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Efficient layout planning of a construction site is fundamental for successful project undertaking as it enhances both productivity and safety of operations. The site layout planning problem is a complex combinatorial optimization problem involving multiple objectives and it grows significantly in size as the numbers of facilities and constraints increase. In addition, as construction evolves, the site layout may need to be dynamically reorganized at various schedule intervals to accommodate operational needs. As opposed to considering only productivity issues, this paper presents an approach for site layout planning that considers, in addition, safety and environmental issues resulting from the proximity or remoteness of certain facilities to others or to existing eco-sensitive assets (e.g., rivers). The optimization model developed includes the necessary constraints resulting from physical and operational limitations and can additionally model given priorities or preferences set by the project or site manager depending on particularities of the project or the construction site. The model is developed to handle more than one project phases which may optimally require facility relocation (especially in projects where the site layout “is moving” regularly (e.g., highway construction). The inclusion of several project phases and site reorganization requires solving a significantly larger problem than considering a static layout, if full optimization is done. For this reason, the development integrates the required robust search objective with the optimization capabilities of genetic algorithms (GAs). The model has been tested on several case studies considering both static and dynamic site layout planning and results indicate that it provides rational solutions, in response to decision parameters and problem constraints, and that dynamic modeling develops more effective layout planning than a static one.

The 2019 European Conference on Computing in Construction (2019 EC3), Crete, Greece, 10-12 July 2019

As building energy management becomes a complicated process, traditional risk analysis is not adequate to address the management needs and advanced complex modelling techniques are required to handle the multi-dimensional synthesis of risks in the electricity systems. STPA (System-Theoretic Process Analysis) is a new hazard analysis technique using concepts of system and control theory. In this paper, the development of a risk management structure in the field of building energy management is presented. The hazard analysis process based on SafetyHAT information tool is applied for building operation 'accident' prevention, by illustrating the usage of the proposed tool. For this purpose, a case study considering a holistic energy management system in the tertiary building sector is examined.

Global warming and energy shortage has drastically increased the necessity to reduce energy use in buildings. Improving the energy efficiency of buildings is a key step-in achieving the energy and CO2 emission targets globally. In this effort, in the presented paper, advanced modeling methods like BIM and energy simulations are conjoined with lean waste elimination concepts into a building process-centric model. Integrating building systems, processes and energy data supports decision making for retrofitting and process reengineering actions within budget constraints. The proposed approach combines existing BIM-based energy performance tools with the development of a Business Process Reengineering architecture to develop an energy efficiency optimization model. In the first direction, BIM-based energy analysis is performed to automatically assess energy performance under varying building conditions. The Lean Business Process Reengineering (LBPR) architecture describes the fundamental layers needed to achieve more energy efficient organizational environments. These layers refer to “Definition”, “Data Information”, “Analysis” and “Therapy”. As Process Performance Indicators are also augmented to the process model, the impacts of modifications through generating different process views can be compared. An optimization model employing genetic algorithms is developed in which, considering the potential budget shortage for building asset interventions, preselected lean business process scenarios feed the optimization model to investigate the optimum solutions. A pilot case that shows the practicability of the proposed methodology is presented.

The Volterra/Wiener neural network (VWNN) has been shown to be an effective tool for on-line estimation of non-linear restoring forces and responses. However, the power of the VWNN for on-line identification has not been fully harnessed due to the high sensitivity of its parameters. This study adopts a probabilistic approach in examining the effects of the VWNN's parameters on the robustness and stability of its estimation capabilities. Large ensembles of simulations were conducted in which random (earthquake-like) ground motions were used to excite representative non-linear structures, and on-line estimation of their acceleration responses was performed. The nonlinearity in the system was introduced via hysteretic restoring forces, and a variety of cases were tested, including softening and hardening. The results showed that each design parameter within the VWNN was linked to a certain type of performance sensitivity. The adaptive gain that controls the change in the weights of the VWNN was also directly linked to the stability of the estimates, as small increases in the gain led to the estimates diverging. Within the neural network, the weight within the transfer function was found to directly correlate with accuracy. The optimum set of parameters for a given excitation often produced unstable solutions for other excitations, but by understanding the relationships between the parameters and their sensitivities, a set of parameters could be carefully chosen to consistently produce accurate and stable on-line estimates for all simulations. The knowledge gained from the relationships between VWNN parameters also allowed for informed decisions on parameter sets for simulations involving different classes of nonlinearities. Offering users a starting point provides a necessary and helpful feature so often missing from other non-linear identification schemes that deal with non-parametric identification of complex nonlinear systems.

