Search Results (585)

Smart Manufacturing Systems (SMSs) have evolved into intelligent, data-driven ecosystems that integrate cyber–physical systems, digital twins, and artificial intelligence to enhance efficiency, sustainability, and resilience. This review synthesises more than 250 recent studies across four domains: manufacturing technologies, systems management, sustainable production, and human–robot collaboration. In process optimisation, hybrid machine learning and genetic algorithms reduce surface roughness in machining by up to 35% and decrease energy use in additive manufacturing by 20–30%. In systems management, digital twins and reinforcement learning enable adaptive scheduling and predictive maintenance, increasing operational flexibility and reducing industrial downtime. Sustainability-oriented research shows that additive manufacturing can cut energy consumption by up to threefold compared with subtractive routes, while aluminium recycling and hot-forming processes lower life-cycle impacts. Furthermore, the integration of ISO 14001, ISO 50001, and ISO 14040 supports consistent environmental and energy performance assessment across sectors. Building on this evidence, the review critically examines recent developments in manufacturing technologies, systems management, sustainable practices, and human–robot collaboration, highlighting emerging paradigms such as explainable AI and human-centric design that strengthen safety, transparency, and resilience. Open challenges and research opportunities are outlined to guide future innovation toward intelligent, adaptive, and sustainable manufacturing systems. Full article

This paper introduces a digital framework that integrates Building Information Modeling (BIM) and Facility Management (FM) to enhance the lifecycle performance of Sustainable Urban Drainage Systems (SuDS). Addressing the limitations of traditional drainage such as poor resilience and fragmented maintenance, the framework consists of the following four layers: BIM-based 3D asset modeling, sensor-driven monitoring, FM-integrated operations, and climate-informed adaptive planning. Grounded in systems engineering and aligned with International Standard ISO 19650 standards, it enables a dynamic digital twin to support continuous feedback and predictive maintenance. Illustrated through diagrams and comparison, the framework promotes adaptability and long-term sustainability in urban water infrastructure. Full article

Wind power is a major source of renewable energy, yet turbine performance is strongly influenced by atmospheric conditions and surrounding terrain. Several meteorological phenomena can hinder energy production, disrupt operations, and accelerate structural deterioration. This paper reviews three key atmospheric hazards affecting wind turbine systems: lightning, icing, and rain. For each phenomenon, the formation mechanisms, operational effects, and mitigation approaches are examined, with offshore-specific processes and conditions integrated directly into each hazard discussion. Building on this foundation, the review then analyses interactions between the hazards, their combined implications for turbine performance and maintenance, and the associated economic impacts. Comparisons of material behaviour across lightning, icing, and rain-erosion conditions are also incorporated. Finally, future research directions are proposed. Full article

The huge impact of construction on the environment is becoming increasingly apparent, and it is unacceptable to many engineers and designers. A growing interest in sustainable construction has been observed for several years. This is especially true for commercial buildings, where achieving an appropriate standard is often the main criterion for investment. Many current publications deal with the topic of energy related to building use. In contrast, knowledge of the so-called embodied carbon footprint is not yet widespread but increasingly important in the context of low-carbon construction. The study created six different building types by juxtaposing different construction variants with different facade variants. The analysis was given to the “cradle to grave” phases, i.e., A1–A4, B4–B5 and C1–C4. Module D (material recycling) is omitted, as well as phases B1–B3 and B6–B7 related to use, maintenance, repair and energy and water consumption. Phases B1–B3 refer to maintenance repair and use activities that are the responsibility of the building manager, so they are taken as estimates at the concept stage. Phase B6 and B7 were excluded from the study, due to the fact that they are not responsible for the embodied carbon footprint, but the operational one. It was assumed that the values for B6 would be shown independently in the building’s energy performance and the final values would be comparable. The purpose of the study was to verify the factors that have the greatest impact on the results of the embodied environmental footprint. The study showed that changes in the building’s design and facade have the greatest impact on the embodied carbon footprint. Furthermore, not only the quantity of materials used but also their durability is crucial, so using durable finishes to minimize the need for repair and replacement can play a key role in reducing the building’s embodied carbon footprint. Differences between the variants reached approximately 107 kg CO₂e/m² (about 15%). The comparison of impact categories further indicates that solutions optimized for global warming potential are not necessarily favorable in other environmental dimensions. Finally, the relatively moderate spread between the most and least favorable variants within the analyzed scope indicates that material substitution alone is insufficient to achieve deep decarbonization of office buildings. Comprehensive strategies addressing material selection, durability, service life and design for disassembly and reuse are therefore required. Full article

