Now showing 1 - 10 of 43
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    A case-based reasoning technique for evaluating performance improvement in automated construction projects
    (01-01-2022)
    Krishnamoorthi, S.
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    Automation of construction processes facilitates increased productivity and overall higher project performance. This paper presents a methodology for comparative assessment of different construction processes and selection of an optimal solution based on appropriate automation implementation. Construction processes are quantitatively evaluated using a methodology combining case-based reasoning and compositional modeling. Through the generation of many combinations of process fragments that are compiled from case libraries, potential solutions are explored and evaluated. An example involving solutions such as RCC frame construction, precast construction, and modular steel frame construction is described in this paper. The study demonstrates the possibility of selection of suitable construction processes based on the quantitative assessment of a large number of potential solutions. Processes are modelled by decomposing them up to the elementary tasks and appropriate level of automation is identified in all the tasks.
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    Automating finite element development using object oriented techniques
    (01-03-1993) ;
    Krishnamoorthy, C. S.
    An object oriented finite element model is presented. The main advantage of this model over conventional systems is that, the additional code required for adding elements to the finite element library is minimal. The powerful mechanisms provided by object oriented systems facilitate this. These mechanisms enable re-use of existing code, and allow the programmer to leave certain operations to the computer, which, without object oriented techniques, would not have been possible. In the above model, the finite elements are represented in the form of a hierarchical tree by which it is possible to develop elements by programming only the differences from existing elements. Suitable object oriented designs have been developed for representing mathematical entities like differential operators and shape functions, with a view to automating the process of development of element properties, so that, the element developer needs to specify just the minimum details, leaving most of the operations to the computer. Some of the concepts in object oriented programming are explained in detail, with the examples used in the above model. © 1993, MCB UP Limited. All rights reserved.
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    Resource Unconstrained and Constrained Project Scheduling Problems and Practices in a Multiproject Environment
    Construction companies execute many projects simultaneously. In such situations, the performance of one project may influence the others positively or negatively. Construction professionals face difficulties in managing multiple projects in limited resource situations. The purpose of this study is to identify the problems in multiproject scheduling from the practitioner's perspective and to discover current practices under resource unconstrained and constrained settings. The specific objectives are (1) determining the most challenging issues being faced in handling multiproject environment, (2) enumerating the practices adopted in the industry, and finally (3) identifying the practitioners' perceptions on the multiproject scheduling aspects such as network modeling approaches; activity execution modes; concept of sharing, dedicating, and substituting resources; centralized and decentralized decision-making models; solution approaches; and tools and techniques. An online questionnaire survey was conducted to address the objectives above. The top challenging issues in managing multiproject environment are identified. Factor analysis identified the factors by grouping the variables (a) decision-related, (b) project environment-related, (c) project management-related, and (d) organization-related factors. Resource-unconstrained situation mainly faces the issue of underutilization and wastage of resources leading to lower profit realization. The following findings were identified to overcome the unconstrained resource situation such as identifying the work front, adopting pull planning approach, creating a common resource pool, and allotting it on a rental basis. On the contrary, resource-constrained situation faces the issues of prioritization of resources, coordination, communication, collaboration, quality issues, and rework. The findings suggest the strategies such as top-up via subcontracting, proactive pull planning, introducing buffers, training the culture of the organization towards better communication, coordination, and collaboration, to improve the reliability of achieving baseline project performances. Various multiproject aspects suggested for effective management. The identified problems, practices, and various multiproject aspects are expected to contribute better management of multiproject resource unconstrained and constrained project scheduling.
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    Equipment activity recognition and early fault detection in automated construction through a hybrid machine learning framework
    (15-01-2023)
    Harichandran, Aparna
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    Mukherjee, Abhijit
    Existing studies on automated construction equipment monitoring have focused mainly on activity recognition rather than fault detection. This paper proposes a novel equipment activity recognition and fault detection framework called hybrid unsupervised and supervised machine learning (HUS-ML). HUS-ML first identifies normal operations and known faulty conditions through supervised learning. Then, an anomaly detection algorithm is applied to spot any unseen faulty conditions. The framework is tested using acceleration measurements from a low-rise automated construction system prototype. HUS-ML outperformed the conventional machine learning approach in activity recognition and fault detection with an average F1 score of 86.6%. The conventional approach failed to detect unseen faulty operations. HUS-ML identified known faulty operations and unseen faulty operations with F1 scores of 98.11% and 76.19%, respectively. The generalizability of the framework is demonstrated by validating it on an independent benchmark dataset with good results.
