Sertac Oruc

The determination of the awareness date for notice is important for claim submission in construction contracts. This research examines the notice mechanisms under the FIDIC Red Book 1999 and 2017 and compares them with the Public Procurement Contracts Law (KİSK), focusing specifically on the interpretation of the phrase “became aware or should have become aware.” Under FIDIC contracts, the Contractor may be required to act not only on actual knowledge but also on knowledge it should reasonably have acquired of the relevant event or circumstance. The Turkish public procurement framework does not contain a general awareness-based time-bar mechanism comparable to FIDIC. However, specific notice requirements exist, notably under Article 29 of the General Conditions of Contract for Construction Works (“YİGŞ”) for extension-of-time requests, subject to exceptions such as delays caused by the administration. Article 473 of the Turkish Code of Obligations uses the expression ‘according to all estimations’ (‘bütün tahminlere göre’) when allowing the employer to withdraw before the delivery date if it clearly becomes evident that the work will not be completed on time; it does not create an awareness-based notice trigger. The comparative analysis demonstrates that this difference between FIDIC and KİSK leads to divergent approaches in determining the correct notice date, claim submissions, and dispute resolution. The research aims to clarify how awareness should be assessed in practice and to evaluate whether the absence of such a concept in KİSK creates legal uncertainty when compared with the FIDIC framework.

Climate change challenges the stationarity assumptions embedded in construction baseline schedules by shifting temperature and precipitation regimes and, consequently, weather-driven work interruptions. This study presents a gridded, data-driven workflow that links climate forcing to construction schedule outcomes by translating daily temperature/precipitation thresholds into Activity Performance Indices (APIs) for climate-sensitive activities and propagating them into project duration through start-date scanning. Historical baselines are represented using ERA5-Land reanalysis at 0.1° (~9 km) resolution, while future conditions are derived from a bias-corrected and downscaled CMIP6 multi-model ensemble under Shared Socioeconomic Pathways (with emphasis on SSP5-8.5 for end-of-century risk). Single- and multi-hazard operability surfaces are mapped across Türkiye and demonstrated using a 46-activity reinforced-concrete building schedule with a 207-day deterministic baseline. Results show strong seasonal redistribution of workability: warming tends to improve winter operability while degrading summer productivity, producing location- and start-date-dependent optimal windows. Nationally, climate effects increase the schedule mean/median to 255/238 days under historical conditions (+23% mean vs. baseline). Under far-future SSP5-8.5 (2071–2100), optimized outcomes converge to 239 days (+16% vs. baseline). If historical planning start dates are carried forward without climate adaptation, mean/median durations rise to 258/244 days, implying ~19 days (~10% of the baseline) of avoidable delay on average, with substantially larger penalties in some regions. The outputs support climate-aligned planning and provide an auditable basis for contract-ready baselines (e.g., via a Climate–Weather Baseline Report approach).

Climate change is shifting building thermal demand from heating toward cooling, yet quantitative, design-relevant projections for semi-arid continental cities remain scarce. This study projects heating and cooling degree days (HDDs and CDDs) for Ankara, Türkiye, using ERA5-Land as the reference, and a quantile delta mapping (QDM) bias-corrected CMIP6 ensemble under SSP2-4.5 and SSP5-8.5, across eight base temperatures (HDD 15–18 °C; CDD 23–26 °C) to 2100. By the far future, HDD18 decreases by 23–37% whereas CDD24 increases by +205 to +498 °C·day/year (with wide inter-model spread), and the heating degree days factor declines from 0.984 to 0.765, marking a shift toward cooling within the degree-day budget; the cooling season extends from 25 to as many as 106 days. In a deterministic −1 to +2 °C local-temperature-offset test, the direction of change persists, and the cooling design dry-bulb temperature rises by up to +7.3 °C, raising peak cooling loads. Independent 2020–2022 station observations support the HDD projections (CDD projections were not independently validated). Because a single grid cell and station cannot resolve urban canyon or local climate zone contrasts, the conclusions apply to the campus vicinity. Overall, the study translates the climate signal into HVAC-sizing and retrofit-relevant metrics for a warming semi-arid capital.

