reach because it best demonstrates how a controller is designed. Previous research for this canal model provided data for comparison. For the operating conditions that are defined in this paper, the QFT controller is shown to have slightly better performance than the PID controller and better performance than the LQR controller. When the canal hydraulic roughness is increased, the QFT controller still performed better than the PID controller.     

浅谈如何建立和完善国有企业内部控制系统

监控管理和内部控制管理是对企业(适用于各类公司,下同)生产经营过程中实施监督的不可或缺的2种手段;同时也是企业规范运作,防止差错的有效管理方式。监控管理是外部对企业实施的监督,内控管理是企业内部的自我控制,两者均可对企业运营过程中出现的偏差起着警示和纠错作用。建立良好的监控管理制度和内部控制体系,是确保企业生产经营规范、合法;提供数据真实、可信的基础。     

Automatic Downstream Water-Level Feedback Control of Branching Canal Networks: Theory

Most of the research on the design of feedback controllers for irrigation canals has been concentrated on single, in-line canals with no branches. Because the branches in a network are hydraulically coupled with each other, it may be difficult to automatically control a branching canal network by designing separate feedback controllers for each branch and then letting them run simultaneously. Thus feedback control of an entire branching canal system may be more efficient if the branching flow dynamics are explicitly taken into account during the feedback controller design process. This paper develops two different feedback controllers for branching canal networks. The first feedback controller was developed using linear quadratic regulator theory and the second using model predictive control. Both algorithms were able to effectively control a simple branching canal network example with relatively small flow changes.     

Can Labor Inputs be Measured and Controlled Automatically?

Real-time control of on-site construction is a growing field still in its infancy. Labor inputs is an important project performance indicator to be controlled because it is a leading input in construction projects. A model for automated control was developed and verified in the field, to demonstrate that labor inputs can be automatically measured and controlled. The concept behind this development is that indirect data—workers’ locations measured automatically at regular time intervals—can be collected automatically and converted into labor inputs using computerized algorithms. The model was implemented in a concept-proving prototype, which was applied in three construction projects to verify the concept and test the feasibility of developing a full-scale prototype. The encouraging results of the field experiments showed that it is possible to convert worker’s locations automatically measured at regular time intervals into labor inputs, and thus automatically control them. The expected accuracy of such a system is ±10–20%. Additional work is currently underway to develop a location measurement system and other components of the full-scale prototype.     

Project Performance Evaluation Based on Statistical Process Control Techniques

The earned value management (EVM) method is considered as a powerful tool that supports the management of project scope, time, and cost. However, conventional EVM may cause some project managers to ignore the information that it can provide about the performance variations of their projects as well as their causes and effects. This paper attempts to refine and improve the performance of traditional EVM by the introduction of statistical control chart techniques. Individual control charts are used as tools to monitor project performance data so that adverse changes can be detected in a timely manner. This allows analysis of in-progress project cost and schedule performance variations and highlights possible needs for corrective action. A comparative analysis between traditional EVM and the new approach is undertaken to identify the effectiveness of the new approach.     

Next Generation Space Telescope. II: Proposed Pointing Control System

The pointing control system of the Hubble Space Telescope (HST) represents the current state of the art for the precision control of a large spacecraft. The proposed Next Generation Space Telescope (NGST) will require an order‐of‐magnitude increase in pointing resolution over that of HST. The use of active optics in the form of a steerable secondary mirror has been proposed for NGST to satisfy these requirements. The primary motivation for this study was to demonstrate the feasibility of satisfying the pointing‐stability requirements by sensing the guide‐star position and steering the optical path of the telescope with the active secondary mirror. To study the requirements of the control system, a two‐degree‐of‐freedom model that retains the rigid‐body mode of the telescope as well as its first oscillatory mode was constructed. The corresponding optimal control law was developed and implemented in a discrete manner to examine the behavior of the system subject to typical spacecraft excitations.     

Automated Collection of Mixer Truck Operations Data in Highly Dense Urban Areas

Our research has investigated the feasibility of directly sourcing autonomous operations data from a construction-vehicle positioning system, so as to enable productivity analysis and simulation modeling in the practical context of ready mixed concrete production and delivery. In this paper, we first review research efforts related to applying radio frequency identification tags and global positioning system for tracking construction resources and acquiring operations data in the field. We then describe the technical design and system components of an automated data collection (ADC) solution to accumulating concrete delivery operations data, which is extended from a construction-vehicle positioning system tailored for highly dense urban areas. We further elaborate on how our ADC system captures, transforms, and analyzes data of mixer truck operations. Truck-tracking experiment results based on field trials are presented to demonstrate the usefulness of data sourced from our ADC system with respect to: (1) analyzing truck-waiting time versus truck-unloading time on site; and (2) predicting truck’s plant-to-site travel time. In conclusion, the ADC solution resulting from this research not only allows sophisticated analysis of mixer truck resource utilization at concreting sites situated in highly dense urban areas, but also provides an accumulation of input data that will enable concrete plant operations simulation modeling.     

Automatic Downstream Water-Level Feedback Control of Branching Canal Networks: Simulation Results

Previous research on canal automation has dealt with the control of single, in-line canals, while canal operators typically have to control an entire network of canals. Because the branches in a network are hydraulically coupled with each other, control of a branching canal network based on separate controllers for each branch may not be the most effective control strategy. A methodology by which existing automatic control systems could be modified to control branching canal networks is provided in a companion paper. This paper presents results of hydraulic simulations of the new methodology to estimate the controllability of a large portion of the branching canal network operated by the Salt River Project (SRP). Two types of controllers were used for this study: (1) linear quadratic regulator (LQR) and (2) model predictive control (MPC). Both controllers used the same underlying process model [integrator-delay (ID) model], and both controllers were capable of feedback and feedforward control. Under feedback control alone, both controllers gave similar performance, but were unable to effectively control the overall system because of the long delay times. When feedforward control was added to the feedback controller, both of these control systems were able to effectively control the branching canal network operated by SRP. For the LQR controller, the volume compensation method for routing known demand change was used as the feedforward controller. For the MPC controller, the ID model was used as the feedforward controller. Slight differences were noted between the performance of the two feedforward controllers.     

航天飞行器用新材料的现状与发展趋势

本文介绍了国内外先进航天飞行器用各类新材料的最新研究与发展趋势。了新型复合材料、高性能金属结构材料、及特殊功能材料的基本特性、制造工艺及应用开发情况。     

Model Development and Validation for Intelligent Data Collection for Lateral Spread Displacements

The geotechnical earthquake engineering community often adopts empirically derived models. Unfortunately, the community has not embraced the value of model validation, leaving practitioners with little information on the uncertainties present in a given model and the model’s predictive capability. In this study, we present a machine learning technique known as support vector regression (SVR) together with rigorous validation for modeling lateral spread displacements and outline how this information can be used for identifying gaps in the data set. We demonstrate the approach using the free face lateral displacement data. The results illustrate that the SVR has relatively better predictive capability than the commonly used empirical relationship derived using multilinear regression. Moreover, the analysis of the SVR model and its support vectors helps in identifying gaps in the data and defining the scope for future data collection.