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    A review of concrete 3D printed structural members
    (04-01-2023) ;
    Senthilnathan, Shanmugaraj
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    Patel, Abhishek
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    Bhat, Saqib
    Concrete 3D Printing (3DP) is a potential technology for increasing automation and introducing digital fabrication in the construction industry. Concrete 3D Printing provides a significant advantage over conventional or precast methods, such as the prospects of topologically optimized designs and integrating functional components within the structural volume of the building components. Many previous studies have compiled state-of-art studies in design parameters, mix properties, robotic technologies, and reinforcement strategies in 3D printed elements. However, there is no literature review on using concrete 3D Printing technology to fabricate structural load-carrying elements and systems. As concrete 3DP is shifting towards a large-scale construction technology paradigm, it is essential to understand the current studies on structural members and focus on future studies to improve further. A systematic literature review process is adopted in this study, where relevant publications are searched and analyzed to answer a set of well-defined research questions. The review is structured by categorizing the publications based on issues/problems associated with structural members and the recent technology solutions developed. It gives an overall view of the studies, which is still in its nascent stage, and the areas which require future focus on 3D printing technology in large-scale construction projects.
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    Improving simulation predictions of wind around buildings using measurements through system identification techniques
    (01-12-2015)
    Vernay, Didier G.
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    Smith, Ian F.C.
    Wind behavior in urban areas is receiving increasing interest from city planners and architects. Computational fluid dynamics (CFD) simulations are often employed to assess wind behavior around buildings. However, the accuracy of CFD simulations is often unknown. Measurements can be used to help understand wind behavior around buildings more accurately. In this paper, a model-based data interpretation framework is presented to integrate information obtained from measurements with simulation results. Multiple model instances are generated from a model class through assigning values to parameters that are not known precisely, including those for inlet wind conditions. The information provided by measurements is used to falsify model instances whose predictions do not match measurements and to estimate the parameter values of the simulation. The information content of measurement data depends on levels of measurement and modeling uncertainties at sensor locations. Modeling uncertainties are those associated with the model class such as effects associated with turbulent fluctuations or thermal processes. The model-based data interpretation framework is applied to the study of the wind behavior around the buildings of the Treelodge@Punggol estate, located in Singapore. The framework incorporates modeling and measurement uncertainties and provides probability-based predictions at unmeasured locations. This paper illustrates the possibility to improve approximations of modeling uncertainties through avoiding falsification of the entire set of model instances. It is concluded that the framework has the potential to infer time-dependent sets of parameter values and to predict time-dependent responses at unmeasured locations.
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    Performance evaluation of a high-influx, bubble dehumidifier
    (15-08-2018)
    Kasamsetty, K. Sai
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    In hot humid climates, a major portion of air conditioning energy is used for dehumidification. In traditional centralized air conditioning systems, air is cooled below the dew point to remove the moisture. This process is energy intensive. Liquid desiccant dehumidification systems (LDDS) provide a promising alternative as they use low grade or free energy like solar energy. Majority of the existing direct-contact liquid desiccant dehumidification modules adopt the concept of low influx rates which ensures high residence time resulting in improved dehumidifier performance. There is limited knowledge with respect to high influx rates which is the topic of this paper. The overall objective of this study is to evaluate the performance of a high-influx, bubble dehumidifier. A prototype dehumidifier employing aqueous Lithium Chloride (LiCl) as desiccant that could accommodate high-influx rates was developed and experiments were conducted to evaluate its performance. Results of the experiments are analyzed in terms of performance parameters namely moisture removal rate (MRR) and dehumidifier effectiveness. The apparatus could achieve a maximum MRR of 0.28 g/s resulting in 66% dehumidifier effectiveness. Empirical equations to predict the MRR were developed through linear regression using experimental data. The input variables used in regression are the inlet depth, the inlet air vapor pressure, the desiccant solution vapor pressure and the diffusion coefficient of water in LiCl solution, along with the design parameters. The diffusion coefficient parameter which was ignored in other dehumidifier models was found to be imperative in this research. The developed empirical equations were validated by comparing the predicted and measured values on a new set of experiments, the data for which were not used for regression. It is found that the time-dependent nonlinear response of the system is predicted accurately by the empirical model. The root mean square errors of the observed and predicted values are less than 8%.