Windows represent a critical component of a building’s envelope, influencing not only thermal performance and natural interior lighting but also the overall environmental impact of the structure. This study applies life cycle assessment to evaluate the impacts of operable and fixed wood-based windows covering the system boundaries of the product stage and maintenance. Scenarios are modelled for different frame surface treatments, regarding varnish layers, paint presence, and aluminium cladding. The impact categories assessed include elements, fossils, and ozone layer depletion; potentials of global warming, acidification, eutrophication; photochemical ozone creation; and toxicity to humans, freshwater and marine water, as well as terrestrial ecotoxicity. The results indicate that the embodied environmental impact of the wood material alone remains relatively small while glazing and aluminium cladding dominate. Regarding the surface treatment, the varnish quantity as well as the presence of paint do not significantly influence the environmental impact. Differences between operable and fixed windows also reflect additional materials and hardware requirements, resulting in operable windows exhibiting higher environmental impacts across all assessed categories. The findings of this study highlight the important role of structural elements and additional components on the overall environmental impact regarding the complexity of a window. Full article

This study addresses the joint scheduling problem of flexible job shop scheduling and automated guided vehicles with the objective of minimizing the makespan. We propose an efficient optimization approach based on a critical-path-driven variable neighborhood descent. The core contribution lies in the development of a critical path detection mechanism that incorporates transportation processes, along with the design of tailored neighborhood structures. Building on this foundation, a problem-specific variable neighborhood descent search strategy is implemented. Unlike traditional variable neighborhood descent approaches, the proposed critical path analysis accurately identifies bottleneck operations in both processing and transportation stages. The designed neighborhood structures effectively coordinate machine scheduling and automated guided vehicles transportation, enabling synergistic optimization. To enhance overall performance, auxiliary strategies such as an external memory archive and population diversity maintenance are integrated. Experimental results on multiple benchmark datasets demonstrate that the proposed method achieves significant improvements in solution quality compared to existing algorithms. Ablation experiments further confirm the critical role of the critical-path-driven variable neighborhood descent mechanism in enhancing algorithmic performance. Full article

With the rapid development of metro–commercial integration, ensuring the safety of building operations has become increasingly critical. This study proposes a comprehensive safety evaluation framework tailored to integrated metro–commercial complexes. The framework establishes a hierarchical indicator system encompassing risk management, human safety management, facility and equipment safety, intelligent information management, and integrated crowd and operational risk. By combining historical records, real-time sensor data, and management logs, secondary indicators are quantified and normalized, while a hybrid weighting method integrating expert judgment and statistical analysis ensures both theoretical validity and empirical robustness. A case study demonstrates the framework’s applicability, yielding an overall operational safety score of 0.601, which corresponds to a “Moderate” level. Detailed analysis identifies deficiencies in flood resilience, intelligent monitoring reliability, and crowd-related fire risks, underscoring the complexity of safety challenges in such facilities. Targeted optimization measures—including enhanced drainage redundancy, condition-based equipment maintenance, improved intelligent monitoring, evacuation corridor expansion, and catering fire safety upgrades—are shown to substantially improve the composite safety index and operational resilience. This study contributes a dynamic, data-driven, and interpretable evaluation methodology that not only supports scientific safety management in metro–commercial buildings but also provides a reference for broader applications in multifunctional urban infrastructure. Full article

Digitalization is becoming one of the key trends in contemporary construction, playing a particularly important role in the building operation phase. This phase represents the longest period of a building’s life cycle and is simultaneously associated with high operational costs. The aim of the presented research was to analyze the views of experts and professionals working in the field of building management and operation on the use of digital tools, their perception of the level of digitalization, and the potential for further development. The research was conducted in the form of a questionnaire survey. The results show that in most cases, basic software tools prevail, while the use of advanced platforms such as CMMS (Computerized Maintenance Management System) or CAFM (Computer-Aided Facility Management) systems remains limited. Only one quarter of respondents actively use IoT sensors, which represent an innovative element with high potential for efficient building operation and sustainability. Paradoxically, some respondents perceive even the use of basic software as representing significant digitalization. The most digitalized areas include financial administration, security systems, and energy management, while digital building passports and workspace management remain on the periphery. The findings highlight the uneven application of digital tools and the need for their broader implementation, which can significantly contribute to the efficiency and sustainability of building management. Full article