Standardized drought indices such as the Standardized Precipitation Index (SPI) are widely used in both monitoring and climate-change impact assessments. However, SPI values are not uniquely defined unless the reference climatology used for standardization is explicitly stated and justified−a methodological issue that becomes critical under non-stationary climate conditions. Here, we present a methodological assessment of how reference-climatology strategy affects SPI-based drought projections under climate change, using Türkiye’s 26 major basins as a hydroclimatically diverse testbed. These assessments inform sustainable water resources planning, agricultural adaptation, and climate-resilient infrastructure design under non-stationary climate. Daily precipitation projections from 56 GCM-RCM pairs (EURO-CORDEX EUR-11, 0.11° (approximately 12 km at the mid-latitudes of the study domain); CMIP5 RCP8.5) were bias-corrected against ERA5-Land and aggregated to basin means. We computed SPI-9 and compared two commonly used reference strategies: (i) a fixed historical baseline (1970–2005), applied consistently to both historical and future periods (fixed-baseline SPI); and (ii) a period-specific baseline (period-specific SPI; future SPI values are standardized to the climatology of the future evaluation period itself). Using the same climate simulations, the two strategies yield markedly different drought projections. At the country scale, end-of-century drought time reaches 458 months under the fixed-baseline strategy, whereas the period-specific strategy indicates 393 drought months. Corresponding severity summaries are likewise stronger under fixed-baseline standardization. The contrast is even stronger in several Mediterranean basins, where fixed-baseline standardization produces persistently severe drought conditions. These results show that SPI-based drought projections are substantially sensitive to the choice of reference-climatology strategy, and that the same climate ensemble can support materially different drought narratives depending on how anomalies are standardized. Because the two strategies differ in both reference-timing and calibration-window length (36 versus 95 years), the headline contrast should be interpreted as a combined effect rather than as a pure baseline-timing result. In the present implementation, the period-specific strategy uses a single future calibration period (2006–2100), so the comparison should be interpreted as a stress test of reference framing under non-stationary climate rather than as an equal-length baseline experiment. An equal-length late-baseline sensitivity check (1970–2005 versus 2065–2100; both spanning 36 years) shows that the fixed-to-late-baseline contrast is larger than the fixed-to-period-specific contrast in 25 of 27 spatial units, including a 3.0-fold amplification at the national scale, indicating that the reference-timing effect persists when calibration-window length is held constant. Because the analysis is based on a CMIP5-driven RCP8.5 ensemble, the numerical projections should be interpreted as a high-end stress-test envelope rather than as the most likely outcome. We therefore recommend that drought projection studies explicitly report the reference-climatology strategy, justify the calibration window, and distinguish between analyses designed to quantify change relative to a historical climate and analyses designed to describe anomalies relative to an evolving future climate. These methodological choices have direct implications for sustainable water resources management and drought-risk preparedness in water-stressed Mediterranean systems, and contribute to broader sustainability targets such as Sustainable Development Goal 6 (Clean Water and Sanitation), SDG 13 (Climate Action), and SDG 15 (Life on Land).

Droughts are among the most damaging natural hazards, exerting severe impacts on ecosystems, economies, and societies. While typically catastrophic, their effects can vary across regions, including polar areas where outcomes may differ unexpectedly. Accurate forecasting is therefore essential for climate adaptation and resource planning. This study analyzes monthly precipitation data from 1970 to 2025 across four Norwegian cities (Bergen, Kristiansand, Oslo, and Tromsø). Standardized Precipitation Index (SPI12) values were calculated and used as inputs to predictive models based on Convolutional Neural Networks (CNN). To enhance performance, the CNN framework was combined with Random Forest (RF) and also optimization techniques, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), as well as signal decomposition methods such as Variational Mode Decomposition (VMD) and the Tunable Q-Factor Wavelet Transform (TQWT). Four input configurations were tested, and results show that CNN–TQWT hybrid models consistently outperformed other approaches across all study sites, highlighting their potential for reliable drought prediction in diverse climatic settings. Specifically, the most effective models were identified as: Bergen: CNNTQWTM03 (R = 0.9732, NSE = 0.9231) Kristiansand: CNNTQWTM03 (R = 0.9879, NSE = 0.9568) Oslo: CNNTQWTM02 (R = 0.9727, NSE = 0.9172) Tromsø: CNNTQWTM01 (R = 0.9777, NSE = 0.9395). The findings highlight the superior accuracy of the TQWT decomposition method across all stations, underscoring its potential to support decision-makers in developing effective agricultural and water management policies. Furthermore, the findings of this study will contribute to the formulation of drought preparedness and development plans. By providing a technical foundation for proactive planning, these results will assist in mitigating the impacts of drought through more resilient management strategies.

