Benny Raphael

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.

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.

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.

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.

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.

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%.

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.

Light shelves are designed to distribute daylight deeper into the room and reduce energy consumption. While many studies have investigated the daylighting performance of light shelves, there are not much work done on the heat reflected by them. This paper aims to experimentally evaluate the radiant heat reflected from light shelves and analyse the trade-off between the transmission of heat and light. The study involves an experimental setup with controlled conditions and a novel methodology to quantify the reflected radiation. About 5 million data points for heat flux data were collected from light shelves made of eight different materials. Results showed that the mirror glass surface with high specular reflectivity, has maximum direct heat transmission of 10.4 W/m2 along with maximum illuminance. However, aluminium with lesser illuminance than mirror glass exhibits a maximum overall heat transmission of 35.8 W/m2. Also, polished black granite slab showed negligible reflected heat and moderate illuminance. Thus, this study helps us select materials for light shelves to reduce heat transmission without significantly affecting reflected light.

A robust monitoring system is essential for ensuring safety and reliability in automated construction. Activity recognition is one of the critical tasks in automated monitoring. Existing studies in this area have not fully exploited the potential for enhancing the performance of machine learning algorithms using domain knowledge, especially in problem formulation. This paper presents a hierarchical machine learning framework for improving the accuracy of identification of Automated Construction System (ACS) operations. The proposed identification framework arranges the operations to be identified in the form of a hierarchy and uses multiple classifiers that are organized hierarchically for separating the operation classes. It is tested on a laboratory prototype of an ACS, which follows a top-down construction method. The ACS consists of a set of lightweight and portable machinery designed to automate the construction of the structural frame of low-rise buildings. Accelerometers were deployed at critical locations on the structure. The acceleration data collected while operating the equipment were used to identify the operations through machine learning techniques. The performance of the proposed framework is compared with that of the conventional approach for equipment operation identification which involves a flat list of classes to be separated. The performance was comparable at the top level. However, the hierarchical framework outperformed the conventional one when fine levels of operations were identified. The versatility and noise tolerance of the hierarchical framework are also reported. Results demonstrate that the framework is robust, and it is feasible to identify the ACS operations precisely. Although the proposed framework is validated on a full-scale prototype of the ACS, the effects of strong ambient disturbances on actual construction sites have not been evaluated. This study will support the development of an automated monitoring system and assist the main operator to ensure safe operations. The high-level operation details collected for this purpose can also be utilised for project performance assessment and progress monitoring. The potential application of the proposed hierarchical framework in the operation recognition of conventional construction equipment is also outlined.

Purpose: The aim of this paper is to synthesize knowledge related to performance evaluation of automated construction processes during the planning and execution phases through a theme-based literature classification. The primary research question that is addressed is “How to quantify the performance improvement in automated construction processes?” Design/methodology/approach: A systematic literature review of papers on automated construction was conducted involving three stages-planning, conducting and reporting. In the planning stage, the purpose of the review is established through key research questions. Then, a four-step process is employed consisting of identification, screening, shortlisting and inclusion of papers. For reporting, observations were critically analysed and categorized according to themes. Findings: The primary conclusion from this study is that the effectiveness of construction processes can only be benchmarked using realistic simulations. Simulations help to pinpoint the root causes of success or failure of projects that are either already completed or under execution. In automated construction, there are many complex interactions between humans and machines; therefore, detailed simulation models are needed for accurate predictions. One key requirement for simulation is the calibration of the models using real data from construction sites. Research limitations/implications: This study is based on a review of 169 papers from a database of peer-reviewed journals, within a time span of 50 years. Originality/value: Gap in research in the area of performance evaluation of automated construction is brought out. The importance of simulation models calibrated with on-site data within a methodology for performance evaluation is highlighted.