Three-Dimensional Printing (3DP) is rapidly emerging as a pivotal technology for advancing Facilities Management (FM) toward smart and sustainable buildings. This systematic review, following PRISMA 2020 guidelines, critically evaluates 3DP applications, benefits, and challenges across core FM domains—construction, maintenance and repair, supply chain management, and specialized applications—through analysis of 179 studies. To our knowledge, this represents the first comprehensive, FM-specific systematic review of 3DP implementation frameworks. Evidence synthesis reveals that 3DP enables on-demand, localized manufacturing of bespoke components, with documented inventory cost reductions in maintenance applications, substantial production cost decreases for complex geometries, and significant lead time improvements from traditional procurement cycles to rapid on-demand fulfillment for spare parts applications. However, quantitative evidence remains limited and context-dependent, particularly regarding economic feasibility and scalability. 3DP adoption in FM faces significant barriers: quality assurance protocols, workforce readiness, BIM/IoT integration challenges, and regulatory uncertainty. This review identifies the absence of validated decision-making frameworks to guide FM professionals on 3DP implementation versus traditional alternatives, a fundamental research and practice gap. Through structured quality assessment and stakeholder analysis, we propose strategic recommendations emphasizing cross-sector collaboration, standardization development, and workforce upskilling. A novel conceptual decision framework supports practical implementation decisions. These findings position 3DP as potentially transformative for sustainable building operations while highlighting critical research priorities for systematic FM sector deployment. Full article

This study proposes an unsupervised anomaly detection method to identify the performance degradation in grid-connected photovoltaic (PV) inverters under multitask operation. Principal Component Analysis (PCA) and One-Class Support Vector Machine (OCSVM) were integrated to build a detection model using routine operational data. The key features include DC input, AC output, AC/DC ratio, and AC power variation, which are reduced to two principal components for anomaly boundary construction. The inverters were flagged as degraded if the AC/DC ratio was <0.96, the power fluctuation exceeded 20%, or the data fell outside the OCSVM-defined boundary. Compared with the Isolation Forest, the proposed method showed higher sensitivity. When applied to a 120 MW PV plant in Taiwan with 1292 inverters, including 55 PV-STATCOM units at night, the framework detected degradation in 5.4% of them. These results support their use in intelligent monitoring and predictive maintenance. In addition, through early fault detection and maintenance prioritization, the proposed framework contributes to enhancing reliability, reducing maintenance costs, and promoting the sustainable operation of utility-scale photovoltaic power plants. Full article

Electrical insulation systems (EISs) are the principal reliability bottleneck of modern electrical machines (EMs). Among the many stresses acting on insulation, thermal stress is the most pervasive because it accelerates chemical reactions that progressively erode dielectric and mechanical integrity, ultimately dictating service life. As EMs migrate into compact, high-power-density platforms—automotive, aerospace, and industrial drives—designers need lifetime models that are not merely explanatory but actionable, linking operating temperatures and missions to quantified ageing and risk. This review article traces the evolution of thermal-ageing modelling from fundamental chemistry to a practical design tool. The historical empirical lineage of Arrhenius equation, Arrhenius–Dakin model, and Montsinger model is first revisited, clarifying their assumptions, parameter definitions, and the construction of thermal endurance curves. A discussion then follows on extensions that address deviations from first-order kinetics and demonstrate how variable temperature histories can be incorporated through cumulative damage formulations suitable for duty-cycle analysis. Since models are required to be anchored in data, accelerated thermal ageing (ATA) practices on representative specimens are outlined, alongside a description of the Weibull post-processing for deriving percentile lifetimes aligned with design targets. Building upon these foundations, the Physics-of-Failure (PoF) approach is introduced as a reliability-oriented design (ROD) methodology, in which validated lifetime models guide material selection and geometry optimisation while supporting prognostics and health management during operation. The emerging trend towards a hybrid PoF–AI approach is also discussed, which integrates artificial intelligence to identify nonlinear degradation patterns and drifting parameter relationships beyond the reach of empirical models, with physical constraints ensuring that predictions remain consistent with known ageing mechanisms. Such integration enables the learning process to adapt to operational variability and coupled stress effects, thereby improving both the accuracy and physical interpretability of lifetime estimation. The review aims to provide a concise view of models, tests, and workflows that convert thermal-ageing knowledge into robust, design-time decisions. By linking empirical and physics-based insights with modern data-driven learning, these developments support proactive maintenance, sustainable asset management, and extended operational lifetimes for next-generation EMs. Full article

Foundation engineering is an essential preliminary stage in high-rise building construction, as it provides the structural load-bearing capacity of the building. Since foundation structures often extend into the subsurface layers, excavation becomes a critical part of construction. When groundwater is encountered during excavation, it is necessary to lower the groundwater level to provide a dry working environment. However, groundwater is a valuable and clean natural resource. In most high-rise construction projects, large volumes of groundwater are extracted through dewatering operations to maintain dry foundation conditions; therefore, minimizing groundwater extraction is particularly important for conserving this precious resource. In Taiwan, groundwater level monitoring at high-rise construction sites has traditionally relied on manually measuring observation wells using graduated rulers and labor-intensive shift schedules. A few construction companies have adopted continuous groundwater monitoring systems, but these require substantial financial investment and maintenance costs. To address these limitations, this study proposes an artificial intelligence (AI)-based groundwater level simulation model. In particular, artificial neural networks (ANNs) are integrated with fuzzy logic theory to develop a predictive model for dewatering operations in high-rise building foundations. Furthermore, a smart water-saving dewatering model is proposed to overcome the deficiencies of conventional dewatering practices, which typically consume excessive groundwater resources. Full article

