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Given the growing interest in modern construction techniques and the emergence of innovative technologies, construction site layout planning research has progressively been investigating approaches to adopt innovative concepts and incorporate renewed approaches to improve widespread efficiency. This research develops a decision-making tool that optimizes construction site layout plans. The developed model targets two main objectives: minimizing material transportation costs and maximizing safety by optimally placing facilities on construction sites.

A novel approach is devised based on the integration of Building Information Modeling and Generative Design (BIM-GD). This engine is used to optimize the multi-objective site layout problems to identify layout alternatives in the early project stages. Parametric modeling uses Dynamo to construct the model and explore constraints initially. Finally, the GD environment is utilized to create different design alternatives, and then the decision-making procedure selects the most appropriate design alternative. Additionally, a case study is applied to validate the effectiveness of the developed model.

The results indicate the effectiveness of the proposed GD tool and its potential for more complex applications. The GD engine examined optimal layout plans, balancing different objectives and adhering to appointed geometric constraints. A case study was conducted to assess the model's effectiveness and showcase its suitability. Construction Site Layout Planning (CSLP) is an essential step in design that can influence considerable aspects, such as material transportation expenses and different safety standards on the site. Employing visual programming for parametric modeling within Dynamo-Revit creates an expedient and user-friendly platform for planning engineers who may require more programming expertise to create and program algorithmic models visually. Utilizing GD in CSLP has proven to be a powerful tool with consequential prospects for improving applications and executing more models.

The findings from this framework are intended to help construction practitioners select the most appropriate site layout during early project stages while incorporating different safety criteria inside construction sites to alleviate actual safety risks.

A new approach is proposed that utilizes an integrated BIM-GD engine to optimize multi-objective site layout problems. This approach targets two main objectives: minimizing material transportation costs and maximizing safety by optimally placing facilities in construction sites.

This research aims to create a methodology that integrates optimization techniques into preliminary cost estimates and predicts the impacts of design alternatives of steel pedestrian bridges (SPBs). The cost estimation process uses two main parameters, but the main goal is to create a cost estimation model.

This study explores a flexible model design that uses computing capabilities for decision-making. Using cost optimization techniques, the model can select an optimal pedestrian bridge system based on multiple criteria that may change independently. This research focuses on four types of SPB systems prevalent in Egypt and worldwide. The study also suggests developing a computerized cost and weight optimization model that enables decision-makers to select the optimal system for SPBs in keeping up with the criteria established for that system.

In this paper, the authors developed an optimization model for cost estimates of SPBs. The model considers two main parameters: weight and cost. The main contribution of this study based on a parametric study is to propose an approach that enables structural engineers and designers to select the optimum system for SPBs.

The implications of this research from a practical perspective are that the study outlines a feasible approach to develop a computerized model that utilizes the capabilities of computing for quick cost optimization that enables decision-makers to select the optimal system for four common SPBs based on multiple criteria that may change independently and in concert with cost optimization during the preliminary design stage.

The model can choose an optimal system for SPBs based on multiple criteria that may change independently and in concert with cost optimization. The resulting optimization model can forecast the optimum cost of the SPBs for different structural spans and road spans based on local unit costs of materials cost of steel structures, fabrication, erection and painting works.

The authors developed a computerized model that uses spreadsheet software's capabilities for cost optimization, enabling decision-makers to select the optimal system for SPBs meeting the criteria established for such a system. Based on structural characteristics and material unit costs, this study shows that using the optimization model for estimating the total direct cost of SPB systems, the project cost can be accurately predicted based on the conceptual design status, and positive prediction outcomes are achieved.

The paper aims to identify a suitable generic brake force distribution ratio (β) corresponding to optimal brake design attributes in a diminutive driving range, where road conditions do not exhibit excessive variations. This will intend for an appropriate allocation of brake force distribution (BFD) to provide dynamic stability to the vehicle during braking.

Two techniques are presented (with and without wheel slip) to satisfy both brake stability and performance while accommodating variations in load sharing and road friction coefficient. Based on parametric optimization of the design variables of hydraulic brake using evolutionary algorithm, taking into account both the laden and unladen circumstances simultaneously, this research develops an improved model for computing and simulating the BFD applied to commercial and passenger vehicles.

The optimal parameter values defining the braking system have been identified, resulting in effective β = 0.695 which enhances the brake forces at respective axles. Nominal slip of 3.42% is achieved with maximum deceleration of 5.72 m/s² maintaining directional stability during braking. The results obtained from both the methodologies are juxtaposed and assessed governing the vehicle stability in straight line motion to prevent wheel lock.

Optimization results establish the practicality, efficacy and applicability of the proposed approaches. The findings provide valuable insights for the design and optimization of hydraulic drum brake systems in modern automobiles, which can lead to safer and more efficient braking systems.