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    Automation of modular assembly of structural frames for buildings
    (01-01-2016) ;
    Rao, K. Sai Chowdeswara
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    On congested sites in many cities around the world, there is no space available for installing heavy tower cranes and the approach roads are narrow. On such sites, it is desirable to have an automated construction system that can assemble the required components without the use of heavy lifting equipments. This paper introduces a new automation technique which aims to reduce the usage of heavy equipment's in construction projects. The construction is done using automated lifting and positioning systems at the ground level which help in completing the project cheaper and faster. The concept proposed here is a top to bottom construction method using modules of standard sizes. A semi-automatic demonstration prototype of the concept has been implemented in a laboratory. In order to evaluate the potential for saving construction time, experiments were conducted to estimate the time for various operations and these were projected to estimate the time for a typical residential building. It is shown that the structural frame of small buildings can be constructed in a short time, with significantly reduced labor and at low cost. The experimental results show that the total time taken by different people to complete the activities are very consistent and the use of skilled manpower is not essential. With further automation, the time is expected to be reduced considerably.
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    Identification of the structural state in automated modular construction
    (01-01-2019)
    Harichandran, A.
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    Mukherjee, A.
    Automated construction involves complex interactions between machines and humans. Unless all possible scenarios involving construction and equipment are carefully evaluated, it may lead to failure of the structure or may cause severe accidents. Hence monitoring of automated construction is very important and sensors should be deployed for obtaining information about the actual state of the structure and the equipment. However, interpreting data from sensors is a great challenge. In this research, a methodology has been developed for monitoring in automated construction. The overall methodology involves a combination of traditional model-based system identification and machine learning techniques. The scope of this paper is limited to the machine learning module of the methodology. The efficacy of this approach is tested and evaluated using experiments involving the construction of a steel structural frame with one storey and one bay. The construction is carried out by a top-to-bottom method. During the construction of the frame, 99 base cases of normal operations are involved. 158 base cases of possible failures have been enumerated. Failure cases involve, for example, certain lifting platforms moving faster than others, improper connections of joints, etc. Strain gauges and accelerometers are installed on the structure and the data from these sensors are used to determine possible failure scenarios. Preliminary results indicate that machine learning has good potential for identifying activities and states in automated construction.
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    Augmenting simulations of airflow around buildings using field measurements
    (01-10-2014)
    Vernay, Didier G.
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    Smith, Ian F.C.
    Computational fluid-dynamics (CFD) simulations have become an important tool for the assessment of airflow in urban areas. However, large discrepancies may appear when simulated predictions are compared with field measurements because of the complexity of airflow behaviour around buildings and difficulties in defining correct sets of parameter values, including those for inlet conditions. Inlet conditions of the CFD model are difficult to estimate and often the values employed do not represent real conditions. In this paper, a model-based data-interpretation framework is proposed in order to integrate knowledge obtained through CFD simulations with those obtained from field measurements carried out in the urban canopy layer (UCL). In this framework, probability-based inlet conditions of the CFD simulation are identified with measurements taken in the UCL. The framework is built on the error-domain model falsification approach that has been developed for the identification of other complex systems. System identification of physics-based models is a challenging task because of the presence of errors in models as well as measurements. This paper presents a methodology to estimate modelling errors. Furthermore, error-domain model falsification has been adapted for the application of airflow modelling around buildings in order to accommodate the time variability of atmospheric conditions. As a case study, the framework is tested and validated for the predictions of airflow around an experimental facility of the Future Cities Laboratory, called "BubbleZERO". Results show that the framework is capable of narrowing down parameter-value sets from over five hundred to a few having possible inlet conditions for the selected case-study. Thus the case-study illustrates an approach to identifying time-varying inlet conditions and predicting wind characteristics at locations where there are no sensors.