The global drive toward sustainability and energy efficiency has accelerated the development of smart buildings integrating the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies optimise energy use, enhance occupant comfort, and advance building management systems. This study examines the integration of IoT and AI in energy-efficient smart buildings, emphasising applications and challenges. A qualitative methodology, combining systematic literature review, case study analysis, and systems analysis, underpins the research. Findings indicate that IoT enables smart metering, real-time energy monitoring, automated lighting and HVAC, occupancy-based energy optimisation, and renewable energy integration. AI complements these functions through predictive maintenance, energy forecasting, demand-side management, intelligent climate control, indoor air quality automation, and behaviour-driven analytics. Together, they reduce carbon emissions, lower operational costs, and improve occupant well-being. However, challenges remain, including data security and privacy risks, interoperability gaps, scalability and cost constraints, and retrofitting difficulties. To address these, the paper proposes a systems thinking-enabled conceptual framework structured around three pillars: adopting IoT and AI as enabling technologies, overcoming integration barriers, and identifying application areas that advance sustainability in smart buildings. This framework supports strategic decision-making toward net-zero and resilient building design. Full article

Ensuring operational continuity in modern industrial systems requires maintenance strategies that are both mature and adaptive to uncertainty. This study introduces and validates the Integrated Maintenance Maturity Model (IMMM), a multidimensional framework that combines reliability, safety, resilience, flexibility, and sustainability into a structured maturity assessment approach. Building on the conceptual foundations of maintenance maturity modeling, the IMMM is enhanced with fuzzy logic to address uncertainty, incorporate expert knowledge, and enable nuanced evaluations. A fuzzy inference system based on Mamdani logic was developed to integrate linguistic variables, apply rule-based reasoning, and defuzzify results into maturity scores. The model also includes additional parameters, such as technology adaptability and resource efficiency, to reflect real-world operational complexity. The applicability of the proposed framework was demonstrated through a case study in the automotive sector, where the fuzzy IMMM identified maturity gaps, supported decision-making, and provided strategic recommendations for advancing maintenance practices. Results confirm the model’s effectiveness in enhancing system dependability, adaptability, and sustainability under uncertainty. This work contributes to the development of predictive, uncertainty-aware maintenance maturity models and offers a practical tool for organizations seeking to strengthen operational resilience while aligning with long-term sustainability goals. Full article

This study addresses the persistence of avoidable failures and efficiency losses in HVAC plants by introducing a field-validated predictive maintenance (PdM) framework that estimates component-level RUL from multiyear BMS telemetry and translates forecasts into schedule-aware maintenance actions. The objective was to determine whether an LSTM ensemble with mode-aware segmentation and isotonic calibration could yield decision-quality RUL forecasts that reduce unplanned outages, downtime, and electricity use in a large Riyadh office building. Two years of 1 min BMS data from chillers, primary pumps, and AHU fans were cleaned, standardized, and segmented by operating mode; RUL labels were derived from time-stamped work orders and failure confirmations; the LSTM produced per-minute RUL estimates trained with a Huber loss, calibrated to lower quantiles, and converted to sustained triggers compared against a fixed-interval program. On the held-out test set, the model achieved a weighted MAE of 19.8 ± 2.1 h and RMSE of 29.1 ± 3.3 h, with quantile calibration error (QCE) $\le 0.06$ and lead-time accuracy (LTA; fraction of triggers whose calibrated lower-quantile RUL is $\ge$ the planning threshold) of 0.79 at a 10-day threshold. When deployed in counterfactual evaluation, triggers reduced unplanned outages by 47.6% (paired bootstrap p = 0.008) and total downtime by 41.3% (p = 0.012), and yielded a 10.6% reduction in HVAC electricity (95% CI: 7.7–13.2%) and a 9.7% decrease in total operating cost. The findings indicate that calibrated sequence models coupled to simple sustained triggers can convert routine BMS data into reliable maintenance schedules with quantifiable reliability and energy benefits. Practically, conservative calibration (q approximately 0.25) with thresholds of 10–12 days provided stable lead windows; future work should assess transferability across climates and facility types using transfer learning and integrate uncertainty-aware triggering with MPC for joint operational and maintenance optimization. Full article