Grey relational analysis (GRA) has already proved itself as an efficient tool for multi-objective optimization of many of the machining processes. In GRA, the distinguishing coefficient (ξ) plays an important role in identifying the optimal parametric combinations of the machining processes and almost all the past researchers have considered its value as 0.5. In this paper, based on past experimental data, the application of GRA is extended to dynamic GRA (DGRA) to optimize two electrochemical machining (ECM) processes.

Instead of a static distinguishing coefficient, this paper considers dynamic distinguishing coefficient for each of the responses for both the ECM processes under consideration. Based on these coefficients, the application of DGRA leads to determination of the dynamic grey relational grade (DGRG) and grey relational standard deviation (GRSD), helping in initial ranking of the alternative experimental trials. Considering the ranks obtained by DGRG and GRSD, a composite rank in terms of rank product score is obtained, aiding in final rankings of the experimental trials for both the ECM processes.

In the first example, the maximum material removal rate (MRR) would be obtained at an optimal combination of ECM parameters as electrolyte concentration = 2 mol/l, voltage = 16V and current = 4A, while another parametric intermix as electrolyte concentration = 2 mol/l, voltage = 14V and current = 2A would result in minimum radial overcut and delamination. For the second example, an optimal combination of ECM parameters as electrode temperature = 30°C, voltage = 12V, duty cycle = 90% and electrolyte concentration = 15 g/l would simultaneously maximize MRR and minimize surface roughness and conicity.

In this paper, two ECM operations are optimized using a newly developed but yet to be popular multi-objective optimization tool in the form of the DGRA technique. For both the examples, the derived rankings of the ECM experiments exactly match with those obtained by the past researchers. Thus, DGRA can be effectively adopted to solve parametric optimization problems in any of the machining processes.

The purpose of this paper is to develop a building information modelling (BIM)-based multi-objective optimization (MOO) framework for volumetric analysis of buildings during early design stages. The objective is to optimize volumetric spaces (3D) instead of 2D spaces to enhance space utilization, thermal comfort, constructability and rental value of buildings

The integration of two fundamental concepts – BIM and MOO, forms the basis of proposed framework. In the early design phases of a project, BIM is used to generate precise building volume data. The non-sorting genetic algorithm-II, a MOO algorithm, is then used to optimize extracted volume data from 3D BIM models, considering four objectives: space utilization, thermal comfort, rental value and construction cost. The framework is implemented in context of a school of architecture building project.

The findings of case study demonstrate significant improvements resulting from MOO of building volumes. Space utilization increased by 30%, while thermal comfort improved by 20%, and construction costs were reduced by 10%. Furthermore, rental value of the case study building increased by 33%.

The proposed framework offers practical implications by enabling project teams to generate optimal building floor layouts during early design stages, thereby avoiding late costly changes during construction phase of project.

The integration of BIM and MOO in this study provides a unique approach to optimize building volumes considering multiple factors during early design stages of a project

This paper investigates the optimization of window design factors (WDFs) in hospital buildings, particularly in government hospitals within the arid climate of the Qassim region, with the aim of achieving a better cooling load reduction. Continuous monitoring of the hospital ward section is crucial due to patients' needs, requiring optimal indoor air quality and cooling load.

The study identifies the optimal conditions for WDF design to mitigate cooling load, including window-to-wall ratio (WWR), window orientation (WO), room size and U-value (thermal properties), effectively reduce energy consumption in terms of sensible cooling load (MWh/m2) and comply with local codes. Data collection involved a smart weather station, while the Integrated Environmental Solution Virtual Environment (IESVE) software facilitated the simulation process.

Key findings reveal that larger patient rooms were about 40% more energy-efficient than smaller rooms. The northern orientation showed lower energy consumption, and specific WWRs and glazing U-values significantly affected energy loads. In an analysis of U-value changes in energy performance based on the Saudi Building Code (SBC), the presented values did not meet the minimum energy consumption standards. For a valid 40% WWR with a thermal permeability of 2.89, 0.181 MWh/m2 was consumed, while for an invalid 100% WWR with the same permeability but facing the north, 0.156 MWh/m2 was consumed, which is considered an invalid practice. It is vital to follow prescribed standards to ensure energy efficiency and avoid unnecessary costs.

Focus lies in emphasizing the significance of adhering to prescribed standards, such as SBC, to guarantee energy efficiency and prevent unwarranted expenses. Additionally, the authors highlight the crucial role of optimizing glazing properties and allocating the WWR appropriately to achieve energy-efficient building design, accounting for diverse orientations and climatic conditions.

This study uses computer-aided design to improve the ecological and environmental sustainability of early-stage landscape designs. Urban expansion on open land and natural habitats has led to a decline in biodiversity and increased climate change impacts, affecting urban inhabitants' quality of life and well-being. While sustainability indicators have been employed to assess the performance of buildings and neighbourhoods, landscape designs' ecological and environmental sustainability has received comparatively less attention, particularly in early-design stages where applying sustainability approaches is impactful.