Machine-learning techniques are widely used across many disciplines, including electricity generation forecasting. In this study, the Support Vector Machine (SVM) based models, one of the machine learning techniques, were developed for daily PV power forecasting. To improve model performance, models were tuned with four metaheuristic optimizers, including the Artificial Bee Colony (ABC), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Daily PV power and temperature data from 2020 to 2023 were obtained for the Stavanger, Oslo, and Kristiansand regions which located in southern Norway. One of the innovative aspects of this study is the investigation of the performance of SVM (Support Vector Machine) combined with various optimization methods across four alternative input configurations. To examine the different feature combinations, four different input configurations were created through the Minimum-Redundancy Maximum-Relevancy (MRMR) method. The analysis results obtained with SVM were further enhanced using all optimization techniques. Among those, the SVM-PSO-M04 ( r = 0.7707, NSE = 0.5748, KGE = 0.7092, PI = 0.2964 and RMSE = 0.6513) method produced the most effective results (improving the correlation coefficient (r) to 0.7707 (approximately a 19% increase over the untuned SVM)) among the tested hybrid configurations obtained in our experiments. Moreover, coupling temperature data alongside PV power as model input also tends to improve forecasting skill. Results of this study provide a case-study benchmark for researchers, institutions, and other stakeholders engaged in renewable energy planning and management in high-latitude regions.

Drought is a natural disaster that often remains unnoticed until ecosystem impacts become severe. Therefore, monitoring and detecting droughts are important research topics. Consequently, drought indices with different focuses, such as precipitation or soil moisture, have been developed. Yet, the utility of the indices is limited before the beginning of the drought. To overcome this shortcoming, drought forecasting and providing decision-makers with an early warning to mitigate the effects is an important research topic. This study aims to take on the forecasting of the droughts with its novelty on the spatial focus, Norway (Drammen, Hamar, and Lillehammer). We forecast the Effective Drought Index (EDI) across spatially diverse Norwegian regions without hydrological constraints. To achieve this, we have utilized precipitation data between 1980 and 2025 and trained our machine learning models, namely, Support Vector Machine (SVM), Multi-layer Perceptron (MLP), Extreme Gradient Boosting (XGboost), Long-Short Term Memory network (LSTM), and Categorical Boosting Algorithm (Catboost). Moreover, the latent feature space is extended by wavelet transformation (WT). The innovative aspect of this study and its contribution to the literature is its novel application of the WT to some algorithms. Furthermore, unlike the literature, EDI was chosen as the drought index in this study, further increasing its innovative nature. Our results indicate that long short-term memory networks enhanced by wavelet transformation provide the best forecasts. Here, the best performance, LSTMW-M04, is achieved over Drammen ( r = 0.9765, NSE = 0.9510, KGE = 0.8641, PI = 0.3211, and RMSE = 0.2207). Although LSTM is already an innovative and successful algorithm, we have further improved the model performance. This result will help decision-makers in a future drought study with both the model input structure and the algorithm used.

In this study, we benchmark various machine learning techniques against a synthetic but physically based reference time series (model-simulated (ERA5-Land/FLake) bottom-temperature series) and assess whether decomposition methods (VMD and EMD) improve forecast accuracy using Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Random Forest (RF), Gaussian Process Regression (GPR), and Long Short-Term Memory (LSTM) with the monthly average data of Mjøsa, the largest lake in Norway, between 1950 and 2024 from the ERA5-Land FLake model. A total of 70% of the dataset was used for training and 30% was reserved for testing. To assess the performance several metrics, correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), Performance Index (PI), RMSE-based RSR, and Root Mean Square Error (RMSE) were used. The results revealed that without decomposition, the GPR-M03 combination outperforms other models (with scores r = 0.9662, NSE = 0.9186, KGE = 0.8786, PI = 0.0231, RSR = 0.2848, and RMSE = 0.2000). Considering decomposition cases, when VMD is applied, the SVM-VMD-M03 combination achieved better results compared to other models (with scores r = 0.9859, NSE = 0.9717, KGE = 0.9755, PI = 0.0135, RSR = 0.1679, and RMSE = 0.1179). Conversely, with decomposition cases, when EMD applied, LSTM-EMD-M03 is explored as the more effective combination than others (with scores r = 0.9562, NSE = 0.9008, KGE = 0.9315, PI = 0.0256, RSR = 0.2978, and RMSE = 0.3143). The results demonstrate that GPR and SVM, coupled with VMD, yield high correlation (e.g., r ≈ 0.986) and low RMSE (~0.12), indicating the ability to reproduce FLake dynamics rather than as accurate predictions of measured bottom temperature.

Environmental factors, such as low and high temperatures, significantly impact the strength, durability, and fresh properties of conventional concrete. These conditions not only affect the strength and durability characteristics of concrete but also its transportation, placement, and curing conditions. Concrete, when properly designed, cured, and placed, can be a highly durable construction material. However, certain physical and chemical influences can hinder concrete from achieving its expected strength and durability. This study examines how climate parameters, specifically maximum temperature, minimum temperature, and precipitation, and their threshold values, will affect concrete pouring activities in Kırıkkale, Turkey, in the future. The findings suggest that as air temperatures rise, concrete pouring performance is expected to increase during winter months, when pouring was previously difficult or impossible. However, in summer months, rising maximum temperatures could lead to decreased performance in concrete pouring activities unless appropriate measures are taken.