Hydration Heat Kinetics and Temperature Control in Ultra-Large Volume Concrete: A Case Study

In recent years, with the ongoing expansion of infrastructure construction, ultra-large volume concrete structures have been widely applied in major projects such as hydraulic engineering, transportation, energy, and high-rise buildings [1-3]. These structures, characterized by their large volumes and thick cross-sections, generate significant hydration heat during construction, causing rapid internal temperature increases. Due to concrete’s relatively low thermal conductivity, heat within the structure is slow to dissipate, leading to large temperature gradients and thermal stresses, which can result in cracking and compromise the overall safety and integrity of the structure [4, 5]. Consequently, the dynamic analysis and effective control of hydration heat in ultra-large volume concrete have become critical technical challenges in the field of civil engineering.

The hardening process of concrete involves hydration reactions between cement and water, with the associated exothermic heat being the primary driver of temperature rise in large-volume concrete structures. The kinetics of hydration reactions-including reaction rates and cumulative heat release-are critically dependent on cement composition, mineral admixture ratios, and environmental temperature. Simultaneously, the evolution of internal temperature fields is governed by coupled heat transfer mechanisms encompassing thermal conduction, phase-change latent heat effects, and boundary heat dissipation conditions [6-8]. A systematic investigation of hydration thermodynamics in large-volume concrete is therefore essential for elucidating temperature evolution mechanisms and advancing intelligent thermal regulation technologies. While current engineering practices employ temperature control strategies such as embedded cooling pipes, layered casting techniques, and composite insulation systems [9, 10], significant challenges persist in adapting these methods to extreme thermal environments characterized by diverse geological conditions, climatic variations, and structural configurations.

Recent advancements in large-volume concrete hydration thermodynamics can be categorized into three key research domains: (1) Hydration heat generation mechanisms: Calorimetric studies have established modified Arrhenius equations and hyperbolic adiabatic temperature-rise models to quantify exothermic behavior in cement-admixture systems [11-14]. Experimental evidence demonstrates that 20% cement replacement with volcanic ash powder reduces peak exothermic output by 28% [15], while wood waste bottom ash incorporation induces distinct modifications to hydration heat release profiles [16]. Nevertheless, synergistic interactions in multi-component cementitious systems remain insufficiently characterized. (2) Temperature field prediction: Finite element-based thermo-mechanical coupling models reveal that incremental casting speed increases of 0.5 m/h elevate core temperature rise by 6-8℃ [17-20]. However, reconciling cost constraints with control efficiency remains a persistent technical challenge.

Therefore, using the Jinan Yellow River Fenghuang Bridge in China as a case study, analyzes the hydration heat dynamics of ultra-large volume concrete and the corresponding temperature control measures. It reveals the distribution patterns of temperature and stress in the joint sections and foundation slabs under different seasonal conditions and proposes cost-effective temperature control and crack prevention technologies. The findings provide both theoretical insights and practical guidance for optimizing temperature control during the construction of ultra-large volume concrete structures.

3.1 Theoretical analysis

The adiabatic temperature rise numerical model for the concrete of the bridge tower connection section and foundation slab is based on the following hyperbolic function:

$Q(\tau )={{Q}_{0}}(1-{{e}^{-\alpha {{\tau }^{\beta }}}})$                                (1)

where, τ represents the age of the concrete when shrinkage begins, Q₀ is the final adiabatic temperature rise, and; α, β are the coefficients that describe the variation in the adiabatic temperature rise.

As time progresses, the elastic modulus is modeled using a four-parameter double-exponential function, i.e.,

$E(\tau )={{E}_{0}}+\Delta E(1-{{e}^{-\alpha {{\tau }^{\beta }}}})$                                (2)

where, E₀ represents the initial elastic modulus; ∆E is the difference between the final elastic modulus E₂ and the initial elastic modulus, which is equal to E₂-E₀; and α, β denotes two parameters related to the rate of change in the elastic modulus.

Furthermore, the concrete creep factor is taken as

$\begin{align}  & C(t,\tau )={{C}_{1}}(1+9.20{{\tau }^{-0.45}})(1-{{e}^{-0.30(t-\tau )}}) \\ & +{{C}_{2}}(1+1.70{{\tau }^{-0.45}})(1-{{e}^{-0.005(t-\tau )}}) \\\end{align}$                                (3)

In the equation, C₁=0.23/E₂, C₂=0.52/E₂.

The equivalent heat release coefficient β_s for heat dissipation from the concrete surface to the surrounding medium through the insulation layer is calculated using the following formula:

${{\beta }_{s}}=\frac{1}{(1/\beta )+\sum{\left( {}^{{{h}_{i}}}/{}_{{{\lambda }_{i}}} \right)}}$                                (4)

where, β is the heat release coefficient, h_i is the thickness of the insulation layer in the i-th layer, and λ_i is the thermal conductivity of the insulation material in the i-th layer.

A 5mm steel plate insulation is applied to the side of the tower connection section. The equivalent heat release coefficient for the steel plate is denoted as 75.9KJ/m·h·℃, which is calculated using Eq. (4).

The thickness of the insulation layer is analyzed and calculated based on Eq. (5).