The authors propose a computation framework for evaluating key landscape sustainability indicators and providing real-time feedback to designers. The method integrates spatial indicators with widely recognized sustainability rating system credits. A specialized tool was developed for measuring biomass optimization, precipitation management and urban heat mitigation, and a proof-of-concept experiment tested the tool's effectiveness on three Mediterranean neighbourhood-level designs.

The results show a clear connection between the applied design strategy to the indicator behaviour. This connection enhances the ability to establish sustainability benchmarks for different types of landscape developments using parametric design.

The study allows non-expert designers to measure and embed landscape sustainability early in the design stages, thus lowering the entry level for incorporating biodiversity enhancement and climate mitigation approaches.

This study expands the parametric vocabulary for measuring landscape sustainability by introducing spatial ecosystem services and architectural sustainability indicators on a unified platform, enabling the integration of critical climate and biodiversity-loss solutions earlier in the development process.

Currently, the design, technological features of voices, and their analysis of various applications are being simulated with the requirement to communicate at a greater distance or more discreetly. The purpose of this study is to explore how voices and their analyses are used in modern literature to generate a variety of solutions, of which only a few successful models exist.

The mel-frequency cepstral coefficient (MFCC), average magnitude difference function, cepstrum analysis and other voice characteristics are effectively modeled and implemented using mathematical modeling with variable weights parametric for each algorithm, which can be used with or without noises. Improvising the design characteristics and their weights with different supervised algorithms that regulate the design model simulation.

Different data models have been influenced by the parametric range and solution analysis in different space parameters, such as frequency or time model, with features such as without, with and after noise reduction. The frequency response of the current design can be analyzed through the Windowing techniques.

A new model and its implementation scenario with pervasive computational algorithms’ (PCA) (such as the hybrid PCA with AdaBoost (HPCA), PCA with bag of features and improved PCA with bag of features) relating the different features such as MFCC, power spectrum, pitch, Window techniques, etc. are calculated using the HPCA. The features are accumulated on the matrix formulations and govern the design feature comparison and its feature classification for improved performance parameters, as mentioned in the results.

This study aims to develop a methodology that extracts an architectural concept from a biological analogy that integrates forms and kinetic behavior to identify whether complex forms work better or simple forms with proper kinetic behavior for improving visual comfort and daylight performance.

The research employs a transdisciplinary approach using several methods consisting of a biomimetic functional-morphological approach, kinetic design strategy, case study comparison using algorithmic workflow and parametric simulation and inverse design, to develop an interactive kinetic façade with optimized daylight performance.

A key development is the introduction of a periodic interactive region (PIR), which draws inspiration from the butterfly wings' nanostructure. These findings challenge conventional perspectives on façade complexity, highlighting the efficacy of simpler shapes paired with appropriate kinetic behavior for improving visual comfort. The results show the façade with a simpler “Bookshelf” shape integrated with a tapered shape of the periodic interactive region, outperforms its more complex counterpart (Hyperbolic Paraboloid component) in terms of daylight performance and glare control, especially in southern orientations, ensuring occupant visual comfort by keeping cases in the imperceptible range while also delivering sufficient average spatial Daylight Autonomy of 89.07%, Useful Daylight Illuminance of 94.53% and Exceeded Useful Daylight Illuminance of 5.11%.

The investigation of kinetic façade studies reveals that precedent literature mostly focused on engineering and building physics aspects, leaving the architectural aspect underutilized during the development phase. Recent studies applied a biomimetic approach for involving the architectural elements besides the other aspects. While the biomimetic method has proven effective in meeting occupants' visual comfort needs, its emphasis has been primarily on the complex form which is difficult to apply within the kinetic façade development. This study can address two gaps: (1) the lack of an architectural aspect in the kinetic façade design specifically in the development of conceptual form and kinetic behavior dimensions and (2) exchanging the superficial biomimetic considerations with an in-depth investigation.

The construction industry is tasked with creating sustainable, efficient and cost-effective buildings. This study aims to develop a building information modeling (BIM)-based multiobjective optimization (MOO) model integrating the nondominated sorting genetic algorithm III (NSGA-III) to enhance sustainability. The goal is to reduce embodied energy and cost in the design process.

Through a case study research method, this study uses BIM, NSGA-III and real-world data in five phases: literature review, identification of factors, BIM model development, MOO model creation and validation in the architecture, engineering and construction sectors.

The innovative BIM-based MOO model optimizes embodied energy and cost to achieve sustainable construction. A commercial building case study validation showed a reduction of 30% in embodied energy and 21% in cost. This study validates the model’s effectiveness in integrating sustainability goals, enhancing decision-making, collaboration, efficiency and providing superior assessment.

This model delivers a unified approach to sustainable design, cutting carbon footprint and strengthening the industry’s ability to attain sustainable solutions. It holds potential for broader application and future integration of social and economic factors.

The research presents a novel BIM-based MOO model, uniquely focusing on sustainable construction with embodied energy and cost considerations. This holistic and innovative framework extends existing methodologies applicable to various buildings and paves the way for additional research in this area.