Efficient layout planning of a construction site is fundamental for successful project undertaking as it enhances both productivity and safety in construction sites. This task usually consists of identifying the temporary facilities needed to support construction operations, determining their size and shape, and optimally positioning them in the unoccupied areas within the site boundaries. The site layout planning problem is a complex combinatorial optimization problem involving multiple objectives and it grows significantly in size as the number of facilities and constraints increases. The existing literature includes a variety of analytical, heuristic, and meta-heuristic techniques for solving the problem but existing studies usually examine a small number of facilities and focus on travel distance minimization, ignoring generally cost related or other decision parameters. The objective of this study is to develop feasible and efficient site layout solutions in a realistic representation scheme taking into consideration not only the total distance traveled but also cost and safety parameters as well. A multi-objective optimization model is developed aiming at minimizing a generalized cost function which results from the construction cost of a facility placed at alternative locations, the transportation cost among locations, and any safety concern in the form of preferred proximity or remoteness of particular facilities to other facilities or work areas. The development integrates the required robust search objective with the optimization capabilities of the genetic algorithms (GAs). The model has been tested on several test cases and the results of a comparative study with existing methods from the literature are presented. The evaluation indicates that the proposed model provides effective and rational solutions, in response to decision parameters and problem constraints, and that it results in more robust layout planning than previous methods both qualitatively and quantitatively.

Several disastrous incidents from earthquakes have been recorded in recent and past human life. Despite the improvements in structural stability and vibration resilience, heavy structures still suffer from construction cost problems which usually hinder their potential investment. Vibration active control techniques present a great potential for reducing the anti-seismic protection costs. So far, existing building vibration control strategies are unable to provide a reliable operation able to reject the evolving uncertain non-linear dynamics that grow as the amplitude of the exogenous ground disturbance increases. This paper applies a vibration active control optimization methodology in applications involving large structures. A simulation model of the structure is used to optimize, in an offline manner, the total structure displacement metric. Simulation experiments demonstrate that the adopted approach namely Automated Fine-Tuning Cognitive Adaptive Optimization (AFT-CAO) - can effectively deal with seismic dynamics both in low and large seismic cases. AFT-CAO was proven capable to provide efficient control decisions that well-established LQR cannot outperform. The structure model used for the simulation tests consists by three vertically interconnected masses, each connected to an external lateral spring with an adjustable applied restoring force.

Structural control is a very broad field combining the areas of automatic control and structural engineering, with applications ranging from aerospace and mechanical engineering to building and civil infrastructure systems. In this paper, the focus is placed on civil engineering applications only. The goal is to address the issues concurring to form the scientific paradigm. As a resut, possible future directions of research into this field are identified.

The identification of safety risks in construction sites has been a major concern of project managers who need to develop proactive safety plans to reduce accident and health related risks. This paper proposes a method which combines BIM technology, virtual reality tools, and Monte Carlo simulation to reveal possible accident conditions and to assess the probability of accident occurrence with the aim to promote safety management and accident reduction. Within the context of construction projects, the construction site is dynamically modelled employing BIM software. A programming language (Visual C #) is used to represent the human/machinery movements in the virtual environment. Finally, Monte Carlo simulation is performed to analyse specific work scenarios which may lead to hazardous conditions and assess the safety level. The aim is to link the risk output with the BIM model and to develop a heat map of accident risk throughout the project allowing, thus, prompt and effective implementation of preventive measures for accident reduction or avoidance.

The Critical Path Method (CPM) has been used in the construction industry as a project planning and control tool for a long time, however, project delays and cost overruns still happen. Compared to CPM, Critical Chain Project Management (CCPM) represents an improved management approach that focuses mostly on human behaviour and resource conflicts, addressing concepts like Student Syndrome, ParkinsonOs Law and multitasking resolution. Additionally, CCPM simplifies project control and utilises buffers to reduce the risk of delay, bringing the project ahead of schedule without increasing costs. On the other hand, Building Information Modelling (BIM), being a methodology that enables enhanced and flexible management of construction project data within a virtual environment, could be exploited as a platform for the introduction of innovative project control methodologies into the construction management practices. This research aims to take advantage of BIM to support the implementation of Critical Chain Project Management methodology along the construction phase of a project. A framework for using BIM as a cost control tool to improve project buffer management is developed and analysed to facilitate the timely project execution and profit increase goals of the contractor.

This policy statement focuses on children and adolescents 5 through 18 years of age. Research suggests both benefits and risks of media use for the health of children and teenagers. Benefits include exposure to new ideas and knowledge acquisition, increased opportunities for social contact and support, and new opportunities to access health-promotion messages and information. Risks include negative health effects on weight and sleep; exposure to inaccurate, inappropriate, or unsafe content and contacts; and compromised privacy and confidentiality. Parents face challenges in monitoring their children's and their own media use and in serving as positive role models. In this new era, evidence regarding healthy media use does not support a one-size-fits-all approach. Parents and pediatricians can work together to develop a Family Media Use Plan (www.healthychildren.org/MediaUsePlan) that considers their children's developmental stages to individualize an appropriate balance for media time and consistent rules about media use, to mentor their children, to set boundaries for accessing content and displaying personal information, and to implement open family communication about media.