$\delta =\frac{0.5h({{T}_{S}}-{{T}_{q}}){{\lambda }_{i}}}{{{\lambda }_{0}}({{T}_{\max }}-{{T}_{b}})}\cdot {{K}_{b}}$                                 (5)

In the equation, $\lambda_0$ represents the thickness of the concrete surface insulation layer, m; $\lambda_i$ is the thermal conductivity of concrete, W/(m·K), set at 2.33; $T_s$ is the thermal conductivity of the insulation material, W/(m·K), with geotextile selected for this project, taken as 0.06; $T_s$ is the surface temperature of the concrete pouring body, ℃; $T_q$ is the average atmospheric temperature when the concrete reaches its peak temperature 3 to 5 days after pouring, ℃; $T_s-T_q$ ranges from 15℃ to 20℃, with a value of 15℃; $T_{max }$ is the maximum temperature inside the concrete pouring body, ℃; $T_b$ is the surface temperature of the concrete, ℃; h is the actual thickness of the concrete structure, taken as 6.22 m; $T_{max }-T_b$ ranges from 20℃ to 25℃, with a value of 25℃; $K_b$ is the heat transfer coefficient correction factor, set at 1.3.

Therefore, $\delta$= 0.5´6.22´15´0.06´2/(2.33´25)=0.0625m. Thus, the thickness of the geotextile insulation should be greater than 62.5 mm.

3.2 Estimation of the pouring temperature

The concrete mix ratio for the bridge tower connection section (C60) is as follows: cement: fly ash: crushed stone: sand: water: admixture is equal to 425: 75: 1070: 713: 140: 6.5. For the foundation slab (C35), the mix ratio is: cement: fly ash: crushed stone: sand: water: admixture is equal to 300: 100: 1035: 811: 152: 4.4. Using local meteorological data and empirical temperature data for the raw materials, the concrete pouring temperatures for different months are estimated based on the reference mix ratios for the bridge tower connection section and foundation slab.

The construction period for the bridge tower connection section runs from August to October, while the foundation slab construction may extend across different months throughout the year. As a result, the year is divided into three periods to estimate the pouring temperatures. To control the temperature of raw materials, measures such as regulating cement temperature upon delivery and shading coarse aggregates will be implemented. Specifically, the cement temperature will be kept below 60℃, and the fly ash temperature will be maintained below 40℃. Table 1 presents the estimated pouring temperatures for both the bridge tower connection section and the foundation slab across different months.

According to the construction schedule, the pouring period for the bridge tower connection section is from August to September. Based on the local temperature conditions, the average temperature during this period ranges from 23.5℃ to 27.5℃, resulting in an estimated pouring temperature for the C60 concrete of between 30.9℃ and 34.5℃. To ensure that the pouring temperature of the tower foundation and solid sections does not exceed 28℃, measures such as mixing crushed ice and using chilled water will be implemented. From December to February, which is the cold season in Jinan, the average temperature ranges from 1.2℃ to 4.3℃, with the foundation slab pouring temperature ranging from 11.7℃ to 13.4℃. During March, April, October, and November, Jinan experiences moderate temperatures, with average temperatures ranging from 9.8℃ to 18.6℃. The pouring temperature of the foundation slab is estimated to range from 17.8℃ to 26.7℃. In May and September, the high-temperature season in Jinan, the average temperature ranges from 23.4℃ to 28.9℃, and the foundation slab pouring temperature ranges from 30.8℃ to 35.3℃. To control the foundation slab pouring temperature and keep it below 28℃, measures such as mixing crushed ice and using chilled water will be implemented.

5.1 Intelligent temperature control system

The concrete intelligent monitoring system proposed in this paper is an integrated intelligent temperature control solution, developed by combining a wireless monitoring module, an intelligent pipe cooling module, and an intelligent curing module. The system incorporates capabilities for data analysis, information push and early warnings, automatic control, and remote monitoring, all designed to optimize concrete temperature control and significantly reduce the risk of cracking. Key features of the system include wireless data collection, a data processing platform, online platform notifications and alerts, and intelligent control mechanism.

The system provides scientific references for parameter adjustment and measure formulation by comparing and analyzing construction methods and temperature control data for similar structures in the same region, in conjunction with optimization solutions derived from finite element simulations. Additionally, by utilizing wireless automatic monitoring equipment, the system enables real-time data monitoring and automatic early warnings. Users can access data in real-time through WeChat and PC clients, and receive alerts for key parameters, ensuring full control over on-site temperature conditions. Furthermore, the system platform supports identity authentication and hierarchical management, delivering personalized data based on user needs and enabling a closed feedback loop through online responses, which enhances issue resolution speed. Lastly, the intelligent temperature control system integrates proprietary pipe cooling circulation and curing modules, offering high-precision flow control. It allows for real-time adjustments based on platform data, overcoming the delays and inaccuracies typically associated with manual interventions.

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(a) Bridge tower connection section low-temp season

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(b) Foundation slab low-temperature season

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(c) Foundation slab normal-temperature season

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(d) Foundation slab high-temperature season

Figure 6. Comparison of experimental and numerical simulation results

5.2 Comparison of temperature control effects

Temperature and stress sensors are installed in the bridge tower concrete connection section and foundation slab, with the cooling pipe temperature regulated through the concrete intelligent monitoring system. Figure 6 presents a comparison of the experimental and numerical simulation results. By analyzing the experimental and simulation data, the accuracy and applicability of the numerical model can be clearly evaluated. In this study, the experimental results and simulation data exhibit consistent trends in key indicators, indicating that the numerical model accurately reflects the dynamic behavior of concrete hydration heat. Under varying temperature conditions, the maximum discrepancy between the experimental and simulation results is 2%, which may be attributed to boundary condition settings and mesh refinement. Overall, the experimental data confirm the reliability of the numerical simulation, providing strong support for the widespread application of the concrete intelligent monitoring system.